init commit

ultralytics/models/__init__.py

@@ -0,0 +1,9 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from .fastsam import FastSAM
+from .nas import NAS
+from .rtdetr import RTDETR
+from .sam import SAM
+from .yolo import YOLO, YOLOE, YOLOWorld
+
+__all__ = "YOLO", "RTDETR", "SAM", "FastSAM", "NAS", "YOLOWorld", "YOLOE"  # allow simpler import

ultralytics/models/fastsam/__init__.py

@@ -0,0 +1,7 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from .model import FastSAM
+from .predict import FastSAMPredictor
+from .val import FastSAMValidator
+
+__all__ = "FastSAMPredictor", "FastSAM", "FastSAMValidator"

ultralytics/models/fastsam/model.py

@@ -0,0 +1,81 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+from ultralytics.engine.model import Model
+
+from .predict import FastSAMPredictor
+from .val import FastSAMValidator
+
+
+class FastSAM(Model):
+    """
+    FastSAM model interface for segment anything tasks.
+
+    This class extends the base Model class to provide specific functionality for the FastSAM (Fast Segment Anything
+    Model) implementation, allowing for efficient and accurate image segmentation with optional prompting support.
+
+    Attributes:
+        model (str): Path to the pre-trained FastSAM model file.
+        task (str): The task type, set to "segment" for FastSAM models.
+
+    Methods:
+        predict: Perform segmentation prediction on image or video source with optional prompts.
+        task_map: Returns mapping of segment task to predictor and validator classes.
+
+    Examples:
+        Initialize FastSAM model and run prediction
+        >>> from ultralytics import FastSAM
+        >>> model = FastSAM("FastSAM-x.pt")
+        >>> results = model.predict("ultralytics/assets/bus.jpg")
+
+        Run prediction with bounding box prompts
+        >>> results = model.predict("image.jpg", bboxes=[[100, 100, 200, 200]])
+    """
+
+    def __init__(self, model: str = "FastSAM-x.pt"):
+        """Initialize the FastSAM model with the specified pre-trained weights."""
+        if str(model) == "FastSAM.pt":
+            model = "FastSAM-x.pt"
+        assert Path(model).suffix not in {".yaml", ".yml"}, "FastSAM models only support pre-trained models."
+        super().__init__(model=model, task="segment")
+
+    def predict(
+        self,
+        source,
+        stream: bool = False,
+        bboxes: list | None = None,
+        points: list | None = None,
+        labels: list | None = None,
+        texts: list | None = None,
+        **kwargs: Any,
+    ):
+        """
+        Perform segmentation prediction on image or video source.
+
+        Supports prompted segmentation with bounding boxes, points, labels, and texts. The method packages these
+        prompts and passes them to the parent class predict method for processing.
+
+        Args:
+            source (str | PIL.Image | np.ndarray): Input source for prediction, can be a file path, URL, PIL image,
+                or numpy array.
+            stream (bool): Whether to enable real-time streaming mode for video inputs.
+            bboxes (list, optional): Bounding box coordinates for prompted segmentation in format [[x1, y1, x2, y2]].
+            points (list, optional): Point coordinates for prompted segmentation in format [[x, y]].
+            labels (list, optional): Class labels for prompted segmentation.
+            texts (list, optional): Text prompts for segmentation guidance.
+            **kwargs (Any): Additional keyword arguments passed to the predictor.
+
+        Returns:
+            (list): List of Results objects containing the prediction results.
+        """
+        prompts = dict(bboxes=bboxes, points=points, labels=labels, texts=texts)
+        return super().predict(source, stream, prompts=prompts, **kwargs)
+
+    @property
+    def task_map(self) -> dict[str, dict[str, Any]]:
+        """Returns a dictionary mapping segment task to corresponding predictor and validator classes."""
+        return {"segment": {"predictor": FastSAMPredictor, "validator": FastSAMValidator}}

ultralytics/models/fastsam/predict.py

@@ -0,0 +1,181 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import torch
+from PIL import Image
+
+from ultralytics.models.yolo.segment import SegmentationPredictor
+from ultralytics.utils import DEFAULT_CFG, checks
+from ultralytics.utils.metrics import box_iou
+from ultralytics.utils.ops import scale_masks
+from ultralytics.utils.torch_utils import TORCH_1_10
+
+from .utils import adjust_bboxes_to_image_border
+
+
+class FastSAMPredictor(SegmentationPredictor):
+    """
+    FastSAMPredictor is specialized for fast SAM (Segment Anything Model) segmentation prediction tasks.
+
+    This class extends the SegmentationPredictor, customizing the prediction pipeline specifically for fast SAM. It
+    adjusts post-processing steps to incorporate mask prediction and non-maximum suppression while optimizing for
+    single-class segmentation.
+
+    Attributes:
+        prompts (dict): Dictionary containing prompt information for segmentation (bboxes, points, labels, texts).
+        device (torch.device): Device on which model and tensors are processed.
+        clip_model (Any, optional): CLIP model for text-based prompting, loaded on demand.
+        clip_preprocess (Any, optional): CLIP preprocessing function for images, loaded on demand.
+
+    Methods:
+        postprocess: Apply postprocessing to FastSAM predictions and handle prompts.
+        prompt: Perform image segmentation inference based on various prompt types.
+        set_prompts: Set prompts to be used during inference.
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """
+        Initialize the FastSAMPredictor with configuration and callbacks.
+
+        This initializes a predictor specialized for Fast SAM (Segment Anything Model) segmentation tasks. The predictor
+        extends SegmentationPredictor with custom post-processing for mask prediction and non-maximum suppression
+        optimized for single-class segmentation.
+
+        Args:
+            cfg (dict): Configuration for the predictor.
+            overrides (dict, optional): Configuration overrides.
+            _callbacks (list, optional): List of callback functions.
+        """
+        super().__init__(cfg, overrides, _callbacks)
+        self.prompts = {}
+
+    def postprocess(self, preds, img, orig_imgs):
+        """
+        Apply postprocessing to FastSAM predictions and handle prompts.
+
+        Args:
+            preds (list[torch.Tensor]): Raw predictions from the model.
+            img (torch.Tensor): Input image tensor that was fed to the model.
+            orig_imgs (list[np.ndarray]): Original images before preprocessing.
+
+        Returns:
+            (list[Results]): Processed results with prompts applied.
+        """
+        bboxes = self.prompts.pop("bboxes", None)
+        points = self.prompts.pop("points", None)
+        labels = self.prompts.pop("labels", None)
+        texts = self.prompts.pop("texts", None)
+        results = super().postprocess(preds, img, orig_imgs)
+        for result in results:
+            full_box = torch.tensor(
+                [0, 0, result.orig_shape[1], result.orig_shape[0]], device=preds[0].device, dtype=torch.float32
+            )
+            boxes = adjust_bboxes_to_image_border(result.boxes.xyxy, result.orig_shape)
+            idx = torch.nonzero(box_iou(full_box[None], boxes) > 0.9).flatten()
+            if idx.numel() != 0:
+                result.boxes.xyxy[idx] = full_box
+
+        return self.prompt(results, bboxes=bboxes, points=points, labels=labels, texts=texts)
+
+    def prompt(self, results, bboxes=None, points=None, labels=None, texts=None):
+        """
+        Perform image segmentation inference based on cues like bounding boxes, points, and text prompts.
+
+        Args:
+            results (Results | list[Results]): Original inference results from FastSAM models without any prompts.
+            bboxes (np.ndarray | list, optional): Bounding boxes with shape (N, 4), in XYXY format.
+            points (np.ndarray | list, optional): Points indicating object locations with shape (N, 2), in pixels.
+            labels (np.ndarray | list, optional): Labels for point prompts, shape (N, ). 1 = foreground, 0 = background.
+            texts (str | list[str], optional): Textual prompts, a list containing string objects.
+
+        Returns:
+            (list[Results]): Output results filtered and determined by the provided prompts.
+        """
+        if bboxes is None and points is None and texts is None:
+            return results
+        prompt_results = []
+        if not isinstance(results, list):
+            results = [results]
+        for result in results:
+            if len(result) == 0:
+                prompt_results.append(result)
+                continue
+            masks = result.masks.data
+            if masks.shape[1:] != result.orig_shape:
+                masks = scale_masks(masks[None], result.orig_shape)[0]
+            # bboxes prompt
+            idx = torch.zeros(len(result), dtype=torch.bool, device=self.device)
+            if bboxes is not None:
+                bboxes = torch.as_tensor(bboxes, dtype=torch.int32, device=self.device)
+                bboxes = bboxes[None] if bboxes.ndim == 1 else bboxes
+                bbox_areas = (bboxes[:, 3] - bboxes[:, 1]) * (bboxes[:, 2] - bboxes[:, 0])
+                mask_areas = torch.stack([masks[:, b[1] : b[3], b[0] : b[2]].sum(dim=(1, 2)) for b in bboxes])
+                full_mask_areas = torch.sum(masks, dim=(1, 2))
+
+                union = bbox_areas[:, None] + full_mask_areas - mask_areas
+                idx[torch.argmax(mask_areas / union, dim=1)] = True
+            if points is not None:
+                points = torch.as_tensor(points, dtype=torch.int32, device=self.device)
+                points = points[None] if points.ndim == 1 else points
+                if labels is None:
+                    labels = torch.ones(points.shape[0])
+                labels = torch.as_tensor(labels, dtype=torch.int32, device=self.device)
+                assert len(labels) == len(points), (
+                    f"Expected `labels` with same size as `point`, but got {len(labels)} and {len(points)}"
+                )
+                point_idx = (
+                    torch.ones(len(result), dtype=torch.bool, device=self.device)
+                    if labels.sum() == 0  # all negative points
+                    else torch.zeros(len(result), dtype=torch.bool, device=self.device)
+                )
+                for point, label in zip(points, labels):
+                    point_idx[torch.nonzero(masks[:, point[1], point[0]], as_tuple=True)[0]] = bool(label)
+                idx |= point_idx
+            if texts is not None:
+                if isinstance(texts, str):
+                    texts = [texts]
+                crop_ims, filter_idx = [], []
+                for i, b in enumerate(result.boxes.xyxy.tolist()):
+                    x1, y1, x2, y2 = (int(x) for x in b)
+                    if (masks[i].sum() if TORCH_1_10 else masks[i].sum(0).sum()) <= 100:  # torch 1.9 bug workaround
+                        filter_idx.append(i)
+                        continue
+                    crop_ims.append(Image.fromarray(result.orig_img[y1:y2, x1:x2, ::-1]))
+                similarity = self._clip_inference(crop_ims, texts)
+                text_idx = torch.argmax(similarity, dim=-1)  # (M, )
+                if len(filter_idx):
+                    text_idx += (torch.tensor(filter_idx, device=self.device)[None] <= int(text_idx)).sum(0)
+                idx[text_idx] = True
+
+            prompt_results.append(result[idx])
+
+        return prompt_results
+
+    def _clip_inference(self, images, texts):
+        """
+        Perform CLIP inference to calculate similarity between images and text prompts.
+
+        Args:
+            images (list[PIL.Image]): List of source images, each should be PIL.Image with RGB channel order.
+            texts (list[str]): List of prompt texts, each should be a string object.
+
+        Returns:
+            (torch.Tensor): Similarity matrix between given images and texts with shape (M, N).
+        """
+        try:
+            import clip
+        except ImportError:
+            checks.check_requirements("git+https://github.com/ultralytics/CLIP.git")
+            import clip
+        if (not hasattr(self, "clip_model")) or (not hasattr(self, "clip_preprocess")):
+            self.clip_model, self.clip_preprocess = clip.load("ViT-B/32", device=self.device)
+        images = torch.stack([self.clip_preprocess(image).to(self.device) for image in images])
+        tokenized_text = clip.tokenize(texts).to(self.device)
+        image_features = self.clip_model.encode_image(images)
+        text_features = self.clip_model.encode_text(tokenized_text)
+        image_features /= image_features.norm(dim=-1, keepdim=True)  # (N, 512)
+        text_features /= text_features.norm(dim=-1, keepdim=True)  # (M, 512)
+        return (image_features * text_features[:, None]).sum(-1)  # (M, N)
+
+    def set_prompts(self, prompts):
+        """Set prompts to be used during inference."""
+        self.prompts = prompts

ultralytics/models/fastsam/utils.py

@@ -0,0 +1,24 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+
+def adjust_bboxes_to_image_border(boxes, image_shape, threshold=20):
+    """
+    Adjust bounding boxes to stick to image border if they are within a certain threshold.
+
+    Args:
+        boxes (torch.Tensor): Bounding boxes with shape (N, 4) in xyxy format.
+        image_shape (tuple): Image dimensions as (height, width).
+        threshold (int): Pixel threshold for considering a box close to the border.
+
+    Returns:
+        (torch.Tensor): Adjusted bounding boxes with shape (N, 4).
+    """
+    # Image dimensions
+    h, w = image_shape
+
+    # Adjust boxes that are close to image borders
+    boxes[boxes[:, 0] < threshold, 0] = 0  # x1
+    boxes[boxes[:, 1] < threshold, 1] = 0  # y1
+    boxes[boxes[:, 2] > w - threshold, 2] = w  # x2
+    boxes[boxes[:, 3] > h - threshold, 3] = h  # y2
+    return boxes

ultralytics/models/fastsam/val.py

@@ -0,0 +1,40 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from ultralytics.models.yolo.segment import SegmentationValidator
+
+
+class FastSAMValidator(SegmentationValidator):
+    """
+    Custom validation class for Fast SAM (Segment Anything Model) segmentation in Ultralytics YOLO framework.
+
+    Extends the SegmentationValidator class, customizing the validation process specifically for Fast SAM. This class
+    sets the task to 'segment' and uses the SegmentMetrics for evaluation. Additionally, plotting features are disabled
+    to avoid errors during validation.
+
+    Attributes:
+        dataloader (torch.utils.data.DataLoader): The data loader object used for validation.
+        save_dir (Path): The directory where validation results will be saved.
+        args (SimpleNamespace): Additional arguments for customization of the validation process.
+        _callbacks (list): List of callback functions to be invoked during validation.
+        metrics (SegmentMetrics): Segmentation metrics calculator for evaluation.
+
+    Methods:
+        __init__: Initialize the FastSAMValidator with custom settings for Fast SAM.
+    """
+
+    def __init__(self, dataloader=None, save_dir=None, args=None, _callbacks=None):
+        """
+        Initialize the FastSAMValidator class, setting the task to 'segment' and metrics to SegmentMetrics.
+
+        Args:
+            dataloader (torch.utils.data.DataLoader, optional): Dataloader to be used for validation.
+            save_dir (Path, optional): Directory to save results.
+            args (SimpleNamespace, optional): Configuration for the validator.
+            _callbacks (list, optional): List of callback functions to be invoked during validation.
+
+        Notes:
+            Plots for ConfusionMatrix and other related metrics are disabled in this class to avoid errors.
+        """
+        super().__init__(dataloader, save_dir, args, _callbacks)
+        self.args.task = "segment"
+        self.args.plots = False  # disable ConfusionMatrix and other plots to avoid errors

ultralytics/models/nas/__init__.py

@@ -0,0 +1,7 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from .model import NAS
+from .predict import NASPredictor
+from .val import NASValidator
+
+__all__ = "NASPredictor", "NASValidator", "NAS"

ultralytics/models/nas/model.py

@@ -0,0 +1,101 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import torch
+
+from ultralytics.engine.model import Model
+from ultralytics.utils import DEFAULT_CFG_DICT
+from ultralytics.utils.downloads import attempt_download_asset
+from ultralytics.utils.patches import torch_load
+from ultralytics.utils.torch_utils import model_info
+
+from .predict import NASPredictor
+from .val import NASValidator
+
+
+class NAS(Model):
+    """
+    YOLO-NAS model for object detection.
+
+    This class provides an interface for the YOLO-NAS models and extends the `Model` class from ultralytics engine.
+    It is designed to facilitate the task of object detection using pre-trained or custom-trained YOLO-NAS models.
+
+    Attributes:
+        model (torch.nn.Module): The loaded YOLO-NAS model.
+        task (str): The task type for the model, defaults to 'detect'.
+        predictor (NASPredictor): The predictor instance for making predictions.
+        validator (NASValidator): The validator instance for model validation.
+
+    Methods:
+        info: Log model information and return model details.
+
+    Examples:
+        >>> from ultralytics import NAS
+        >>> model = NAS("yolo_nas_s")
+        >>> results = model.predict("ultralytics/assets/bus.jpg")
+
+    Notes:
+        YOLO-NAS models only support pre-trained models. Do not provide YAML configuration files.
+    """
+
+    def __init__(self, model: str = "yolo_nas_s.pt") -> None:
+        """Initialize the NAS model with the provided or default model."""
+        assert Path(model).suffix not in {".yaml", ".yml"}, "YOLO-NAS models only support pre-trained models."
+        super().__init__(model, task="detect")
+
+    def _load(self, weights: str, task=None) -> None:
+        """
+        Load an existing NAS model weights or create a new NAS model with pretrained weights.
+
+        Args:
+            weights (str): Path to the model weights file or model name.
+            task (str, optional): Task type for the model.
+        """
+        import super_gradients
+
+        suffix = Path(weights).suffix
+        if suffix == ".pt":
+            self.model = torch_load(attempt_download_asset(weights))
+        elif suffix == "":
+            self.model = super_gradients.training.models.get(weights, pretrained_weights="coco")
+
+        # Override the forward method to ignore additional arguments
+        def new_forward(x, *args, **kwargs):
+            """Ignore additional __call__ arguments."""
+            return self.model._original_forward(x)
+
+        self.model._original_forward = self.model.forward
+        self.model.forward = new_forward
+
+        # Standardize model attributes for compatibility
+        self.model.fuse = lambda verbose=True: self.model
+        self.model.stride = torch.tensor([32])
+        self.model.names = dict(enumerate(self.model._class_names))
+        self.model.is_fused = lambda: False  # for info()
+        self.model.yaml = {}  # for info()
+        self.model.pt_path = weights  # for export()
+        self.model.task = "detect"  # for export()
+        self.model.args = {**DEFAULT_CFG_DICT, **self.overrides}  # for export()
+        self.model.eval()
+
+    def info(self, detailed: bool = False, verbose: bool = True) -> dict[str, Any]:
+        """
+        Log model information.
+
+        Args:
+            detailed (bool): Show detailed information about model.
+            verbose (bool): Controls verbosity.
+
+        Returns:
+            (dict[str, Any]): Model information dictionary.
+        """
+        return model_info(self.model, detailed=detailed, verbose=verbose, imgsz=640)
+
+    @property
+    def task_map(self) -> dict[str, dict[str, Any]]:
+        """Return a dictionary mapping tasks to respective predictor and validator classes."""
+        return {"detect": {"predictor": NASPredictor, "validator": NASValidator}}

ultralytics/models/nas/predict.py

@@ -0,0 +1,58 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import torch
+
+from ultralytics.models.yolo.detect.predict import DetectionPredictor
+from ultralytics.utils import ops
+
+
+class NASPredictor(DetectionPredictor):
+    """
+    Ultralytics YOLO NAS Predictor for object detection.
+
+    This class extends the DetectionPredictor from ultralytics engine and is responsible for post-processing the
+    raw predictions generated by the YOLO NAS models. It applies operations like non-maximum suppression and
+    scaling the bounding boxes to fit the original image dimensions.
+
+    Attributes:
+        args (Namespace): Namespace containing various configurations for post-processing including confidence
+            threshold, IoU threshold, agnostic NMS flag, maximum detections, and class filtering options.
+        model (torch.nn.Module): The YOLO NAS model used for inference.
+        batch (list): Batch of inputs for processing.
+
+    Examples:
+        >>> from ultralytics import NAS
+        >>> model = NAS("yolo_nas_s")
+        >>> predictor = model.predictor
+
+        Assume that raw_preds, img, orig_imgs are available
+        >>> results = predictor.postprocess(raw_preds, img, orig_imgs)
+
+    Notes:
+        Typically, this class is not instantiated directly. It is used internally within the NAS class.
+    """
+
+    def postprocess(self, preds_in, img, orig_imgs):
+        """
+        Postprocess NAS model predictions to generate final detection results.
+
+        This method takes raw predictions from a YOLO NAS model, converts bounding box formats, and applies
+        post-processing operations to generate the final detection results compatible with Ultralytics
+        result visualization and analysis tools.
+
+        Args:
+            preds_in (list): Raw predictions from the NAS model, typically containing bounding boxes and class scores.
+            img (torch.Tensor): Input image tensor that was fed to the model, with shape (B, C, H, W).
+            orig_imgs (list | torch.Tensor | np.ndarray): Original images before preprocessing, used for scaling
+                coordinates back to original dimensions.
+
+        Returns:
+            (list): List of Results objects containing the processed predictions for each image in the batch.
+
+        Examples:
+            >>> predictor = NAS("yolo_nas_s").predictor
+            >>> results = predictor.postprocess(raw_preds, img, orig_imgs)
+        """
+        boxes = ops.xyxy2xywh(preds_in[0][0])  # Convert bounding boxes from xyxy to xywh format
+        preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1)  # Concatenate boxes with class scores
+        return super().postprocess(preds, img, orig_imgs)

ultralytics/models/nas/val.py

@@ -0,0 +1,39 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import torch
+
+from ultralytics.models.yolo.detect import DetectionValidator
+from ultralytics.utils import ops
+
+__all__ = ["NASValidator"]
+
+
+class NASValidator(DetectionValidator):
+    """
+    Ultralytics YOLO NAS Validator for object detection.
+
+    Extends DetectionValidator from the Ultralytics models package and is designed to post-process the raw predictions
+    generated by YOLO NAS models. It performs non-maximum suppression to remove overlapping and low-confidence boxes,
+    ultimately producing the final detections.
+
+    Attributes:
+        args (Namespace): Namespace containing various configurations for post-processing, such as confidence and IoU
+            thresholds.
+        lb (torch.Tensor): Optional tensor for multilabel NMS.
+
+    Examples:
+        >>> from ultralytics import NAS
+        >>> model = NAS("yolo_nas_s")
+        >>> validator = model.validator
+        >>> # Assumes that raw_preds are available
+        >>> final_preds = validator.postprocess(raw_preds)
+
+    Notes:
+        This class is generally not instantiated directly but is used internally within the NAS class.
+    """
+
+    def postprocess(self, preds_in):
+        """Apply Non-maximum suppression to prediction outputs."""
+        boxes = ops.xyxy2xywh(preds_in[0][0])  # Convert bounding box format from xyxy to xywh
+        preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1)  # Concatenate boxes with scores and permute
+        return super().postprocess(preds)

ultralytics/models/rtdetr/__init__.py

@@ -0,0 +1,7 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from .model import RTDETR
+from .predict import RTDETRPredictor
+from .val import RTDETRValidator
+
+__all__ = "RTDETRPredictor", "RTDETRValidator", "RTDETR"

ultralytics/models/rtdetr/model.py

@@ -0,0 +1,66 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+"""
+Interface for Baidu's RT-DETR, a Vision Transformer-based real-time object detector.
+
+RT-DETR offers real-time performance and high accuracy, excelling in accelerated backends like CUDA with TensorRT.
+It features an efficient hybrid encoder and IoU-aware query selection for enhanced detection accuracy.
+
+References:
+    https://arxiv.org/pdf/2304.08069.pdf
+"""
+
+from ultralytics.engine.model import Model
+from ultralytics.nn.tasks import RTDETRDetectionModel
+from ultralytics.utils.torch_utils import TORCH_1_11
+
+from .predict import RTDETRPredictor
+from .train import RTDETRTrainer
+from .val import RTDETRValidator
+
+
+class RTDETR(Model):
+    """
+    Interface for Baidu's RT-DETR model, a Vision Transformer-based real-time object detector.
+
+    This model provides real-time performance with high accuracy. It supports efficient hybrid encoding, IoU-aware
+    query selection, and adaptable inference speed.
+
+    Attributes:
+        model (str): Path to the pre-trained model.
+
+    Methods:
+        task_map: Return a task map for RT-DETR, associating tasks with corresponding Ultralytics classes.
+
+    Examples:
+        Initialize RT-DETR with a pre-trained model
+        >>> from ultralytics import RTDETR
+        >>> model = RTDETR("rtdetr-l.pt")
+        >>> results = model("image.jpg")
+    """
+
+    def __init__(self, model: str = "rtdetr-l.pt") -> None:
+        """
+        Initialize the RT-DETR model with the given pre-trained model file.
+
+        Args:
+            model (str): Path to the pre-trained model. Supports .pt, .yaml, and .yml formats.
+        """
+        assert TORCH_1_11, "RTDETR requires torch>=1.11"
+        super().__init__(model=model, task="detect")
+
+    @property
+    def task_map(self) -> dict:
+        """
+        Return a task map for RT-DETR, associating tasks with corresponding Ultralytics classes.
+
+        Returns:
+            (dict): A dictionary mapping task names to Ultralytics task classes for the RT-DETR model.
+        """
+        return {
+            "detect": {
+                "predictor": RTDETRPredictor,
+                "validator": RTDETRValidator,
+                "trainer": RTDETRTrainer,
+                "model": RTDETRDetectionModel,
+            }
+        }

ultralytics/models/rtdetr/predict.py

@@ -0,0 +1,92 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import torch
+
+from ultralytics.data.augment import LetterBox
+from ultralytics.engine.predictor import BasePredictor
+from ultralytics.engine.results import Results
+from ultralytics.utils import ops
+
+
+class RTDETRPredictor(BasePredictor):
+    """
+    RT-DETR (Real-Time Detection Transformer) Predictor extending the BasePredictor class for making predictions.
+
+    This class leverages Vision Transformers to provide real-time object detection while maintaining high accuracy.
+    It supports key features like efficient hybrid encoding and IoU-aware query selection.
+
+    Attributes:
+        imgsz (int): Image size for inference (must be square and scale-filled).
+        args (dict): Argument overrides for the predictor.
+        model (torch.nn.Module): The loaded RT-DETR model.
+        batch (list): Current batch of processed inputs.
+
+    Methods:
+        postprocess: Postprocess raw model predictions to generate bounding boxes and confidence scores.
+        pre_transform: Pre-transform input images before feeding them into the model for inference.
+
+    Examples:
+        >>> from ultralytics.utils import ASSETS
+        >>> from ultralytics.models.rtdetr import RTDETRPredictor
+        >>> args = dict(model="rtdetr-l.pt", source=ASSETS)
+        >>> predictor = RTDETRPredictor(overrides=args)
+        >>> predictor.predict_cli()
+    """
+
+    def postprocess(self, preds, img, orig_imgs):
+        """
+        Postprocess the raw predictions from the model to generate bounding boxes and confidence scores.
+
+        The method filters detections based on confidence and class if specified in `self.args`. It converts
+        model predictions to Results objects containing properly scaled bounding boxes.
+
+        Args:
+            preds (list | tuple): List of [predictions, extra] from the model, where predictions contain
+                bounding boxes and scores.
+            img (torch.Tensor): Processed input images with shape (N, 3, H, W).
+            orig_imgs (list | torch.Tensor): Original, unprocessed images.
+
+        Returns:
+            results (list[Results]): A list of Results objects containing the post-processed bounding boxes,
+                confidence scores, and class labels.
+        """
+        if not isinstance(preds, (list, tuple)):  # list for PyTorch inference but list[0] Tensor for export inference
+            preds = [preds, None]
+
+        nd = preds[0].shape[-1]
+        bboxes, scores = preds[0].split((4, nd - 4), dim=-1)
+
+        if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
+            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
+
+        results = []
+        for bbox, score, orig_img, img_path in zip(bboxes, scores, orig_imgs, self.batch[0]):  # (300, 4)
+            bbox = ops.xywh2xyxy(bbox)
+            max_score, cls = score.max(-1, keepdim=True)  # (300, 1)
+            idx = max_score.squeeze(-1) > self.args.conf  # (300, )
+            if self.args.classes is not None:
+                idx = (cls == torch.tensor(self.args.classes, device=cls.device)).any(1) & idx
+            pred = torch.cat([bbox, max_score, cls], dim=-1)[idx]  # filter
+            pred = pred[pred[:, 4].argsort(descending=True)][: self.args.max_det]
+            oh, ow = orig_img.shape[:2]
+            pred[..., [0, 2]] *= ow  # scale x coordinates to original width
+            pred[..., [1, 3]] *= oh  # scale y coordinates to original height
+            results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
+        return results
+
+    def pre_transform(self, im):
+        """
+        Pre-transform input images before feeding them into the model for inference.
+
+        The input images are letterboxed to ensure a square aspect ratio and scale-filled. The size must be square
+        (640) and scale_filled.
+
+        Args:
+            im (list[np.ndarray]  | torch.Tensor): Input images of shape (N, 3, H, W) for tensor,
+                [(H, W, 3) x N] for list.
+
+        Returns:
+            (list): List of pre-transformed images ready for model inference.
+        """
+        letterbox = LetterBox(self.imgsz, auto=False, scale_fill=True)
+        return [letterbox(image=x) for x in im]

ultralytics/models/rtdetr/train.py

@@ -0,0 +1,92 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from copy import copy
+
+from ultralytics.models.yolo.detect import DetectionTrainer
+from ultralytics.nn.tasks import RTDETRDetectionModel
+from ultralytics.utils import RANK, colorstr
+
+from .val import RTDETRDataset, RTDETRValidator
+
+
+class RTDETRTrainer(DetectionTrainer):
+    """
+    Trainer class for the RT-DETR model developed by Baidu for real-time object detection.
+
+    This class extends the DetectionTrainer class for YOLO to adapt to the specific features and architecture of RT-DETR.
+    The model leverages Vision Transformers and has capabilities like IoU-aware query selection and adaptable inference
+    speed.
+
+    Attributes:
+        loss_names (tuple): Names of the loss components used for training.
+        data (dict): Dataset configuration containing class count and other parameters.
+        args (dict): Training arguments and hyperparameters.
+        save_dir (Path): Directory to save training results.
+        test_loader (DataLoader): DataLoader for validation/testing data.
+
+    Methods:
+        get_model: Initialize and return an RT-DETR model for object detection tasks.
+        build_dataset: Build and return an RT-DETR dataset for training or validation.
+        get_validator: Return a DetectionValidator suitable for RT-DETR model validation.
+
+    Notes:
+        - F.grid_sample used in RT-DETR does not support the `deterministic=True` argument.
+        - AMP training can lead to NaN outputs and may produce errors during bipartite graph matching.
+
+    Examples:
+        >>> from ultralytics.models.rtdetr.train import RTDETRTrainer
+        >>> args = dict(model="rtdetr-l.yaml", data="coco8.yaml", imgsz=640, epochs=3)
+        >>> trainer = RTDETRTrainer(overrides=args)
+        >>> trainer.train()
+    """
+
+    def get_model(self, cfg: dict | None = None, weights: str | None = None, verbose: bool = True):
+        """
+        Initialize and return an RT-DETR model for object detection tasks.
+
+        Args:
+            cfg (dict, optional): Model configuration.
+            weights (str, optional): Path to pre-trained model weights.
+            verbose (bool): Verbose logging if True.
+
+        Returns:
+            (RTDETRDetectionModel): Initialized model.
+        """
+        model = RTDETRDetectionModel(cfg, nc=self.data["nc"], ch=self.data["channels"], verbose=verbose and RANK == -1)
+        if weights:
+            model.load(weights)
+        return model
+
+    def build_dataset(self, img_path: str, mode: str = "val", batch: int | None = None):
+        """
+        Build and return an RT-DETR dataset for training or validation.
+
+        Args:
+            img_path (str): Path to the folder containing images.
+            mode (str): Dataset mode, either 'train' or 'val'.
+            batch (int, optional): Batch size for rectangle training.
+
+        Returns:
+            (RTDETRDataset): Dataset object for the specific mode.
+        """
+        return RTDETRDataset(
+            img_path=img_path,
+            imgsz=self.args.imgsz,
+            batch_size=batch,
+            augment=mode == "train",
+            hyp=self.args,
+            rect=False,
+            cache=self.args.cache or None,
+            single_cls=self.args.single_cls or False,
+            prefix=colorstr(f"{mode}: "),
+            classes=self.args.classes,
+            data=self.data,
+            fraction=self.args.fraction if mode == "train" else 1.0,
+        )
+
+    def get_validator(self):
+        """Return a DetectionValidator suitable for RT-DETR model validation."""
+        self.loss_names = "giou_loss", "cls_loss", "l1_loss"
+        return RTDETRValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))

ultralytics/models/rtdetr/val.py

@@ -0,0 +1,218 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import torch
+
+from ultralytics.data import YOLODataset
+from ultralytics.data.augment import Compose, Format, v8_transforms
+from ultralytics.models.yolo.detect import DetectionValidator
+from ultralytics.utils import colorstr, ops
+
+__all__ = ("RTDETRValidator",)  # tuple or list
+
+
+class RTDETRDataset(YOLODataset):
+    """
+    Real-Time DEtection and TRacking (RT-DETR) dataset class extending the base YOLODataset class.
+
+    This specialized dataset class is designed for use with the RT-DETR object detection model and is optimized for
+    real-time detection and tracking tasks.
+
+    Attributes:
+        augment (bool): Whether to apply data augmentation.
+        rect (bool): Whether to use rectangular training.
+        use_segments (bool): Whether to use segmentation masks.
+        use_keypoints (bool): Whether to use keypoint annotations.
+        imgsz (int): Target image size for training.
+
+    Methods:
+        load_image: Load one image from dataset index.
+        build_transforms: Build transformation pipeline for the dataset.
+
+    Examples:
+        Initialize an RT-DETR dataset
+        >>> dataset = RTDETRDataset(img_path="path/to/images", imgsz=640)
+        >>> image, hw = dataset.load_image(0)
+    """
+
+    def __init__(self, *args, data=None, **kwargs):
+        """
+        Initialize the RTDETRDataset class by inheriting from the YOLODataset class.
+
+        This constructor sets up a dataset specifically optimized for the RT-DETR (Real-Time DEtection and TRacking)
+        model, building upon the base YOLODataset functionality.
+
+        Args:
+            *args (Any): Variable length argument list passed to the parent YOLODataset class.
+            data (dict | None): Dictionary containing dataset information. If None, default values will be used.
+            **kwargs (Any): Additional keyword arguments passed to the parent YOLODataset class.
+        """
+        super().__init__(*args, data=data, **kwargs)
+
+    def load_image(self, i, rect_mode=False):
+        """
+        Load one image from dataset index 'i'.
+
+        Args:
+            i (int): Index of the image to load.
+            rect_mode (bool, optional): Whether to use rectangular mode for batch inference.
+
+        Returns:
+            im (torch.Tensor): The loaded image.
+            resized_hw (tuple): Height and width of the resized image with shape (2,).
+
+        Examples:
+            Load an image from the dataset
+            >>> dataset = RTDETRDataset(img_path="path/to/images")
+            >>> image, hw = dataset.load_image(0)
+        """
+        return super().load_image(i=i, rect_mode=rect_mode)
+
+    def build_transforms(self, hyp=None):
+        """
+        Build transformation pipeline for the dataset.
+
+        Args:
+            hyp (dict, optional): Hyperparameters for transformations.
+
+        Returns:
+            (Compose): Composition of transformation functions.
+        """
+        if self.augment:
+            hyp.mosaic = hyp.mosaic if self.augment and not self.rect else 0.0
+            hyp.mixup = hyp.mixup if self.augment and not self.rect else 0.0
+            hyp.cutmix = hyp.cutmix if self.augment and not self.rect else 0.0
+            transforms = v8_transforms(self, self.imgsz, hyp, stretch=True)
+        else:
+            # transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz), auto=False, scale_fill=True)])
+            transforms = Compose([])
+        transforms.append(
+            Format(
+                bbox_format="xywh",
+                normalize=True,
+                return_mask=self.use_segments,
+                return_keypoint=self.use_keypoints,
+                batch_idx=True,
+                mask_ratio=hyp.mask_ratio,
+                mask_overlap=hyp.overlap_mask,
+            )
+        )
+        return transforms
+
+
+class RTDETRValidator(DetectionValidator):
+    """
+    RTDETRValidator extends the DetectionValidator class to provide validation capabilities specifically tailored for
+    the RT-DETR (Real-Time DETR) object detection model.
+
+    The class allows building of an RTDETR-specific dataset for validation, applies Non-maximum suppression for
+    post-processing, and updates evaluation metrics accordingly.
+
+    Attributes:
+        args (Namespace): Configuration arguments for validation.
+        data (dict): Dataset configuration dictionary.
+
+    Methods:
+        build_dataset: Build an RTDETR Dataset for validation.
+        postprocess: Apply Non-maximum suppression to prediction outputs.
+
+    Examples:
+        Initialize and run RT-DETR validation
+        >>> from ultralytics.models.rtdetr import RTDETRValidator
+        >>> args = dict(model="rtdetr-l.pt", data="coco8.yaml")
+        >>> validator = RTDETRValidator(args=args)
+        >>> validator()
+
+    Notes:
+        For further details on the attributes and methods, refer to the parent DetectionValidator class.
+    """
+
+    def build_dataset(self, img_path, mode="val", batch=None):
+        """
+        Build an RTDETR Dataset.
+
+        Args:
+            img_path (str): Path to the folder containing images.
+            mode (str, optional): `train` mode or `val` mode, users are able to customize different augmentations for
+                each mode.
+            batch (int, optional): Size of batches, this is for `rect`.
+
+        Returns:
+            (RTDETRDataset): Dataset configured for RT-DETR validation.
+        """
+        return RTDETRDataset(
+            img_path=img_path,
+            imgsz=self.args.imgsz,
+            batch_size=batch,
+            augment=False,  # no augmentation
+            hyp=self.args,
+            rect=False,  # no rect
+            cache=self.args.cache or None,
+            prefix=colorstr(f"{mode}: "),
+            data=self.data,
+        )
+
+    def postprocess(
+        self, preds: torch.Tensor | list[torch.Tensor] | tuple[torch.Tensor]
+    ) -> list[dict[str, torch.Tensor]]:
+        """
+        Apply Non-maximum suppression to prediction outputs.
+
+        Args:
+            preds (torch.Tensor | list | tuple): Raw predictions from the model. If tensor, should have shape
+                (batch_size, num_predictions, num_classes + 4) where last dimension contains bbox coords and class scores.
+
+        Returns:
+            (list[dict[str, torch.Tensor]]): List of dictionaries for each image, each containing:
+                - 'bboxes': Tensor of shape (N, 4) with bounding box coordinates
+                - 'conf': Tensor of shape (N,) with confidence scores
+                - 'cls': Tensor of shape (N,) with class indices
+        """
+        if not isinstance(preds, (list, tuple)):  # list for PyTorch inference but list[0] Tensor for export inference
+            preds = [preds, None]
+
+        bs, _, nd = preds[0].shape
+        bboxes, scores = preds[0].split((4, nd - 4), dim=-1)
+        bboxes *= self.args.imgsz
+        outputs = [torch.zeros((0, 6), device=bboxes.device)] * bs
+        for i, bbox in enumerate(bboxes):  # (300, 4)
+            bbox = ops.xywh2xyxy(bbox)
+            score, cls = scores[i].max(-1)  # (300, )
+            pred = torch.cat([bbox, score[..., None], cls[..., None]], dim=-1)  # filter
+            # Sort by confidence to correctly get internal metrics
+            pred = pred[score.argsort(descending=True)]
+            outputs[i] = pred[score > self.args.conf]
+
+        return [{"bboxes": x[:, :4], "conf": x[:, 4], "cls": x[:, 5]} for x in outputs]
+
+    def pred_to_json(self, predn: dict[str, torch.Tensor], pbatch: dict[str, Any]) -> None:
+        """
+        Serialize YOLO predictions to COCO json format.
+
+        Args:
+            predn (dict[str, torch.Tensor]): Predictions dictionary containing 'bboxes', 'conf', and 'cls' keys
+                with bounding box coordinates, confidence scores, and class predictions.
+            pbatch (dict[str, Any]): Batch dictionary containing 'imgsz', 'ori_shape', 'ratio_pad', and 'im_file'.
+        """
+        path = Path(pbatch["im_file"])
+        stem = path.stem
+        image_id = int(stem) if stem.isnumeric() else stem
+        box = predn["bboxes"].clone()
+        box[..., [0, 2]] *= pbatch["ori_shape"][1] / self.args.imgsz  # native-space pred
+        box[..., [1, 3]] *= pbatch["ori_shape"][0] / self.args.imgsz  # native-space pred
+        box = ops.xyxy2xywh(box)  # xywh
+        box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
+        for b, s, c in zip(box.tolist(), predn["conf"].tolist(), predn["cls"].tolist()):
+            self.jdict.append(
+                {
+                    "image_id": image_id,
+                    "file_name": path.name,
+                    "category_id": self.class_map[int(c)],
+                    "bbox": [round(x, 3) for x in b],
+                    "score": round(s, 5),
+                }
+            )

ultralytics/models/sam/__init__.py

@@ -0,0 +1,12 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from .model import SAM
+from .predict import Predictor, SAM2DynamicInteractivePredictor, SAM2Predictor, SAM2VideoPredictor
+
+__all__ = (
+    "SAM",
+    "Predictor",
+    "SAM2Predictor",
+    "SAM2VideoPredictor",
+    "SAM2DynamicInteractivePredictor",
+)  # tuple or list of exportable items

ultralytics/models/sam/amg.py

@@ -0,0 +1,281 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+import math
+from collections.abc import Generator
+from itertools import product
+from typing import Any
+
+import numpy as np
+import torch
+
+
+def is_box_near_crop_edge(
+    boxes: torch.Tensor, crop_box: list[int], orig_box: list[int], atol: float = 20.0
+) -> torch.Tensor:
+    """
+    Determine if bounding boxes are near the edge of a cropped image region using a specified tolerance.
+
+    Args:
+        boxes (torch.Tensor): Bounding boxes in XYXY format.
+        crop_box (list[int]): Crop box coordinates in [x0, y0, x1, y1] format.
+        orig_box (list[int]): Original image box coordinates in [x0, y0, x1, y1] format.
+        atol (float, optional): Absolute tolerance for edge proximity detection.
+
+    Returns:
+        (torch.Tensor): Boolean tensor indicating which boxes are near crop edges.
+
+    Examples:
+        >>> boxes = torch.tensor([[10, 10, 50, 50], [100, 100, 150, 150]])
+        >>> crop_box = [0, 0, 200, 200]
+        >>> orig_box = [0, 0, 300, 300]
+        >>> near_edge = is_box_near_crop_edge(boxes, crop_box, orig_box, atol=20.0)
+    """
+    crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)
+    orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)
+    boxes = uncrop_boxes_xyxy(boxes, crop_box).float()
+    near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)
+    near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)
+    near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)
+    return torch.any(near_crop_edge, dim=1)
+
+
+def batch_iterator(batch_size: int, *args) -> Generator[list[Any]]:
+    """
+    Yield batches of data from input arguments with specified batch size for efficient processing.
+
+    This function takes a batch size and any number of iterables, then yields batches of elements from those
+    iterables. All input iterables must have the same length.
+
+    Args:
+        batch_size (int): Size of each batch to yield.
+        *args (Any): Variable length input iterables to batch. All iterables must have the same length.
+
+    Yields:
+        (list[Any]): A list of batched elements from each input iterable.
+
+    Examples:
+        >>> data = [1, 2, 3, 4, 5]
+        >>> labels = ["a", "b", "c", "d", "e"]
+        >>> for batch in batch_iterator(2, data, labels):
+        ...     print(batch)
+        [[1, 2], ['a', 'b']]
+        [[3, 4], ['c', 'd']]
+        [[5], ['e']]
+    """
+    assert args and all(len(a) == len(args[0]) for a in args), "Batched iteration must have same-size inputs."
+    n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)
+    for b in range(n_batches):
+        yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]
+
+
+def calculate_stability_score(masks: torch.Tensor, mask_threshold: float, threshold_offset: float) -> torch.Tensor:
+    """
+    Compute the stability score for a batch of masks.
+
+    The stability score is the IoU between binary masks obtained by thresholding the predicted mask logits at
+    high and low values.
+
+    Args:
+        masks (torch.Tensor): Batch of predicted mask logits.
+        mask_threshold (float): Threshold value for creating binary masks.
+        threshold_offset (float): Offset applied to the threshold for creating high and low binary masks.
+
+    Returns:
+        (torch.Tensor): Stability scores for each mask in the batch.
+
+    Notes:
+        - One mask is always contained inside the other.
+        - Memory is saved by preventing unnecessary cast to torch.int64.
+
+    Examples:
+        >>> masks = torch.rand(10, 256, 256)  # Batch of 10 masks
+        >>> mask_threshold = 0.5
+        >>> threshold_offset = 0.1
+        >>> stability_scores = calculate_stability_score(masks, mask_threshold, threshold_offset)
+    """
+    intersections = (masks > (mask_threshold + threshold_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32)
+    unions = (masks > (mask_threshold - threshold_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32)
+    return intersections / unions
+
+
+def build_point_grid(n_per_side: int) -> np.ndarray:
+    """Generate a 2D grid of evenly spaced points in the range [0,1]x[0,1] for image segmentation tasks."""
+    offset = 1 / (2 * n_per_side)
+    points_one_side = np.linspace(offset, 1 - offset, n_per_side)
+    points_x = np.tile(points_one_side[None, :], (n_per_side, 1))
+    points_y = np.tile(points_one_side[:, None], (1, n_per_side))
+    return np.stack([points_x, points_y], axis=-1).reshape(-1, 2)
+
+
+def build_all_layer_point_grids(n_per_side: int, n_layers: int, scale_per_layer: int) -> list[np.ndarray]:
+    """Generate point grids for multiple crop layers with varying scales and densities."""
+    return [build_point_grid(int(n_per_side / (scale_per_layer**i))) for i in range(n_layers + 1)]
+
+
+def generate_crop_boxes(
+    im_size: tuple[int, ...], n_layers: int, overlap_ratio: float
+) -> tuple[list[list[int]], list[int]]:
+    """
+    Generate crop boxes of varying sizes for multiscale image processing, with layered overlapping regions.
+
+    Args:
+        im_size (tuple[int, ...]): Height and width of the input image.
+        n_layers (int): Number of layers to generate crop boxes for.
+        overlap_ratio (float): Ratio of overlap between adjacent crop boxes.
+
+    Returns:
+        crop_boxes (list[list[int]]): List of crop boxes in [x0, y0, x1, y1] format.
+        layer_idxs (list[int]): List of layer indices corresponding to each crop box.
+
+    Examples:
+        >>> im_size = (800, 1200)  # Height, width
+        >>> n_layers = 3
+        >>> overlap_ratio = 0.25
+        >>> crop_boxes, layer_idxs = generate_crop_boxes(im_size, n_layers, overlap_ratio)
+    """
+    crop_boxes, layer_idxs = [], []
+    im_h, im_w = im_size
+    short_side = min(im_h, im_w)
+
+    # Original image
+    crop_boxes.append([0, 0, im_w, im_h])
+    layer_idxs.append(0)
+
+    def crop_len(orig_len, n_crops, overlap):
+        """Calculate the length of each crop given the original length, number of crops, and overlap."""
+        return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))
+
+    for i_layer in range(n_layers):
+        n_crops_per_side = 2 ** (i_layer + 1)
+        overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))
+
+        crop_w = crop_len(im_w, n_crops_per_side, overlap)
+        crop_h = crop_len(im_h, n_crops_per_side, overlap)
+
+        crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]
+        crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]
+
+        # Crops in XYWH format
+        for x0, y0 in product(crop_box_x0, crop_box_y0):
+            box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]
+            crop_boxes.append(box)
+            layer_idxs.append(i_layer + 1)
+
+    return crop_boxes, layer_idxs
+
+
+def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: list[int]) -> torch.Tensor:
+    """Uncrop bounding boxes by adding the crop box offset to their coordinates."""
+    x0, y0, _, _ = crop_box
+    offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)
+    # Check if boxes has a channel dimension
+    if len(boxes.shape) == 3:
+        offset = offset.unsqueeze(1)
+    return boxes + offset
+
+
+def uncrop_points(points: torch.Tensor, crop_box: list[int]) -> torch.Tensor:
+    """Uncrop points by adding the crop box offset to their coordinates."""
+    x0, y0, _, _ = crop_box
+    offset = torch.tensor([[x0, y0]], device=points.device)
+    # Check if points has a channel dimension
+    if len(points.shape) == 3:
+        offset = offset.unsqueeze(1)
+    return points + offset
+
+
+def uncrop_masks(masks: torch.Tensor, crop_box: list[int], orig_h: int, orig_w: int) -> torch.Tensor:
+    """Uncrop masks by padding them to the original image size, handling coordinate transformations."""
+    x0, y0, x1, y1 = crop_box
+    if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:
+        return masks
+    # Coordinate transform masks
+    pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)
+    pad = (x0, pad_x - x0, y0, pad_y - y0)
+    return torch.nn.functional.pad(masks, pad, value=0)
+
+
+def remove_small_regions(mask: np.ndarray, area_thresh: float, mode: str) -> tuple[np.ndarray, bool]:
+    """
+    Remove small disconnected regions or holes in a mask based on area threshold and mode.
+
+    Args:
+        mask (np.ndarray): Binary mask to process.
+        area_thresh (float): Area threshold below which regions will be removed.
+        mode (str): Processing mode, either 'holes' to fill small holes or 'islands' to remove small disconnected
+            regions.
+
+    Returns:
+        processed_mask (np.ndarray): Processed binary mask with small regions removed.
+        modified (bool): Whether any regions were modified.
+
+    Examples:
+        >>> mask = np.zeros((100, 100), dtype=np.bool_)
+        >>> mask[40:60, 40:60] = True  # Create a square
+        >>> mask[45:55, 45:55] = False  # Create a hole
+        >>> processed_mask, modified = remove_small_regions(mask, 50, "holes")
+    """
+    import cv2  # type: ignore
+
+    assert mode in {"holes", "islands"}, f"Provided mode {mode} is invalid"
+    correct_holes = mode == "holes"
+    working_mask = (correct_holes ^ mask).astype(np.uint8)
+    n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
+    sizes = stats[:, -1][1:]  # Row 0 is background label
+    small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
+    if not small_regions:
+        return mask, False
+    fill_labels = [0] + small_regions
+    if not correct_holes:
+        # If every region is below threshold, keep largest
+        fill_labels = [i for i in range(n_labels) if i not in fill_labels] or [int(np.argmax(sizes)) + 1]
+    mask = np.isin(regions, fill_labels)
+    return mask, True
+
+
+def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:
+    """
+    Calculate bounding boxes in XYXY format around binary masks.
+
+    Args:
+        masks (torch.Tensor): Binary masks with shape (B, H, W) or (B, C, H, W).
+
+    Returns:
+        (torch.Tensor): Bounding boxes in XYXY format with shape (B, 4) or (B, C, 4).
+
+    Notes:
+        - Handles empty masks by returning zero boxes.
+        - Preserves input tensor dimensions in the output.
+    """
+    # torch.max below raises an error on empty inputs, just skip in this case
+    if torch.numel(masks) == 0:
+        return torch.zeros(*masks.shape[:-2], 4, device=masks.device)
+
+    # Normalize shape to CxHxW
+    shape = masks.shape
+    h, w = shape[-2:]
+    masks = masks.flatten(0, -3) if len(shape) > 2 else masks.unsqueeze(0)
+    # Get top and bottom edges
+    in_height, _ = torch.max(masks, dim=-1)
+    in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]
+    bottom_edges, _ = torch.max(in_height_coords, dim=-1)
+    in_height_coords = in_height_coords + h * (~in_height)
+    top_edges, _ = torch.min(in_height_coords, dim=-1)
+
+    # Get left and right edges
+    in_width, _ = torch.max(masks, dim=-2)
+    in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]
+    right_edges, _ = torch.max(in_width_coords, dim=-1)
+    in_width_coords = in_width_coords + w * (~in_width)
+    left_edges, _ = torch.min(in_width_coords, dim=-1)
+
+    # If the mask is empty the right edge will be to the left of the left edge.
+    # Replace these boxes with [0, 0, 0, 0]
+    empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)
+    out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)
+    out = out * (~empty_filter).unsqueeze(-1)
+
+    # Return to original shape
+    return out.reshape(*shape[:-2], 4) if len(shape) > 2 else out[0]

ultralytics/models/sam/build.py

@@ -0,0 +1,358 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from functools import partial
+
+import torch
+
+from ultralytics.utils.downloads import attempt_download_asset
+
+from .modules.decoders import MaskDecoder
+from .modules.encoders import FpnNeck, Hiera, ImageEncoder, ImageEncoderViT, MemoryEncoder, PromptEncoder
+from .modules.memory_attention import MemoryAttention, MemoryAttentionLayer
+from .modules.sam import SAM2Model, SAMModel
+from .modules.tiny_encoder import TinyViT
+from .modules.transformer import TwoWayTransformer
+
+
+def build_sam_vit_h(checkpoint=None):
+    """Build and return a Segment Anything Model (SAM) h-size model with specified encoder parameters."""
+    return _build_sam(
+        encoder_embed_dim=1280,
+        encoder_depth=32,
+        encoder_num_heads=16,
+        encoder_global_attn_indexes=[7, 15, 23, 31],
+        checkpoint=checkpoint,
+    )
+
+
+def build_sam_vit_l(checkpoint=None):
+    """Build and return a Segment Anything Model (SAM) l-size model with specified encoder parameters."""
+    return _build_sam(
+        encoder_embed_dim=1024,
+        encoder_depth=24,
+        encoder_num_heads=16,
+        encoder_global_attn_indexes=[5, 11, 17, 23],
+        checkpoint=checkpoint,
+    )
+
+
+def build_sam_vit_b(checkpoint=None):
+    """Build and return a Segment Anything Model (SAM) b-size model with specified encoder parameters."""
+    return _build_sam(
+        encoder_embed_dim=768,
+        encoder_depth=12,
+        encoder_num_heads=12,
+        encoder_global_attn_indexes=[2, 5, 8, 11],
+        checkpoint=checkpoint,
+    )
+
+
+def build_mobile_sam(checkpoint=None):
+    """Build and return a Mobile Segment Anything Model (Mobile-SAM) for efficient image segmentation."""
+    return _build_sam(
+        encoder_embed_dim=[64, 128, 160, 320],
+        encoder_depth=[2, 2, 6, 2],
+        encoder_num_heads=[2, 4, 5, 10],
+        encoder_global_attn_indexes=None,
+        mobile_sam=True,
+        checkpoint=checkpoint,
+    )
+
+
+def build_sam2_t(checkpoint=None):
+    """Build and return a Segment Anything Model 2 (SAM2) tiny-size model with specified architecture parameters."""
+    return _build_sam2(
+        encoder_embed_dim=96,
+        encoder_stages=[1, 2, 7, 2],
+        encoder_num_heads=1,
+        encoder_global_att_blocks=[5, 7, 9],
+        encoder_window_spec=[8, 4, 14, 7],
+        encoder_backbone_channel_list=[768, 384, 192, 96],
+        checkpoint=checkpoint,
+    )
+
+
+def build_sam2_s(checkpoint=None):
+    """Build and return a small-size Segment Anything Model 2 (SAM2) with specified architecture parameters."""
+    return _build_sam2(
+        encoder_embed_dim=96,
+        encoder_stages=[1, 2, 11, 2],
+        encoder_num_heads=1,
+        encoder_global_att_blocks=[7, 10, 13],
+        encoder_window_spec=[8, 4, 14, 7],
+        encoder_backbone_channel_list=[768, 384, 192, 96],
+        checkpoint=checkpoint,
+    )
+
+
+def build_sam2_b(checkpoint=None):
+    """Build and return a Segment Anything Model 2 (SAM2) base-size model with specified architecture parameters."""
+    return _build_sam2(
+        encoder_embed_dim=112,
+        encoder_stages=[2, 3, 16, 3],
+        encoder_num_heads=2,
+        encoder_global_att_blocks=[12, 16, 20],
+        encoder_window_spec=[8, 4, 14, 7],
+        encoder_window_spatial_size=[14, 14],
+        encoder_backbone_channel_list=[896, 448, 224, 112],
+        checkpoint=checkpoint,
+    )
+
+
+def build_sam2_l(checkpoint=None):
+    """Build and return a large-size Segment Anything Model 2 (SAM2) with specified architecture parameters."""
+    return _build_sam2(
+        encoder_embed_dim=144,
+        encoder_stages=[2, 6, 36, 4],
+        encoder_num_heads=2,
+        encoder_global_att_blocks=[23, 33, 43],
+        encoder_window_spec=[8, 4, 16, 8],
+        encoder_backbone_channel_list=[1152, 576, 288, 144],
+        checkpoint=checkpoint,
+    )
+
+
+def _build_sam(
+    encoder_embed_dim,
+    encoder_depth,
+    encoder_num_heads,
+    encoder_global_attn_indexes,
+    checkpoint=None,
+    mobile_sam=False,
+):
+    """
+    Build a Segment Anything Model (SAM) with specified encoder parameters.
+
+    Args:
+        encoder_embed_dim (int | list[int]): Embedding dimension for the encoder.
+        encoder_depth (int | list[int]): Depth of the encoder.
+        encoder_num_heads (int | list[int]): Number of attention heads in the encoder.
+        encoder_global_attn_indexes (list[int] | None): Indexes for global attention in the encoder.
+        checkpoint (str | None, optional): Path to the model checkpoint file.
+        mobile_sam (bool, optional): Whether to build a Mobile-SAM model.
+
+    Returns:
+        (SAMModel): A Segment Anything Model instance with the specified architecture.
+
+    Examples:
+        >>> sam = _build_sam(768, 12, 12, [2, 5, 8, 11])
+        >>> sam = _build_sam([64, 128, 160, 320], [2, 2, 6, 2], [2, 4, 5, 10], None, mobile_sam=True)
+    """
+    prompt_embed_dim = 256
+    image_size = 1024
+    vit_patch_size = 16
+    image_embedding_size = image_size // vit_patch_size
+    image_encoder = (
+        TinyViT(
+            img_size=1024,
+            in_chans=3,
+            num_classes=1000,
+            embed_dims=encoder_embed_dim,
+            depths=encoder_depth,
+            num_heads=encoder_num_heads,
+            window_sizes=[7, 7, 14, 7],
+            mlp_ratio=4.0,
+            drop_rate=0.0,
+            drop_path_rate=0.0,
+            use_checkpoint=False,
+            mbconv_expand_ratio=4.0,
+            local_conv_size=3,
+            layer_lr_decay=0.8,
+        )
+        if mobile_sam
+        else ImageEncoderViT(
+            depth=encoder_depth,
+            embed_dim=encoder_embed_dim,
+            img_size=image_size,
+            mlp_ratio=4,
+            norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
+            num_heads=encoder_num_heads,
+            patch_size=vit_patch_size,
+            qkv_bias=True,
+            use_rel_pos=True,
+            global_attn_indexes=encoder_global_attn_indexes,
+            window_size=14,
+            out_chans=prompt_embed_dim,
+        )
+    )
+    sam = SAMModel(
+        image_encoder=image_encoder,
+        prompt_encoder=PromptEncoder(
+            embed_dim=prompt_embed_dim,
+            image_embedding_size=(image_embedding_size, image_embedding_size),
+            input_image_size=(image_size, image_size),
+            mask_in_chans=16,
+        ),
+        mask_decoder=MaskDecoder(
+            num_multimask_outputs=3,
+            transformer=TwoWayTransformer(
+                depth=2,
+                embedding_dim=prompt_embed_dim,
+                mlp_dim=2048,
+                num_heads=8,
+            ),
+            transformer_dim=prompt_embed_dim,
+            iou_head_depth=3,
+            iou_head_hidden_dim=256,
+        ),
+        pixel_mean=[123.675, 116.28, 103.53],
+        pixel_std=[58.395, 57.12, 57.375],
+    )
+    if checkpoint is not None:
+        checkpoint = attempt_download_asset(checkpoint)
+        with open(checkpoint, "rb") as f:
+            state_dict = torch.load(f)
+        sam.load_state_dict(state_dict)
+    sam.eval()
+    return sam
+
+
+def _build_sam2(
+    encoder_embed_dim=1280,
+    encoder_stages=[2, 6, 36, 4],
+    encoder_num_heads=2,
+    encoder_global_att_blocks=[7, 15, 23, 31],
+    encoder_backbone_channel_list=[1152, 576, 288, 144],
+    encoder_window_spatial_size=[7, 7],
+    encoder_window_spec=[8, 4, 16, 8],
+    checkpoint=None,
+):
+    """
+    Build and return a Segment Anything Model 2 (SAM2) with specified architecture parameters.
+
+    Args:
+        encoder_embed_dim (int, optional): Embedding dimension for the encoder.
+        encoder_stages (list[int], optional): Number of blocks in each stage of the encoder.
+        encoder_num_heads (int, optional): Number of attention heads in the encoder.
+        encoder_global_att_blocks (list[int], optional): Indices of global attention blocks in the encoder.
+        encoder_backbone_channel_list (list[int], optional): Channel dimensions for each level of the encoder backbone.
+        encoder_window_spatial_size (list[int], optional): Spatial size of the window for position embeddings.
+        encoder_window_spec (list[int], optional): Window specifications for each stage of the encoder.
+        checkpoint (str | None, optional): Path to the checkpoint file for loading pre-trained weights.
+
+    Returns:
+        (SAM2Model): A configured and initialized SAM2 model.
+
+    Examples:
+        >>> sam2_model = _build_sam2(encoder_embed_dim=96, encoder_stages=[1, 2, 7, 2])
+        >>> sam2_model.eval()
+    """
+    image_encoder = ImageEncoder(
+        trunk=Hiera(
+            embed_dim=encoder_embed_dim,
+            num_heads=encoder_num_heads,
+            stages=encoder_stages,
+            global_att_blocks=encoder_global_att_blocks,
+            window_pos_embed_bkg_spatial_size=encoder_window_spatial_size,
+            window_spec=encoder_window_spec,
+        ),
+        neck=FpnNeck(
+            d_model=256,
+            backbone_channel_list=encoder_backbone_channel_list,
+            fpn_top_down_levels=[2, 3],
+            fpn_interp_model="nearest",
+        ),
+        scalp=1,
+    )
+    memory_attention = MemoryAttention(d_model=256, pos_enc_at_input=True, num_layers=4, layer=MemoryAttentionLayer())
+    memory_encoder = MemoryEncoder(out_dim=64)
+
+    is_sam2_1 = checkpoint is not None and "sam2.1" in checkpoint
+    sam2 = SAM2Model(
+        image_encoder=image_encoder,
+        memory_attention=memory_attention,
+        memory_encoder=memory_encoder,
+        num_maskmem=7,
+        image_size=1024,
+        sigmoid_scale_for_mem_enc=20.0,
+        sigmoid_bias_for_mem_enc=-10.0,
+        use_mask_input_as_output_without_sam=True,
+        directly_add_no_mem_embed=True,
+        use_high_res_features_in_sam=True,
+        multimask_output_in_sam=True,
+        iou_prediction_use_sigmoid=True,
+        use_obj_ptrs_in_encoder=True,
+        add_tpos_enc_to_obj_ptrs=True,
+        only_obj_ptrs_in_the_past_for_eval=True,
+        pred_obj_scores=True,
+        pred_obj_scores_mlp=True,
+        fixed_no_obj_ptr=True,
+        multimask_output_for_tracking=True,
+        use_multimask_token_for_obj_ptr=True,
+        multimask_min_pt_num=0,
+        multimask_max_pt_num=1,
+        use_mlp_for_obj_ptr_proj=True,
+        compile_image_encoder=False,
+        no_obj_embed_spatial=is_sam2_1,
+        proj_tpos_enc_in_obj_ptrs=is_sam2_1,
+        use_signed_tpos_enc_to_obj_ptrs=is_sam2_1,
+        sam_mask_decoder_extra_args=dict(
+            dynamic_multimask_via_stability=True,
+            dynamic_multimask_stability_delta=0.05,
+            dynamic_multimask_stability_thresh=0.98,
+        ),
+    )
+
+    if checkpoint is not None:
+        checkpoint = attempt_download_asset(checkpoint)
+        with open(checkpoint, "rb") as f:
+            state_dict = torch.load(f)["model"]
+        sam2.load_state_dict(state_dict)
+    sam2.eval()
+    return sam2
+
+
+sam_model_map = {
+    "sam_h.pt": build_sam_vit_h,
+    "sam_l.pt": build_sam_vit_l,
+    "sam_b.pt": build_sam_vit_b,
+    "mobile_sam.pt": build_mobile_sam,
+    "sam2_t.pt": build_sam2_t,
+    "sam2_s.pt": build_sam2_s,
+    "sam2_b.pt": build_sam2_b,
+    "sam2_l.pt": build_sam2_l,
+    "sam2.1_t.pt": build_sam2_t,
+    "sam2.1_s.pt": build_sam2_s,
+    "sam2.1_b.pt": build_sam2_b,
+    "sam2.1_l.pt": build_sam2_l,
+}
+
+
+def build_sam(ckpt="sam_b.pt"):
+    """
+    Build and return a Segment Anything Model (SAM) based on the provided checkpoint.
+
+    Args:
+        ckpt (str | Path, optional): Path to the checkpoint file or name of a pre-defined SAM model.
+
+    Returns:
+        (SAMModel | SAM2Model): A configured and initialized SAM or SAM2 model instance.
+
+    Raises:
+        FileNotFoundError: If the provided checkpoint is not a supported SAM model.
+
+    Examples:
+        >>> sam_model = build_sam("sam_b.pt")
+        >>> sam_model = build_sam("path/to/custom_checkpoint.pt")
+
+    Notes:
+        Supported pre-defined models include:
+        - SAM: 'sam_h.pt', 'sam_l.pt', 'sam_b.pt', 'mobile_sam.pt'
+        - SAM2: 'sam2_t.pt', 'sam2_s.pt', 'sam2_b.pt', 'sam2_l.pt'
+    """
+    model_builder = None
+    ckpt = str(ckpt)  # to allow Path ckpt types
+    for k in sam_model_map.keys():
+        if ckpt.endswith(k):
+            model_builder = sam_model_map.get(k)
+
+    if not model_builder:
+        raise FileNotFoundError(f"{ckpt} is not a supported SAM model. Available models are: \n {sam_model_map.keys()}")
+
+    return model_builder(ckpt)

ultralytics/models/sam/model.py

@@ -0,0 +1,172 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+"""
+SAM model interface.
+
+This module provides an interface to the Segment Anything Model (SAM) from ultralytics, designed for real-time image
+segmentation tasks. The SAM model allows for promptable segmentation with unparalleled versatility in image analysis,
+and has been trained on the SA-1B dataset. It features zero-shot performance capabilities, enabling it to adapt to new
+image distributions and tasks without prior knowledge.
+
+Key Features:
+    - Promptable segmentation
+    - Real-time performance
+    - Zero-shot transfer capabilities
+    - Trained on SA-1B dataset
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from ultralytics.engine.model import Model
+from ultralytics.utils.torch_utils import model_info
+
+from .predict import Predictor, SAM2Predictor
+
+
+class SAM(Model):
+    """
+    SAM (Segment Anything Model) interface class for real-time image segmentation tasks.
+
+    This class provides an interface to the Segment Anything Model (SAM) from ultralytics, designed for
+    promptable segmentation with versatility in image analysis. It supports various prompts such as bounding
+    boxes, points, or labels, and features zero-shot performance capabilities.
+
+    Attributes:
+        model (torch.nn.Module): The loaded SAM model.
+        is_sam2 (bool): Indicates whether the model is SAM2 variant.
+        task (str): The task type, set to "segment" for SAM models.
+
+    Methods:
+        predict: Perform segmentation prediction on the given image or video source.
+        info: Log information about the SAM model.
+
+    Examples:
+        >>> sam = SAM("sam_b.pt")
+        >>> results = sam.predict("image.jpg", points=[[500, 375]])
+        >>> for r in results:
+        >>>     print(f"Detected {len(r.masks)} masks")
+    """
+
+    def __init__(self, model: str = "sam_b.pt") -> None:
+        """
+        Initialize the SAM (Segment Anything Model) instance.
+
+        Args:
+            model (str): Path to the pre-trained SAM model file. File should have a .pt or .pth extension.
+
+        Raises:
+            NotImplementedError: If the model file extension is not .pt or .pth.
+
+        Examples:
+            >>> sam = SAM("sam_b.pt")
+            >>> print(sam.is_sam2)
+        """
+        if model and Path(model).suffix not in {".pt", ".pth"}:
+            raise NotImplementedError("SAM prediction requires pre-trained *.pt or *.pth model.")
+        self.is_sam2 = "sam2" in Path(model).stem
+        super().__init__(model=model, task="segment")
+
+    def _load(self, weights: str, task=None):
+        """
+        Load the specified weights into the SAM model.
+
+        Args:
+            weights (str): Path to the weights file. Should be a .pt or .pth file containing the model parameters.
+            task (str | None): Task name. If provided, it specifies the particular task the model is being loaded for.
+
+        Examples:
+            >>> sam = SAM("sam_b.pt")
+            >>> sam._load("path/to/custom_weights.pt")
+        """
+        from .build import build_sam  # slow import
+
+        self.model = build_sam(weights)
+
+    def predict(self, source, stream: bool = False, bboxes=None, points=None, labels=None, **kwargs):
+        """
+        Perform segmentation prediction on the given image or video source.
+
+        Args:
+            source (str | PIL.Image | np.ndarray): Path to the image or video file, or a PIL.Image object, or
+                a np.ndarray object.
+            stream (bool): If True, enables real-time streaming.
+            bboxes (list[list[float]] | None): List of bounding box coordinates for prompted segmentation.
+            points (list[list[float]] | None): List of points for prompted segmentation.
+            labels (list[int] | None): List of labels for prompted segmentation.
+            **kwargs (Any): Additional keyword arguments for prediction.
+
+        Returns:
+            (list): The model predictions.
+
+        Examples:
+            >>> sam = SAM("sam_b.pt")
+            >>> results = sam.predict("image.jpg", points=[[500, 375]])
+            >>> for r in results:
+            ...     print(f"Detected {len(r.masks)} masks")
+        """
+        overrides = dict(conf=0.25, task="segment", mode="predict", imgsz=1024)
+        kwargs = {**overrides, **kwargs}
+        prompts = dict(bboxes=bboxes, points=points, labels=labels)
+        return super().predict(source, stream, prompts=prompts, **kwargs)
+
+    def __call__(self, source=None, stream: bool = False, bboxes=None, points=None, labels=None, **kwargs):
+        """
+        Perform segmentation prediction on the given image or video source.
+
+        This method is an alias for the 'predict' method, providing a convenient way to call the SAM model
+        for segmentation tasks.
+
+        Args:
+            source (str | PIL.Image | np.ndarray | None): Path to the image or video file, or a PIL.Image
+                object, or a np.ndarray object.
+            stream (bool): If True, enables real-time streaming.
+            bboxes (list[list[float]] | None): List of bounding box coordinates for prompted segmentation.
+            points (list[list[float]] | None): List of points for prompted segmentation.
+            labels (list[int] | None): List of labels for prompted segmentation.
+            **kwargs (Any): Additional keyword arguments to be passed to the predict method.
+
+        Returns:
+            (list): The model predictions, typically containing segmentation masks and other relevant information.
+
+        Examples:
+            >>> sam = SAM("sam_b.pt")
+            >>> results = sam("image.jpg", points=[[500, 375]])
+            >>> print(f"Detected {len(results[0].masks)} masks")
+        """
+        return self.predict(source, stream, bboxes, points, labels, **kwargs)
+
+    def info(self, detailed: bool = False, verbose: bool = True):
+        """
+        Log information about the SAM model.
+
+        Args:
+            detailed (bool): If True, displays detailed information about the model layers and operations.
+            verbose (bool): If True, prints the information to the console.
+
+        Returns:
+            (tuple): A tuple containing the model's information (string representations of the model).
+
+        Examples:
+            >>> sam = SAM("sam_b.pt")
+            >>> info = sam.info()
+            >>> print(info[0])  # Print summary information
+        """
+        return model_info(self.model, detailed=detailed, verbose=verbose)
+
+    @property
+    def task_map(self) -> dict[str, dict[str, type[Predictor]]]:
+        """
+        Provide a mapping from the 'segment' task to its corresponding 'Predictor'.
+
+        Returns:
+            (dict[str, dict[str, Type[Predictor]]]): A dictionary mapping the 'segment' task to its corresponding
+                Predictor class. For SAM2 models, it maps to SAM2Predictor, otherwise to the standard Predictor.
+
+        Examples:
+            >>> sam = SAM("sam_b.pt")
+            >>> task_map = sam.task_map
+            >>> print(task_map)
+            {'segment': {'predictor': <class 'ultralytics.models.sam.predict.Predictor'>}}
+        """
+        return {"segment": {"predictor": SAM2Predictor if self.is_sam2 else Predictor}}

ultralytics/models/sam/modules/__init__.py

@@ -0,0 +1 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

ultralytics/models/sam/modules/decoders.py

@@ -0,0 +1,513 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+import torch
+from torch import nn
+
+from ultralytics.nn.modules import MLP, LayerNorm2d
+
+
+class MaskDecoder(nn.Module):
+    """
+    Decoder module for generating masks and their associated quality scores using a transformer architecture.
+
+    This class predicts masks given image and prompt embeddings, utilizing a transformer to process the inputs and
+    generate mask predictions along with their quality scores.
+
+    Attributes:
+        transformer_dim (int): Channel dimension for the transformer module.
+        transformer (nn.Module): Transformer module used for mask prediction.
+        num_multimask_outputs (int): Number of masks to predict for disambiguating masks.
+        iou_token (nn.Embedding): Embedding for the IoU token.
+        num_mask_tokens (int): Number of mask tokens.
+        mask_tokens (nn.Embedding): Embedding for the mask tokens.
+        output_upscaling (nn.Sequential): Neural network sequence for upscaling the output.
+        output_hypernetworks_mlps (nn.ModuleList): Hypernetwork MLPs for generating masks.
+        iou_prediction_head (nn.Module): MLP for predicting mask quality.
+
+    Methods:
+        forward: Predict masks given image and prompt embeddings.
+        predict_masks: Internal method for mask prediction.
+
+    Examples:
+        >>> decoder = MaskDecoder(transformer_dim=256, transformer=transformer_module)
+        >>> masks, iou_pred = decoder(
+        ...     image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, multimask_output=True
+        ... )
+        >>> print(f"Predicted masks shape: {masks.shape}, IoU predictions shape: {iou_pred.shape}")
+    """
+
+    def __init__(
+        self,
+        transformer_dim: int,
+        transformer: nn.Module,
+        num_multimask_outputs: int = 3,
+        activation: type[nn.Module] = nn.GELU,
+        iou_head_depth: int = 3,
+        iou_head_hidden_dim: int = 256,
+    ) -> None:
+        """
+        Initialize the MaskDecoder module for generating masks and their associated quality scores.
+
+        Args:
+            transformer_dim (int): Channel dimension for the transformer module.
+            transformer (nn.Module): Transformer module used for mask prediction.
+            num_multimask_outputs (int): Number of masks to predict for disambiguating masks.
+            activation (Type[nn.Module]): Type of activation to use when upscaling masks.
+            iou_head_depth (int): Depth of the MLP used to predict mask quality.
+            iou_head_hidden_dim (int): Hidden dimension of the MLP used to predict mask quality.
+
+        Examples:
+            >>> transformer = nn.TransformerEncoder(nn.TransformerEncoderLayer(d_model=256, nhead=8), num_layers=6)
+            >>> decoder = MaskDecoder(transformer_dim=256, transformer=transformer)
+            >>> print(decoder)
+        """
+        super().__init__()
+        self.transformer_dim = transformer_dim
+        self.transformer = transformer
+
+        self.num_multimask_outputs = num_multimask_outputs
+
+        self.iou_token = nn.Embedding(1, transformer_dim)
+        self.num_mask_tokens = num_multimask_outputs + 1
+        self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
+
+        self.output_upscaling = nn.Sequential(
+            nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
+            LayerNorm2d(transformer_dim // 4),
+            activation(),
+            nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
+            activation(),
+        )
+        self.output_hypernetworks_mlps = nn.ModuleList(
+            [MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) for _ in range(self.num_mask_tokens)]
+        )
+
+        self.iou_prediction_head = MLP(transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth)
+
+    def forward(
+        self,
+        image_embeddings: torch.Tensor,
+        image_pe: torch.Tensor,
+        sparse_prompt_embeddings: torch.Tensor,
+        dense_prompt_embeddings: torch.Tensor,
+        multimask_output: bool,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Predict masks given image and prompt embeddings.
+
+        Args:
+            image_embeddings (torch.Tensor): Embeddings from the image encoder.
+            image_pe (torch.Tensor): Positional encoding with the shape of image_embeddings.
+            sparse_prompt_embeddings (torch.Tensor): Embeddings of the points and boxes.
+            dense_prompt_embeddings (torch.Tensor): Embeddings of the mask inputs.
+            multimask_output (bool): Whether to return multiple masks or a single mask.
+
+        Returns:
+            masks (torch.Tensor): Batched predicted masks.
+            iou_pred (torch.Tensor): Batched predictions of mask quality.
+
+        Examples:
+            >>> decoder = MaskDecoder(transformer_dim=256, transformer=transformer_module)
+            >>> image_emb = torch.rand(1, 256, 64, 64)
+            >>> image_pe = torch.rand(1, 256, 64, 64)
+            >>> sparse_emb = torch.rand(1, 2, 256)
+            >>> dense_emb = torch.rand(1, 256, 64, 64)
+            >>> masks, iou_pred = decoder(image_emb, image_pe, sparse_emb, dense_emb, multimask_output=True)
+            >>> print(f"Masks shape: {masks.shape}, IoU predictions shape: {iou_pred.shape}")
+        """
+        masks, iou_pred = self.predict_masks(
+            image_embeddings=image_embeddings,
+            image_pe=image_pe,
+            sparse_prompt_embeddings=sparse_prompt_embeddings,
+            dense_prompt_embeddings=dense_prompt_embeddings,
+        )
+
+        # Select the correct mask or masks for output
+        mask_slice = slice(1, None) if multimask_output else slice(0, 1)
+        masks = masks[:, mask_slice, :, :]
+        iou_pred = iou_pred[:, mask_slice]
+
+        return masks, iou_pred
+
+    def predict_masks(
+        self,
+        image_embeddings: torch.Tensor,
+        image_pe: torch.Tensor,
+        sparse_prompt_embeddings: torch.Tensor,
+        dense_prompt_embeddings: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Predict masks and quality scores using image and prompt embeddings via transformer architecture."""
+        # Concatenate output tokens
+        output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
+        output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.shape[0], -1, -1)
+        tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
+
+        # Expand per-image data in batch direction to be per-mask
+        src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
+        src = src + dense_prompt_embeddings
+        pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
+        b, c, h, w = src.shape
+
+        # Run the transformer
+        hs, src = self.transformer(src, pos_src, tokens)
+        iou_token_out = hs[:, 0, :]
+        mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
+
+        # Upscale mask embeddings and predict masks using the mask tokens
+        src = src.transpose(1, 2).view(b, c, h, w)
+        upscaled_embedding = self.output_upscaling(src)
+        hyper_in_list: list[torch.Tensor] = [
+            self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]) for i in range(self.num_mask_tokens)
+        ]
+        hyper_in = torch.stack(hyper_in_list, dim=1)
+        b, c, h, w = upscaled_embedding.shape
+        masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
+
+        # Generate mask quality predictions
+        iou_pred = self.iou_prediction_head(iou_token_out)
+
+        return masks, iou_pred
+
+
+class SAM2MaskDecoder(nn.Module):
+    """
+    Transformer-based decoder for predicting instance segmentation masks from image and prompt embeddings.
+
+    This class extends the functionality of the MaskDecoder, incorporating additional features such as
+    high-resolution feature processing, dynamic multimask output, and object score prediction.
+
+    Attributes:
+        transformer_dim (int): Channel dimension of the transformer.
+        transformer (nn.Module): Transformer used to predict masks.
+        num_multimask_outputs (int): Number of masks to predict when disambiguating masks.
+        iou_token (nn.Embedding): Embedding for IOU token.
+        num_mask_tokens (int): Total number of mask tokens.
+        mask_tokens (nn.Embedding): Embedding for mask tokens.
+        pred_obj_scores (bool): Whether to predict object scores.
+        obj_score_token (nn.Embedding): Embedding for object score token.
+        use_multimask_token_for_obj_ptr (bool): Whether to use multimask token for object pointer.
+        output_upscaling (nn.Sequential): Upscaling layers for output.
+        use_high_res_features (bool): Whether to use high-resolution features.
+        conv_s0 (nn.Conv2d): Convolutional layer for high-resolution features (s0).
+        conv_s1 (nn.Conv2d): Convolutional layer for high-resolution features (s1).
+        output_hypernetworks_mlps (nn.ModuleList): List of MLPs for output hypernetworks.
+        iou_prediction_head (MLP): MLP for IOU prediction.
+        pred_obj_score_head (nn.Linear | MLP): Linear layer or MLP for object score prediction.
+        dynamic_multimask_via_stability (bool): Whether to use dynamic multimask via stability.
+        dynamic_multimask_stability_delta (float): Delta value for dynamic multimask stability.
+        dynamic_multimask_stability_thresh (float): Threshold for dynamic multimask stability.
+
+    Methods:
+        forward: Predict masks given image and prompt embeddings.
+        predict_masks: Predict instance segmentation masks from image and prompt embeddings.
+        _get_stability_scores: Compute mask stability scores based on IoU between thresholds.
+        _dynamic_multimask_via_stability: Dynamically select the most stable mask output.
+
+    Examples:
+        >>> image_embeddings = torch.rand(1, 256, 64, 64)
+        >>> image_pe = torch.rand(1, 256, 64, 64)
+        >>> sparse_prompt_embeddings = torch.rand(1, 2, 256)
+        >>> dense_prompt_embeddings = torch.rand(1, 256, 64, 64)
+        >>> decoder = SAM2MaskDecoder(256, transformer)
+        >>> masks, iou_pred, sam_tokens_out, obj_score_logits = decoder.forward(
+        ...     image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, True, False
+        ... )
+    """
+
+    def __init__(
+        self,
+        transformer_dim: int,
+        transformer: nn.Module,
+        num_multimask_outputs: int = 3,
+        activation: type[nn.Module] = nn.GELU,
+        iou_head_depth: int = 3,
+        iou_head_hidden_dim: int = 256,
+        use_high_res_features: bool = False,
+        iou_prediction_use_sigmoid=False,
+        dynamic_multimask_via_stability=False,
+        dynamic_multimask_stability_delta=0.05,
+        dynamic_multimask_stability_thresh=0.98,
+        pred_obj_scores: bool = False,
+        pred_obj_scores_mlp: bool = False,
+        use_multimask_token_for_obj_ptr: bool = False,
+    ) -> None:
+        """
+        Initialize the SAM2MaskDecoder module for predicting instance segmentation masks.
+
+        This decoder extends the functionality of MaskDecoder, incorporating additional features such as
+        high-resolution feature processing, dynamic multimask output, and object score prediction.
+
+        Args:
+            transformer_dim (int): Channel dimension of the transformer.
+            transformer (nn.Module): Transformer used to predict masks.
+            num_multimask_outputs (int): Number of masks to predict when disambiguating masks.
+            activation (Type[nn.Module]): Type of activation to use when upscaling masks.
+            iou_head_depth (int): Depth of the MLP used to predict mask quality.
+            iou_head_hidden_dim (int): Hidden dimension of the MLP used to predict mask quality.
+            use_high_res_features (bool): Whether to use high-resolution features.
+            iou_prediction_use_sigmoid (bool): Whether to use sigmoid for IOU prediction.
+            dynamic_multimask_via_stability (bool): Whether to use dynamic multimask via stability.
+            dynamic_multimask_stability_delta (float): Delta value for dynamic multimask stability.
+            dynamic_multimask_stability_thresh (float): Threshold for dynamic multimask stability.
+            pred_obj_scores (bool): Whether to predict object scores.
+            pred_obj_scores_mlp (bool): Whether to use MLP for object score prediction.
+            use_multimask_token_for_obj_ptr (bool): Whether to use multimask token for object pointer.
+
+        Examples:
+            >>> transformer = nn.TransformerEncoder(nn.TransformerEncoderLayer(d_model=256, nhead=8), num_layers=6)
+            >>> decoder = SAM2MaskDecoder(transformer_dim=256, transformer=transformer)
+            >>> print(decoder)
+        """
+        super().__init__()
+        self.transformer_dim = transformer_dim
+        self.transformer = transformer
+
+        self.num_multimask_outputs = num_multimask_outputs
+
+        self.iou_token = nn.Embedding(1, transformer_dim)
+        self.num_mask_tokens = num_multimask_outputs + 1
+        self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
+
+        self.pred_obj_scores = pred_obj_scores
+        if self.pred_obj_scores:
+            self.obj_score_token = nn.Embedding(1, transformer_dim)
+        self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr
+
+        self.output_upscaling = nn.Sequential(
+            nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
+            LayerNorm2d(transformer_dim // 4),
+            activation(),
+            nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
+            activation(),
+        )
+        self.use_high_res_features = use_high_res_features
+        if use_high_res_features:
+            self.conv_s0 = nn.Conv2d(transformer_dim, transformer_dim // 8, kernel_size=1, stride=1)
+            self.conv_s1 = nn.Conv2d(transformer_dim, transformer_dim // 4, kernel_size=1, stride=1)
+
+        self.output_hypernetworks_mlps = nn.ModuleList(
+            [MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) for _ in range(self.num_mask_tokens)]
+        )
+
+        self.iou_prediction_head = MLP(
+            transformer_dim,
+            iou_head_hidden_dim,
+            self.num_mask_tokens,
+            iou_head_depth,
+            sigmoid=iou_prediction_use_sigmoid,
+        )
+        if self.pred_obj_scores:
+            self.pred_obj_score_head = nn.Linear(transformer_dim, 1)
+            if pred_obj_scores_mlp:
+                self.pred_obj_score_head = MLP(transformer_dim, transformer_dim, 1, 3)
+
+        # When outputting a single mask, optionally we can dynamically fall back to the best
+        # multimask output token if the single mask output token gives low stability scores.
+        self.dynamic_multimask_via_stability = dynamic_multimask_via_stability
+        self.dynamic_multimask_stability_delta = dynamic_multimask_stability_delta
+        self.dynamic_multimask_stability_thresh = dynamic_multimask_stability_thresh
+
+    def forward(
+        self,
+        image_embeddings: torch.Tensor,
+        image_pe: torch.Tensor,
+        sparse_prompt_embeddings: torch.Tensor,
+        dense_prompt_embeddings: torch.Tensor,
+        multimask_output: bool,
+        repeat_image: bool,
+        high_res_features: list[torch.Tensor] | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Predict masks given image and prompt embeddings.
+
+        Args:
+            image_embeddings (torch.Tensor): Embeddings from the image encoder with shape (B, C, H, W).
+            image_pe (torch.Tensor): Positional encoding with the shape of image_embeddings (B, C, H, W).
+            sparse_prompt_embeddings (torch.Tensor): Embeddings of the points and boxes with shape (B, N, C).
+            dense_prompt_embeddings (torch.Tensor): Embeddings of the mask inputs with shape (B, C, H, W).
+            multimask_output (bool): Whether to return multiple masks or a single mask.
+            repeat_image (bool): Flag to repeat the image embeddings.
+            high_res_features (list[torch.Tensor] | None, optional): Optional high-resolution features.
+
+        Returns:
+            masks (torch.Tensor): Batched predicted masks with shape (B, N, H, W).
+            iou_pred (torch.Tensor): Batched predictions of mask quality with shape (B, N).
+            sam_tokens_out (torch.Tensor): Batched SAM token for mask output with shape (B, N, C).
+            object_score_logits (torch.Tensor): Batched object score logits with shape (B, 1).
+
+        Examples:
+            >>> image_embeddings = torch.rand(1, 256, 64, 64)
+            >>> image_pe = torch.rand(1, 256, 64, 64)
+            >>> sparse_prompt_embeddings = torch.rand(1, 2, 256)
+            >>> dense_prompt_embeddings = torch.rand(1, 256, 64, 64)
+            >>> decoder = SAM2MaskDecoder(256, transformer)
+            >>> masks, iou_pred, sam_tokens_out, obj_score_logits = decoder.forward(
+            ...     image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, True, False
+            ... )
+        """
+        masks, iou_pred, mask_tokens_out, object_score_logits = self.predict_masks(
+            image_embeddings=image_embeddings,
+            image_pe=image_pe,
+            sparse_prompt_embeddings=sparse_prompt_embeddings,
+            dense_prompt_embeddings=dense_prompt_embeddings,
+            repeat_image=repeat_image,
+            high_res_features=high_res_features,
+        )
+
+        # Select the correct mask or masks for output
+        if multimask_output:
+            masks = masks[:, 1:, :, :]
+            iou_pred = iou_pred[:, 1:]
+        elif self.dynamic_multimask_via_stability and not self.training:
+            masks, iou_pred = self._dynamic_multimask_via_stability(masks, iou_pred)
+        else:
+            masks = masks[:, 0:1, :, :]
+            iou_pred = iou_pred[:, 0:1]
+
+        if multimask_output and self.use_multimask_token_for_obj_ptr:
+            sam_tokens_out = mask_tokens_out[:, 1:]  # [b, 3, c] shape
+        else:
+            # Take the mask output token. Here we *always* use the token for single mask output.
+            # At test time, even if we track after 1-click (and using multimask_output=True),
+            # we still take the single mask token here. The rationale is that we always track
+            # after multiple clicks during training, so the past tokens seen during training
+            # are always the single mask token (and we'll let it be the object-memory token).
+            sam_tokens_out = mask_tokens_out[:, 0:1]  # [b, 1, c] shape
+
+        return masks, iou_pred, sam_tokens_out, object_score_logits
+
+    def predict_masks(
+        self,
+        image_embeddings: torch.Tensor,
+        image_pe: torch.Tensor,
+        sparse_prompt_embeddings: torch.Tensor,
+        dense_prompt_embeddings: torch.Tensor,
+        repeat_image: bool,
+        high_res_features: list[torch.Tensor] | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Predict instance segmentation masks from image and prompt embeddings using a transformer."""
+        # Concatenate output tokens
+        s = 0
+        if self.pred_obj_scores:
+            output_tokens = torch.cat(
+                [
+                    self.obj_score_token.weight,
+                    self.iou_token.weight,
+                    self.mask_tokens.weight,
+                ],
+                dim=0,
+            )
+            s = 1
+        else:
+            output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
+        output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.shape[0], -1, -1)
+        tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
+
+        # Expand per-image data in batch direction to be per-mask
+        if repeat_image:
+            src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
+        else:
+            assert image_embeddings.shape[0] == tokens.shape[0]
+            src = image_embeddings
+        src = src + dense_prompt_embeddings
+        assert image_pe.shape[0] == 1, "image_pe should have size 1 in batch dim (from `get_dense_pe()`)"
+        pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
+        b, c, h, w = src.shape
+
+        # Run the transformer
+        hs, src = self.transformer(src, pos_src, tokens)
+        iou_token_out = hs[:, s, :]
+        mask_tokens_out = hs[:, s + 1 : (s + 1 + self.num_mask_tokens), :]
+
+        # Upscale mask embeddings and predict masks using the mask tokens
+        src = src.transpose(1, 2).view(b, c, h, w)
+        if not self.use_high_res_features or high_res_features is None:
+            upscaled_embedding = self.output_upscaling(src)
+        else:
+            dc1, ln1, act1, dc2, act2 = self.output_upscaling
+            feat_s0, feat_s1 = high_res_features
+            upscaled_embedding = act1(ln1(dc1(src) + feat_s1))
+            upscaled_embedding = act2(dc2(upscaled_embedding) + feat_s0)
+
+        hyper_in_list: list[torch.Tensor] = [
+            self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]) for i in range(self.num_mask_tokens)
+        ]
+        hyper_in = torch.stack(hyper_in_list, dim=1)
+        b, c, h, w = upscaled_embedding.shape
+        masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
+
+        # Generate mask quality predictions
+        iou_pred = self.iou_prediction_head(iou_token_out)
+        if self.pred_obj_scores:
+            assert s == 1
+            object_score_logits = self.pred_obj_score_head(hs[:, 0, :])
+        else:
+            # Obj scores logits - default to 10.0, i.e. assuming the object is present, sigmoid(10)=1
+            object_score_logits = 10.0 * iou_pred.new_ones(iou_pred.shape[0], 1)
+
+        return masks, iou_pred, mask_tokens_out, object_score_logits
+
+    def _get_stability_scores(self, mask_logits):
+        """Compute mask stability scores based on IoU between upper and lower thresholds."""
+        mask_logits = mask_logits.flatten(-2)
+        stability_delta = self.dynamic_multimask_stability_delta
+        area_i = torch.sum(mask_logits > stability_delta, dim=-1).float()
+        area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float()
+        return torch.where(area_u > 0, area_i / area_u, 1.0)
+
+    def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores):
+        """
+        Dynamically select the most stable mask output based on stability scores and IoU predictions.
+
+        This method is used when outputting a single mask. If the stability score from the current single-mask
+        output (based on output token 0) falls below a threshold, it instead selects from multi-mask outputs
+        (based on output tokens 1-3) the mask with the highest predicted IoU score. This ensures a valid mask
+        for both clicking and tracking scenarios.
+
+        Args:
+            all_mask_logits (torch.Tensor): Logits for all predicted masks, shape (B, N, H, W) where B is
+                batch size, N is number of masks (typically 4), and H, W are mask dimensions.
+            all_iou_scores (torch.Tensor): Predicted IoU scores for all masks, shape (B, N).
+
+        Returns:
+            mask_logits_out (torch.Tensor): Selected mask logits, shape (B, 1, H, W).
+            iou_scores_out (torch.Tensor): Selected IoU scores, shape (B, 1).
+
+        Examples:
+            >>> decoder = SAM2MaskDecoder(...)
+            >>> all_mask_logits = torch.rand(2, 4, 256, 256)  # 2 images, 4 masks each
+            >>> all_iou_scores = torch.rand(2, 4)
+            >>> mask_logits, iou_scores = decoder._dynamic_multimask_via_stability(all_mask_logits, all_iou_scores)
+            >>> print(mask_logits.shape, iou_scores.shape)
+            torch.Size([2, 1, 256, 256]) torch.Size([2, 1])
+        """
+        # The best mask from multimask output tokens (1~3)
+        multimask_logits = all_mask_logits[:, 1:, :, :]
+        multimask_iou_scores = all_iou_scores[:, 1:]
+        best_scores_inds = torch.argmax(multimask_iou_scores, dim=-1)
+        batch_inds = torch.arange(multimask_iou_scores.shape[0], device=all_iou_scores.device)
+        best_multimask_logits = multimask_logits[batch_inds, best_scores_inds]
+        best_multimask_logits = best_multimask_logits.unsqueeze(1)
+        best_multimask_iou_scores = multimask_iou_scores[batch_inds, best_scores_inds]
+        best_multimask_iou_scores = best_multimask_iou_scores.unsqueeze(1)
+
+        # The mask from singlemask output token 0 and its stability score
+        singlemask_logits = all_mask_logits[:, 0:1, :, :]
+        singlemask_iou_scores = all_iou_scores[:, 0:1]
+        stability_scores = self._get_stability_scores(singlemask_logits)
+        is_stable = stability_scores >= self.dynamic_multimask_stability_thresh
+
+        # Dynamically fall back to best multimask output upon low stability scores.
+        mask_logits_out = torch.where(
+            is_stable[..., None, None].expand_as(singlemask_logits),
+            singlemask_logits,
+            best_multimask_logits,
+        )
+        iou_scores_out = torch.where(
+            is_stable.expand_as(singlemask_iou_scores),
+            singlemask_iou_scores,
+            best_multimask_iou_scores,
+        )
+        return mask_logits_out, iou_scores_out

ultralytics/models/sam/modules/encoders.py

@@ -0,0 +1,851 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ultralytics.nn.modules import LayerNorm2d
+
+from .blocks import (
+    Block,
+    CXBlock,
+    Fuser,
+    MaskDownSampler,
+    MultiScaleBlock,
+    PatchEmbed,
+    PositionEmbeddingRandom,
+    PositionEmbeddingSine,
+)
+
+
+class ImageEncoderViT(nn.Module):
+    """
+    An image encoder using Vision Transformer (ViT) architecture for encoding images into a compact latent space.
+
+    This class processes images by splitting them into patches, applying transformer blocks, and generating a final
+    encoded representation through a neck module.
+
+    Attributes:
+        img_size (int): Dimension of input images, assumed to be square.
+        patch_embed (PatchEmbed): Module for patch embedding.
+        pos_embed (nn.Parameter | None): Absolute positional embedding for patches.
+        blocks (nn.ModuleList): List of transformer blocks for processing patch embeddings.
+        neck (nn.Sequential): Neck module to further process the output.
+
+    Methods:
+        forward: Process input through patch embedding, positional embedding, blocks, and neck.
+
+    Examples:
+        >>> import torch
+        >>> encoder = ImageEncoderViT(img_size=224, patch_size=16, embed_dim=768, depth=12, num_heads=12)
+        >>> input_image = torch.randn(1, 3, 224, 224)
+        >>> output = encoder(input_image)
+        >>> print(output.shape)
+    """
+
+    def __init__(
+        self,
+        img_size: int = 1024,
+        patch_size: int = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        out_chans: int = 256,
+        qkv_bias: bool = True,
+        norm_layer: type[nn.Module] = nn.LayerNorm,
+        act_layer: type[nn.Module] = nn.GELU,
+        use_abs_pos: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        global_attn_indexes: tuple[int, ...] = (),
+    ) -> None:
+        """
+        Initialize an ImageEncoderViT instance for encoding images using Vision Transformer architecture.
+
+        Args:
+            img_size (int): Input image size, assumed to be square.
+            patch_size (int): Size of image patches.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Dimension of patch embeddings.
+            depth (int): Number of transformer blocks.
+            num_heads (int): Number of attention heads in each block.
+            mlp_ratio (float): Ratio of MLP hidden dimension to embedding dimension.
+            out_chans (int): Number of output channels from the neck module.
+            qkv_bias (bool): If True, adds learnable bias to query, key, value projections.
+            norm_layer (Type[nn.Module]): Type of normalization layer to use.
+            act_layer (Type[nn.Module]): Type of activation layer to use.
+            use_abs_pos (bool): If True, uses absolute positional embeddings.
+            use_rel_pos (bool): If True, adds relative positional embeddings to attention maps.
+            rel_pos_zero_init (bool): If True, initializes relative positional parameters to zero.
+            window_size (int): Size of attention window for windowed attention blocks.
+            global_attn_indexes (tuple[int, ...]): Indices of blocks that use global attention.
+
+        Examples:
+            >>> encoder = ImageEncoderViT(img_size=224, patch_size=16, embed_dim=768, depth=12, num_heads=12)
+            >>> input_image = torch.randn(1, 3, 224, 224)
+            >>> output = encoder(input_image)
+            >>> print(output.shape)
+        """
+        super().__init__()
+        self.img_size = img_size
+
+        self.patch_embed = PatchEmbed(
+            kernel_size=(patch_size, patch_size),
+            stride=(patch_size, patch_size),
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+        )
+
+        self.pos_embed: nn.Parameter | None = None
+        if use_abs_pos:
+            # Initialize absolute positional embedding with pretrain image size
+            self.pos_embed = nn.Parameter(torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim))
+
+        self.blocks = nn.ModuleList()
+        for i in range(depth):
+            block = Block(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                use_rel_pos=use_rel_pos,
+                rel_pos_zero_init=rel_pos_zero_init,
+                window_size=window_size if i not in global_attn_indexes else 0,
+                input_size=(img_size // patch_size, img_size // patch_size),
+            )
+            self.blocks.append(block)
+
+        self.neck = nn.Sequential(
+            nn.Conv2d(
+                embed_dim,
+                out_chans,
+                kernel_size=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+            nn.Conv2d(
+                out_chans,
+                out_chans,
+                kernel_size=3,
+                padding=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Process input through patch embedding, positional embedding, transformer blocks, and neck module."""
+        x = self.patch_embed(x)
+        if self.pos_embed is not None:
+            pos_embed = (
+                F.interpolate(self.pos_embed.permute(0, 3, 1, 2), scale_factor=self.img_size / 1024).permute(0, 2, 3, 1)
+                if self.img_size != 1024
+                else self.pos_embed
+            )
+            x = x + pos_embed
+        for blk in self.blocks:
+            x = blk(x)
+        return self.neck(x.permute(0, 3, 1, 2))
+
+
+class PromptEncoder(nn.Module):
+    """
+    Encode different types of prompts for input to SAM's mask decoder, producing sparse and dense embeddings.
+
+    Attributes:
+        embed_dim (int): Dimension of the embeddings.
+        input_image_size (tuple[int, int]): Size of the input image as (H, W).
+        image_embedding_size (tuple[int, int]): Spatial size of the image embedding as (H, W).
+        pe_layer (PositionEmbeddingRandom): Module for random position embedding.
+        num_point_embeddings (int): Number of point embeddings for different types of points.
+        point_embeddings (nn.ModuleList): List of point embeddings.
+        not_a_point_embed (nn.Embedding): Embedding for points that are not part of any label.
+        mask_input_size (tuple[int, int]): Size of the input mask.
+        mask_downscaling (nn.Sequential): Neural network for downscaling the mask.
+        no_mask_embed (nn.Embedding): Embedding for cases where no mask is provided.
+
+    Methods:
+        get_dense_pe: Return the positional encoding used to encode point prompts.
+        forward: Embed different types of prompts, returning both sparse and dense embeddings.
+
+    Examples:
+        >>> prompt_encoder = PromptEncoder(256, (64, 64), (1024, 1024), 16)
+        >>> points = (torch.rand(1, 5, 2), torch.randint(0, 4, (1, 5)))
+        >>> boxes = torch.rand(1, 2, 2)
+        >>> masks = torch.rand(1, 1, 256, 256)
+        >>> sparse_embeddings, dense_embeddings = prompt_encoder(points, boxes, masks)
+        >>> print(sparse_embeddings.shape, dense_embeddings.shape)
+        torch.Size([1, 7, 256]) torch.Size([1, 256, 64, 64])
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        image_embedding_size: tuple[int, int],
+        input_image_size: tuple[int, int],
+        mask_in_chans: int,
+        activation: type[nn.Module] = nn.GELU,
+    ) -> None:
+        """
+        Initialize the PromptEncoder module for encoding various types of prompts.
+
+        Args:
+            embed_dim (int): The dimension of the embeddings.
+            image_embedding_size (tuple[int, int]): The spatial size of the image embedding as (H, W).
+            input_image_size (tuple[int, int]): The padded size of the input image as (H, W).
+            mask_in_chans (int): The number of hidden channels used for encoding input masks.
+            activation (Type[nn.Module]): The activation function to use when encoding input masks.
+
+        Examples:
+            >>> prompt_encoder = PromptEncoder(256, (64, 64), (1024, 1024), 16)
+            >>> points = (torch.rand(1, 5, 2), torch.randint(0, 4, (1, 5)))
+            >>> boxes = torch.rand(1, 2, 2)
+            >>> masks = torch.rand(1, 1, 256, 256)
+            >>> sparse_embeddings, dense_embeddings = prompt_encoder(points, boxes, masks)
+            >>> print(sparse_embeddings.shape, dense_embeddings.shape)
+            torch.Size([1, 7, 256]) torch.Size([1, 256, 64, 64])
+        """
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.input_image_size = input_image_size
+        self.image_embedding_size = image_embedding_size
+        self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
+
+        self.num_point_embeddings: int = 4  # pos/neg point + 2 box corners
+        point_embeddings = [nn.Embedding(1, embed_dim) for _ in range(self.num_point_embeddings)]
+        self.point_embeddings = nn.ModuleList(point_embeddings)
+        self.not_a_point_embed = nn.Embedding(1, embed_dim)
+
+        self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
+        self.mask_downscaling = nn.Sequential(
+            nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
+            LayerNorm2d(mask_in_chans // 4),
+            activation(),
+            nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
+            LayerNorm2d(mask_in_chans),
+            activation(),
+            nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
+        )
+        self.no_mask_embed = nn.Embedding(1, embed_dim)
+
+    def get_dense_pe(self) -> torch.Tensor:
+        """
+        Return the dense positional encoding used for encoding point prompts.
+
+        Generate a positional encoding for a dense set of points matching the shape of the image
+        encoding. The encoding is used to provide spatial information to the model when processing point prompts.
+
+        Returns:
+            (torch.Tensor): Positional encoding tensor with shape (1, embed_dim, H, W), where H and W are the
+                height and width of the image embedding size, respectively.
+
+        Examples:
+            >>> prompt_encoder = PromptEncoder(256, (64, 64), (1024, 1024), 16)
+            >>> dense_pe = prompt_encoder.get_dense_pe()
+            >>> print(dense_pe.shape)
+            torch.Size([1, 256, 64, 64])
+        """
+        return self.pe_layer(self.image_embedding_size).unsqueeze(0)
+
+    def _embed_points(self, points: torch.Tensor, labels: torch.Tensor, pad: bool) -> torch.Tensor:
+        """Embed point prompts by applying positional encoding and label-specific embeddings."""
+        points = points + 0.5  # Shift to center of pixel
+        if pad:
+            padding_point = torch.zeros((points.shape[0], 1, 2), dtype=points.dtype, device=points.device)
+            padding_label = -torch.ones((labels.shape[0], 1), dtype=labels.dtype, device=labels.device)
+            points = torch.cat([points, padding_point], dim=1)
+            labels = torch.cat([labels, padding_label], dim=1)
+        point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
+        point_embedding[labels == -1] = 0.0
+        point_embedding[labels == -1] += self.not_a_point_embed.weight
+        point_embedding[labels == 0] += self.point_embeddings[0].weight
+        point_embedding[labels == 1] += self.point_embeddings[1].weight
+        point_embedding[labels == 2] += self.point_embeddings[2].weight
+        point_embedding[labels == 3] += self.point_embeddings[3].weight
+        return point_embedding
+
+    def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
+        """Embed box prompts by applying positional encoding and adding corner embeddings."""
+        boxes = boxes + 0.5  # Shift to center of pixel
+        coords = boxes.reshape(-1, 2, 2)
+        corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
+        corner_embedding[:, 0, :] += self.point_embeddings[2].weight
+        corner_embedding[:, 1, :] += self.point_embeddings[3].weight
+        return corner_embedding
+
+    def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
+        """Embed mask inputs by downscaling and processing through convolutional layers."""
+        return self.mask_downscaling(masks)
+
+    @staticmethod
+    def _get_batch_size(
+        points: tuple[torch.Tensor, torch.Tensor] | None,
+        boxes: torch.Tensor | None,
+        masks: torch.Tensor | None,
+    ) -> int:
+        """Get the batch size of the output given the batch size of the input prompts."""
+        if points is not None:
+            return points[0].shape[0]
+        elif boxes is not None:
+            return boxes.shape[0]
+        elif masks is not None:
+            return masks.shape[0]
+        else:
+            return 1
+
+    def forward(
+        self,
+        points: tuple[torch.Tensor, torch.Tensor] | None,
+        boxes: torch.Tensor | None,
+        masks: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Embed different types of prompts, returning both sparse and dense embeddings.
+
+        Args:
+            points (tuple[torch.Tensor, torch.Tensor] | None): Point coordinates and labels to embed. The first
+                tensor contains coordinates with shape (B, N, 2), and the second tensor contains labels with
+                shape (B, N).
+            boxes (torch.Tensor | None): Boxes to embed with shape (B, M, 2, 2), where M is the number of boxes.
+            masks (torch.Tensor | None): Masks to embed with shape (B, 1, H, W).
+
+        Returns:
+            sparse_embeddings (torch.Tensor): Sparse embeddings for points and boxes with shape (B, N, embed_dim).
+            dense_embeddings (torch.Tensor): Dense embeddings for masks of shape (B, embed_dim, embed_H, embed_W).
+
+        Examples:
+            >>> encoder = PromptEncoder(256, (64, 64), (1024, 1024), 16)
+            >>> points = (torch.rand(1, 5, 2), torch.randint(0, 4, (1, 5)))
+            >>> boxes = torch.rand(1, 2, 2, 2)
+            >>> masks = torch.rand(1, 1, 256, 256)
+            >>> sparse_emb, dense_emb = encoder(points, boxes, masks)
+            >>> print(sparse_emb.shape, dense_emb.shape)
+            torch.Size([1, 7, 256]) torch.Size([1, 256, 64, 64])
+        """
+        bs = self._get_batch_size(points, boxes, masks)
+        sparse_embeddings = torch.empty(
+            (bs, 0, self.embed_dim),
+            dtype=self.point_embeddings[0].weight.dtype,
+            device=self.point_embeddings[0].weight.device,
+        )
+        if points is not None:
+            coords, labels = points
+            point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
+            sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
+        if boxes is not None:
+            box_embeddings = self._embed_boxes(boxes)
+            sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
+
+        if masks is not None:
+            dense_embeddings = self._embed_masks(masks)
+        else:
+            dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
+                bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
+            )
+
+        return sparse_embeddings, dense_embeddings
+
+
+class MemoryEncoder(nn.Module):
+    """
+    Encode pixel features and masks into a memory representation for efficient image segmentation.
+
+    This class processes pixel-level features and masks, fusing them to generate encoded memory representations
+    suitable for downstream tasks in image segmentation models like SAM (Segment Anything Model).
+
+    Attributes:
+        mask_downsampler (MaskDownSampler): Module for downsampling input masks.
+        pix_feat_proj (nn.Conv2d): Convolutional layer for projecting pixel features.
+        fuser (Fuser): Module for fusing pixel features and masks.
+        position_encoding (PositionEmbeddingSine): Module for adding positional encoding to features.
+        out_proj (nn.Module): Output projection layer, either nn.Identity or nn.Conv2d.
+
+    Methods:
+        forward: Process input pixel features and masks to generate encoded memory representations.
+
+    Examples:
+        >>> import torch
+        >>> encoder = MemoryEncoder(out_dim=256, in_dim=256)
+        >>> pix_feat = torch.randn(1, 256, 64, 64)
+        >>> masks = torch.randn(1, 1, 64, 64)
+        >>> encoded_feat, pos = encoder(pix_feat, masks)
+        >>> print(encoded_feat.shape, pos.shape)
+        torch.Size([1, 256, 64, 64]) torch.Size([1, 128, 64, 64])
+    """
+
+    def __init__(
+        self,
+        out_dim,
+        in_dim=256,  # in_dim of pix_feats
+    ):
+        """
+        Initialize the MemoryEncoder for encoding pixel features and masks into memory representations.
+
+        This encoder processes pixel-level features and masks, fusing them to generate encoded memory representations
+        suitable for downstream tasks in image segmentation models like SAM (Segment Anything Model).
+
+        Args:
+            out_dim (int): Output dimension of the encoded features.
+            in_dim (int): Input dimension of the pixel features.
+
+        Examples:
+            >>> encoder = MemoryEncoder(out_dim=256, in_dim=256)
+            >>> pix_feat = torch.randn(1, 256, 64, 64)
+            >>> masks = torch.randn(1, 1, 64, 64)
+            >>> encoded_feat, pos = encoder(pix_feat, masks)
+            >>> print(encoded_feat.shape, pos.shape)
+            torch.Size([1, 256, 64, 64]) torch.Size([1, 128, 64, 64])
+        """
+        super().__init__()
+
+        self.mask_downsampler = MaskDownSampler(kernel_size=3, stride=2, padding=1)
+
+        self.pix_feat_proj = nn.Conv2d(in_dim, in_dim, kernel_size=1)
+        self.fuser = Fuser(CXBlock(dim=256), num_layers=2)
+        self.position_encoding = PositionEmbeddingSine(num_pos_feats=64)
+        self.out_proj = nn.Identity()
+        if out_dim != in_dim:
+            self.out_proj = nn.Conv2d(in_dim, out_dim, kernel_size=1)
+
+    def forward(
+        self,
+        pix_feat: torch.Tensor,
+        masks: torch.Tensor,
+        skip_mask_sigmoid: bool = False,
+    ) -> dict:
+        """Process pixel features and masks to generate encoded memory representations for segmentation."""
+        if not skip_mask_sigmoid:
+            masks = F.sigmoid(masks)
+        masks = self.mask_downsampler(masks)
+
+        # Fuse pix_feats and downsampled masks, in case the visual features are on CPU, cast them to CUDA
+        pix_feat = pix_feat.to(masks.device)
+
+        x = self.pix_feat_proj(pix_feat)
+        x = x + masks
+        x = self.fuser(x)
+        x = self.out_proj(x)
+
+        pos = self.position_encoding(x).to(x.dtype)
+
+        return {"vision_features": x, "vision_pos_enc": [pos]}
+
+
+class ImageEncoder(nn.Module):
+    """
+    Encode images using a trunk-neck architecture, producing multiscale features and positional encodings.
+
+    This class combines a trunk network for feature extraction with a neck network for feature refinement
+    and positional encoding generation. It can optionally discard the lowest resolution features.
+
+    Attributes:
+        trunk (nn.Module): The trunk network for initial feature extraction.
+        neck (nn.Module): The neck network for feature refinement and positional encoding generation.
+        scalp (int): Number of lowest resolution feature levels to discard.
+
+    Methods:
+        forward: Process the input image through the trunk and neck networks.
+
+    Examples:
+        >>> trunk = SomeTrunkNetwork()
+        >>> neck = SomeNeckNetwork()
+        >>> encoder = ImageEncoder(trunk, neck, scalp=1)
+        >>> image = torch.randn(1, 3, 224, 224)
+        >>> output = encoder(image)
+        >>> print(output.keys())
+        dict_keys(['vision_features', 'vision_pos_enc', 'backbone_fpn'])
+    """
+
+    def __init__(
+        self,
+        trunk: nn.Module,
+        neck: nn.Module,
+        scalp: int = 0,
+    ):
+        """
+        Initialize the ImageEncoder with trunk and neck networks for feature extraction and refinement.
+
+        This encoder combines a trunk network for feature extraction with a neck network for feature refinement
+        and positional encoding generation. It can optionally discard the lowest resolution features.
+
+        Args:
+            trunk (nn.Module): The trunk network for initial feature extraction.
+            neck (nn.Module): The neck network for feature refinement and positional encoding generation.
+            scalp (int): Number of lowest resolution feature levels to discard.
+
+        Examples:
+            >>> trunk = SomeTrunkNetwork()
+            >>> neck = SomeNeckNetwork()
+            >>> encoder = ImageEncoder(trunk, neck, scalp=1)
+            >>> image = torch.randn(1, 3, 224, 224)
+            >>> output = encoder(image)
+            >>> print(output.keys())
+            dict_keys(['vision_features', 'vision_pos_enc', 'backbone_fpn'])
+        """
+        super().__init__()
+        self.trunk = trunk
+        self.neck = neck
+        self.scalp = scalp
+        assert self.trunk.channel_list == self.neck.backbone_channel_list, (
+            f"Channel dims of trunk {self.trunk.channel_list} and neck {self.neck.backbone_channel_list} do not match."
+        )
+
+    def forward(self, sample: torch.Tensor):
+        """Encode input through trunk and neck networks, returning multiscale features and positional encodings."""
+        features, pos = self.neck(self.trunk(sample))
+        if self.scalp > 0:
+            # Discard the lowest resolution features
+            features, pos = features[: -self.scalp], pos[: -self.scalp]
+
+        src = features[-1]
+        return {
+            "vision_features": src,
+            "vision_pos_enc": pos,
+            "backbone_fpn": features,
+        }
+
+
+class FpnNeck(nn.Module):
+    """
+    A Feature Pyramid Network (FPN) neck variant for multiscale feature fusion in object detection models.
+
+    This FPN variant removes the output convolution and uses bicubic interpolation for feature resizing,
+    similar to ViT positional embedding interpolation.
+
+    Attributes:
+        position_encoding (PositionEmbeddingSine): Sinusoidal positional encoding module.
+        convs (nn.ModuleList): List of convolutional layers for each backbone level.
+        backbone_channel_list (list[int]): List of channel dimensions from the backbone.
+        fpn_interp_model (str): Interpolation mode for FPN feature resizing.
+        fuse_type (str): Type of feature fusion, either 'sum' or 'avg'.
+        fpn_top_down_levels (list[int]): Levels to have top-down features in outputs.
+
+    Methods:
+        forward: Perform forward pass through the FPN neck.
+
+    Examples:
+        >>> backbone_channels = [64, 128, 256, 512]
+        >>> fpn_neck = FpnNeck(256, backbone_channels)
+        >>> inputs = [torch.rand(1, c, 32, 32) for c in backbone_channels]
+        >>> outputs, positions = fpn_neck(inputs)
+        >>> print(len(outputs), len(positions))
+        4 4
+    """
+
+    def __init__(
+        self,
+        d_model: int,
+        backbone_channel_list: list[int],
+        kernel_size: int = 1,
+        stride: int = 1,
+        padding: int = 0,
+        fpn_interp_model: str = "bilinear",
+        fuse_type: str = "sum",
+        fpn_top_down_levels: list[int] | None = None,
+    ):
+        """
+        Initialize a modified Feature Pyramid Network (FPN) neck.
+
+        This FPN variant removes the output convolution and uses bicubic interpolation for feature resizing,
+        similar to ViT positional embedding interpolation.
+
+        Args:
+            d_model (int): Dimension of the model.
+            backbone_channel_list (list[int]): List of channel dimensions from the backbone.
+            kernel_size (int): Kernel size for the convolutional layers.
+            stride (int): Stride for the convolutional layers.
+            padding (int): Padding for the convolutional layers.
+            fpn_interp_model (str): Interpolation mode for FPN feature resizing.
+            fuse_type (str): Type of feature fusion, either 'sum' or 'avg'.
+            fpn_top_down_levels (Optional[list[int]]): Levels to have top-down features in outputs.
+
+        Examples:
+            >>> backbone_channels = [64, 128, 256, 512]
+            >>> fpn_neck = FpnNeck(256, backbone_channels)
+            >>> print(fpn_neck)
+        """
+        super().__init__()
+        self.position_encoding = PositionEmbeddingSine(num_pos_feats=256)
+        self.convs = nn.ModuleList()
+        self.backbone_channel_list = backbone_channel_list
+        for dim in backbone_channel_list:
+            current = nn.Sequential()
+            current.add_module(
+                "conv",
+                nn.Conv2d(
+                    in_channels=dim,
+                    out_channels=d_model,
+                    kernel_size=kernel_size,
+                    stride=stride,
+                    padding=padding,
+                ),
+            )
+
+            self.convs.append(current)
+        self.fpn_interp_model = fpn_interp_model
+        assert fuse_type in {"sum", "avg"}
+        self.fuse_type = fuse_type
+
+        # Levels to have top-down features in its outputs
+        # e.g. if fpn_top_down_levels is [2, 3], then only outputs of level 2 and 3
+        # have top-down propagation, while outputs of level 0 and level 1 have only
+        # lateral features from the same backbone level
+        if fpn_top_down_levels is None:
+            # Default is to have top-down features on all levels
+            fpn_top_down_levels = range(len(self.convs))
+        self.fpn_top_down_levels = list(fpn_top_down_levels)
+
+    def forward(self, xs: list[torch.Tensor]):
+        """
+        Perform forward pass through the Feature Pyramid Network (FPN) neck.
+
+        This method processes a list of input tensors from the backbone through the FPN, applying lateral connections
+        and top-down feature fusion. It generates output feature maps and corresponding positional encodings.
+
+        Args:
+            xs (list[torch.Tensor]): List of input tensors from the backbone, each with shape (B, C, H, W).
+
+        Returns:
+            out (list[torch.Tensor]): List of output feature maps after FPN processing, each with shape
+                (B, d_model, H, W).
+            pos (list[torch.Tensor]): List of positional encodings corresponding to each output feature map.
+
+        Examples:
+            >>> fpn_neck = FpnNeck(d_model=256, backbone_channel_list=[64, 128, 256, 512])
+            >>> inputs = [torch.rand(1, c, 32, 32) for c in [64, 128, 256, 512]]
+            >>> outputs, positions = fpn_neck(inputs)
+            >>> print(len(outputs), len(positions))
+            4 4
+        """
+        out = [None] * len(self.convs)
+        pos = [None] * len(self.convs)
+        assert len(xs) == len(self.convs)
+        # FPN forward pass
+        # see https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/fpn.py
+        prev_features = None
+        # Forward in top-down order (from low to high resolution)
+        n = len(self.convs) - 1
+        for i in range(n, -1, -1):
+            x = xs[i]
+            lateral_features = self.convs[n - i](x)
+            if i in self.fpn_top_down_levels and prev_features is not None:
+                top_down_features = F.interpolate(
+                    prev_features.to(dtype=x.dtype),
+                    scale_factor=2.0,
+                    mode=self.fpn_interp_model,
+                    align_corners=(None if self.fpn_interp_model == "nearest" else False),
+                    antialias=False,
+                )
+                prev_features = lateral_features + top_down_features
+                if self.fuse_type == "avg":
+                    prev_features /= 2
+            else:
+                prev_features = lateral_features
+            x_out = prev_features
+            out[i] = x_out
+            pos[i] = self.position_encoding(x_out).to(x_out.dtype)
+
+        return out, pos
+
+
+class Hiera(nn.Module):
+    """
+    Hierarchical vision transformer for efficient multiscale feature extraction in image processing tasks.
+
+    This class implements a Hiera model, which is a hierarchical vision transformer architecture designed for
+    efficient multiscale feature extraction. It uses a series of transformer blocks organized into stages,
+    with optional pooling and global attention mechanisms.
+
+    Attributes:
+        window_spec (tuple[int, ...]): Window sizes for each stage.
+        q_stride (tuple[int, int]): Downsampling stride between stages.
+        stage_ends (list[int]): Indices of the last block in each stage.
+        q_pool_blocks (list[int]): Indices of blocks where pooling is applied.
+        return_interm_layers (bool): Whether to return intermediate layer outputs.
+        patch_embed (PatchEmbed): Module for patch embedding.
+        global_att_blocks (tuple[int, ...]): Indices of blocks with global attention.
+        window_pos_embed_bkg_spatial_size (tuple[int, int]): Spatial size for window positional embedding background.
+        pos_embed (nn.Parameter): Positional embedding for the background.
+        pos_embed_window (nn.Parameter): Positional embedding for the window.
+        blocks (nn.ModuleList): List of MultiScaleBlock modules.
+        channel_list (list[int]): List of output channel dimensions for each stage.
+
+    Methods:
+        _get_pos_embed: Generate positional embeddings by interpolating and combining window and background embeddings.
+        forward: Perform the forward pass through the Hiera model.
+
+    Examples:
+        >>> model = Hiera(embed_dim=96, num_heads=1, stages=(2, 3, 16, 3))
+        >>> input_tensor = torch.randn(1, 3, 224, 224)
+        >>> output_features = model(input_tensor)
+        >>> for feat in output_features:
+        ...     print(feat.shape)
+    """
+
+    def __init__(
+        self,
+        embed_dim: int = 96,  # initial embed dim
+        num_heads: int = 1,  # initial number of heads
+        drop_path_rate: float = 0.0,  # stochastic depth
+        q_pool: int = 3,  # number of q_pool stages
+        q_stride: tuple[int, int] = (2, 2),  # downsample stride bet. stages
+        stages: tuple[int, ...] = (2, 3, 16, 3),  # blocks per stage
+        dim_mul: float = 2.0,  # dim_mul factor at stage shift
+        head_mul: float = 2.0,  # head_mul factor at stage shift
+        window_pos_embed_bkg_spatial_size: tuple[int, int] = (14, 14),
+        # window size per stage, when not using global att.
+        window_spec: tuple[int, ...] = (
+            8,
+            4,
+            14,
+            7,
+        ),
+        # global attn in these blocks
+        global_att_blocks: tuple[int, ...] = (
+            12,
+            16,
+            20,
+        ),
+        return_interm_layers=True,  # return feats from every stage
+    ):
+        """
+        Initialize a Hiera model, a hierarchical vision transformer for efficient multiscale feature extraction.
+
+        Hiera is a hierarchical vision transformer architecture designed for efficient multiscale feature extraction
+        in image processing tasks. It uses a series of transformer blocks organized into stages, with optional
+        pooling and global attention mechanisms.
+
+        Args:
+            embed_dim (int): Initial embedding dimension for the model.
+            num_heads (int): Initial number of attention heads.
+            drop_path_rate (float): Stochastic depth rate.
+            q_pool (int): Number of query pooling stages.
+            q_stride (tuple[int, int]): Downsampling stride between stages.
+            stages (tuple[int, ...]): Number of blocks per stage.
+            dim_mul (float): Dimension multiplier factor at stage transitions.
+            head_mul (float): Head multiplier factor at stage transitions.
+            window_pos_embed_bkg_spatial_size (tuple[int, int]): Spatial size for window positional embedding background.
+            window_spec (tuple[int, ...]): Window sizes for each stage when not using global attention.
+            global_att_blocks (tuple[int, ...]): Indices of blocks that use global attention.
+            return_interm_layers (bool): Whether to return intermediate layer outputs.
+
+        Examples:
+            >>> model = Hiera(embed_dim=96, num_heads=1, stages=(2, 3, 16, 3))
+            >>> input_tensor = torch.randn(1, 3, 224, 224)
+            >>> output_features = model(input_tensor)
+            >>> for feat in output_features:
+            ...     print(feat.shape)
+        """
+        super().__init__()
+
+        assert len(stages) == len(window_spec)
+        self.window_spec = window_spec
+
+        depth = sum(stages)
+        self.q_stride = q_stride
+        self.stage_ends = [sum(stages[:i]) - 1 for i in range(1, len(stages) + 1)]
+        assert 0 <= q_pool <= len(self.stage_ends[:-1])
+        self.q_pool_blocks = [x + 1 for x in self.stage_ends[:-1]][:q_pool]
+        self.return_interm_layers = return_interm_layers
+
+        self.patch_embed = PatchEmbed(
+            embed_dim=embed_dim,
+            kernel_size=(7, 7),
+            stride=(4, 4),
+            padding=(3, 3),
+        )
+        # Which blocks have global attention?
+        self.global_att_blocks = global_att_blocks
+
+        # Windowed positional embedding (https://arxiv.org/abs/2311.05613)
+        self.window_pos_embed_bkg_spatial_size = window_pos_embed_bkg_spatial_size
+        self.pos_embed = nn.Parameter(torch.zeros(1, embed_dim, *self.window_pos_embed_bkg_spatial_size))
+        self.pos_embed_window = nn.Parameter(torch.zeros(1, embed_dim, self.window_spec[0], self.window_spec[0]))
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+
+        cur_stage = 1
+        self.blocks = nn.ModuleList()
+
+        for i in range(depth):
+            dim_out = embed_dim
+            # Lags by a block, so first block of next stage uses an initial window size
+            # of previous stage and final window size of current stage
+            window_size = self.window_spec[cur_stage - 1]
+
+            if self.global_att_blocks is not None:
+                window_size = 0 if i in self.global_att_blocks else window_size
+
+            if i - 1 in self.stage_ends:
+                dim_out = int(embed_dim * dim_mul)
+                num_heads = int(num_heads * head_mul)
+                cur_stage += 1
+
+            block = MultiScaleBlock(
+                dim=embed_dim,
+                dim_out=dim_out,
+                num_heads=num_heads,
+                drop_path=dpr[i],
+                q_stride=self.q_stride if i in self.q_pool_blocks else None,
+                window_size=window_size,
+            )
+
+            embed_dim = dim_out
+            self.blocks.append(block)
+
+        self.channel_list = (
+            [self.blocks[i].dim_out for i in self.stage_ends[::-1]]
+            if return_interm_layers
+            else [self.blocks[-1].dim_out]
+        )
+
+    def _get_pos_embed(self, hw: tuple[int, int]) -> torch.Tensor:
+        """Generate positional embeddings by interpolating and combining window and background embeddings."""
+        h, w = hw
+        window_embed = self.pos_embed_window
+        pos_embed = F.interpolate(self.pos_embed, size=(h, w), mode="bicubic")
+        pos_embed = pos_embed + window_embed.tile([x // y for x, y in zip(pos_embed.shape, window_embed.shape)])
+        pos_embed = pos_embed.permute(0, 2, 3, 1)
+        return pos_embed
+
+    def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
+        """
+        Perform forward pass through Hiera model, extracting multiscale features from input images.
+
+        Args:
+            x (torch.Tensor): Input tensor with shape (B, C, H, W) representing a batch of images.
+
+        Returns:
+            (list[torch.Tensor]): List of feature maps at different scales, each with shape (B, C_i, H_i, W_i), where
+                C_i is the channel dimension and H_i, W_i are the spatial dimensions at scale i. The list is ordered
+                from highest resolution (fine features) to lowest resolution (coarse features) if return_interm_layers
+                is True, otherwise contains only the final output.
+
+        Examples:
+            >>> model = Hiera(embed_dim=96, num_heads=1, stages=(2, 3, 16, 3))
+            >>> input_tensor = torch.randn(1, 3, 224, 224)
+            >>> output_features = model(input_tensor)
+            >>> for feat in output_features:
+            ...     print(feat.shape)
+        """
+        x = self.patch_embed(x)
+        # x: (B, H, W, C)
+
+        # Add positional embedding
+        x = x + self._get_pos_embed(x.shape[1:3])
+
+        outputs = []
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+            if (i == self.stage_ends[-1]) or (i in self.stage_ends and self.return_interm_layers):
+                feats = x.permute(0, 3, 1, 2)
+                outputs.append(feats)
+
+        return outputs

ultralytics/models/sam/modules/memory_attention.py

@@ -0,0 +1,312 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+import copy
+
+import torch
+from torch import nn
+
+from .blocks import RoPEAttention
+
+
+class MemoryAttentionLayer(nn.Module):
+    """
+    Implements a memory attention layer with self-attention and cross-attention mechanisms for neural networks.
+
+    This class combines self-attention, cross-attention, and feedforward components to process input tensors and
+    generate memory-based attention outputs.
+
+    Attributes:
+        d_model (int): Dimensionality of the model.
+        dim_feedforward (int): Dimensionality of the feedforward network.
+        dropout_value (float): Dropout rate for regularization.
+        self_attn (RoPEAttention): Self-attention mechanism using RoPE (Rotary Position Embedding).
+        cross_attn_image (RoPEAttention): Cross-attention mechanism for image processing.
+        linear1 (nn.Linear): First linear layer of the feedforward network.
+        linear2 (nn.Linear): Second linear layer of the feedforward network.
+        norm1 (nn.LayerNorm): Layer normalization for self-attention output.
+        norm2 (nn.LayerNorm): Layer normalization for cross-attention output.
+        norm3 (nn.LayerNorm): Layer normalization for feedforward network output.
+        dropout1 (nn.Dropout): Dropout layer after self-attention.
+        dropout2 (nn.Dropout): Dropout layer after cross-attention.
+        dropout3 (nn.Dropout): Dropout layer after feedforward network.
+        activation (nn.ReLU): Activation function for the feedforward network.
+        pos_enc_at_attn (bool): Flag to add positional encoding at attention.
+        pos_enc_at_cross_attn_queries (bool): Flag to add positional encoding to cross-attention queries.
+        pos_enc_at_cross_attn_keys (bool): Flag to add positional encoding to cross-attention keys.
+
+    Methods:
+        forward: Performs the full memory attention operation on input tensors.
+        _forward_sa: Performs self-attention on input tensor.
+        _forward_ca: Performs cross-attention between target and memory tensors.
+
+    Examples:
+        >>> layer = MemoryAttentionLayer(d_model=256, dim_feedforward=2048, dropout=0.1)
+        >>> tgt = torch.randn(1, 100, 256)
+        >>> memory = torch.randn(1, 100, 64)
+        >>> pos = torch.randn(1, 100, 256)
+        >>> query_pos = torch.randn(1, 100, 256)
+        >>> output = layer(tgt, memory, pos, query_pos)
+        >>> print(output.shape)
+        torch.Size([1, 100, 256])
+    """
+
+    def __init__(
+        self,
+        d_model: int = 256,
+        dim_feedforward: int = 2048,
+        dropout: float = 0.1,
+        pos_enc_at_attn: bool = False,
+        pos_enc_at_cross_attn_keys: bool = True,
+        pos_enc_at_cross_attn_queries: bool = False,
+    ):
+        """
+        Initialize a memory attention layer with self-attention, cross-attention, and feedforward components.
+
+        Args:
+            d_model (int): Dimensionality of the model.
+            dim_feedforward (int): Dimensionality of the feedforward network.
+            dropout (float): Dropout rate for regularization.
+            pos_enc_at_attn (bool): Whether to add positional encoding at attention.
+            pos_enc_at_cross_attn_keys (bool): Whether to add positional encoding to cross-attention keys.
+            pos_enc_at_cross_attn_queries (bool): Whether to add positional encoding to cross-attention queries.
+        """
+        super().__init__()
+        self.d_model = d_model
+        self.dim_feedforward = dim_feedforward
+        self.dropout_value = dropout
+        self.self_attn = RoPEAttention(embedding_dim=256, num_heads=1, downsample_rate=1)
+        self.cross_attn_image = RoPEAttention(
+            rope_k_repeat=True,
+            embedding_dim=256,
+            num_heads=1,
+            downsample_rate=1,
+            kv_in_dim=64,
+        )
+
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+        self.dropout3 = nn.Dropout(dropout)
+
+        self.activation = nn.ReLU()
+
+        # Where to add pos enc
+        self.pos_enc_at_attn = pos_enc_at_attn
+        self.pos_enc_at_cross_attn_queries = pos_enc_at_cross_attn_queries
+        self.pos_enc_at_cross_attn_keys = pos_enc_at_cross_attn_keys
+
+    def _forward_sa(self, tgt: torch.Tensor, query_pos: torch.Tensor | None) -> torch.Tensor:
+        """Perform self-attention on input tensor using positional encoding and RoPE attention mechanism."""
+        tgt2 = self.norm1(tgt)
+        q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2
+        tgt2 = self.self_attn(q, k, v=tgt2)
+        tgt = tgt + self.dropout1(tgt2)
+        return tgt
+
+    def _forward_ca(
+        self,
+        tgt: torch.Tensor,
+        memory: torch.Tensor,
+        query_pos: torch.Tensor | None,
+        pos: torch.Tensor | None,
+        num_k_exclude_rope: int = 0,
+    ) -> torch.Tensor:
+        """Perform cross-attention between target and memory tensors using RoPEAttention mechanism."""
+        kwds = {}
+        if num_k_exclude_rope > 0:
+            assert isinstance(self.cross_attn_image, RoPEAttention)
+            kwds = {"num_k_exclude_rope": num_k_exclude_rope}
+
+        # Cross-Attention
+        tgt2 = self.norm2(tgt)
+        tgt2 = self.cross_attn_image(
+            q=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2,
+            k=memory + pos if self.pos_enc_at_cross_attn_keys else memory,
+            v=memory,
+            **kwds,
+        )
+        tgt = tgt + self.dropout2(tgt2)
+        return tgt
+
+    def forward(
+        self,
+        tgt: torch.Tensor,
+        memory: torch.Tensor,
+        pos: torch.Tensor | None = None,
+        query_pos: torch.Tensor | None = None,
+        num_k_exclude_rope: int = 0,
+    ) -> torch.Tensor:
+        """
+        Process input tensors through self-attention, cross-attention, and feedforward network layers.
+
+        Args:
+            tgt (torch.Tensor): Target tensor for self-attention with shape (N, L, D).
+            memory (torch.Tensor): Memory tensor for cross-attention with shape (N, S, D).
+            pos (Optional[torch.Tensor]): Positional encoding for memory tensor.
+            query_pos (Optional[torch.Tensor]): Positional encoding for target tensor.
+            num_k_exclude_rope (int): Number of keys to exclude from rotary position embedding.
+
+        Returns:
+            (torch.Tensor): Processed tensor after attention and feedforward layers with shape (N, L, D).
+        """
+        tgt = self._forward_sa(tgt, query_pos)
+        tgt = self._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope)
+        # MLP
+        tgt2 = self.norm3(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+        tgt = tgt + self.dropout3(tgt2)
+        return tgt
+
+
+class MemoryAttention(nn.Module):
+    """
+    Memory attention module for processing sequential data with self and cross-attention mechanisms.
+
+    This class implements a multi-layer attention mechanism that combines self-attention and cross-attention
+    for processing sequential data, particularly useful in transformer-like architectures.
+
+    Attributes:
+        d_model (int): The dimension of the model's hidden state.
+        layers (nn.ModuleList): A list of MemoryAttentionLayer modules.
+        num_layers (int): The number of attention layers.
+        norm (nn.LayerNorm): Layer normalization applied to the output.
+        pos_enc_at_input (bool): Whether to apply positional encoding at the input.
+        batch_first (bool): Whether the input tensors are in batch-first format.
+
+    Methods:
+        forward: Processes input tensors through the attention layers.
+
+    Examples:
+        >>> d_model = 256
+        >>> layer = MemoryAttentionLayer(d_model)
+        >>> attention = MemoryAttention(d_model, pos_enc_at_input=True, layer=layer, num_layers=3)
+        >>> curr = torch.randn(10, 32, d_model)  # (seq_len, batch_size, d_model)
+        >>> memory = torch.randn(20, 32, d_model)  # (mem_len, batch_size, d_model)
+        >>> curr_pos = torch.randn(10, 32, d_model)
+        >>> memory_pos = torch.randn(20, 32, d_model)
+        >>> output = attention(curr, memory, curr_pos, memory_pos)
+        >>> print(output.shape)
+        torch.Size([10, 32, 256])
+    """
+
+    def __init__(
+        self,
+        d_model: int,
+        pos_enc_at_input: bool,
+        layer: nn.Module,
+        num_layers: int,
+        batch_first: bool = True,  # Do layers expect batch first input?
+    ):
+        """
+        Initialize MemoryAttention with specified layers and normalization for sequential data processing.
+
+        This class implements a multi-layer attention mechanism that combines self-attention and cross-attention
+        for processing sequential data, particularly useful in transformer-like architectures.
+
+        Args:
+            d_model (int): The dimension of the model's hidden state.
+            pos_enc_at_input (bool): Whether to apply positional encoding at the input.
+            layer (nn.Module): The attention layer to be used in the module.
+            num_layers (int): The number of attention layers.
+            batch_first (bool): Whether the input tensors are in batch-first format.
+
+        Examples:
+            >>> d_model = 256
+            >>> layer = MemoryAttentionLayer(d_model)
+            >>> attention = MemoryAttention(d_model, pos_enc_at_input=True, layer=layer, num_layers=3)
+            >>> curr = torch.randn(10, 32, d_model)  # (seq_len, batch_size, d_model)
+            >>> memory = torch.randn(20, 32, d_model)  # (mem_len, batch_size, d_model)
+            >>> curr_pos = torch.randn(10, 32, d_model)
+            >>> memory_pos = torch.randn(20, 32, d_model)
+            >>> output = attention(curr, memory, curr_pos, memory_pos)
+            >>> print(output.shape)
+            torch.Size([10, 32, 256])
+        """
+        super().__init__()
+        self.d_model = d_model
+        self.layers = nn.ModuleList([copy.deepcopy(layer) for _ in range(num_layers)])
+        self.num_layers = num_layers
+        self.norm = nn.LayerNorm(d_model)
+        self.pos_enc_at_input = pos_enc_at_input
+        self.batch_first = batch_first
+
+    def forward(
+        self,
+        curr: torch.Tensor,  # self-attention inputs
+        memory: torch.Tensor,  # cross-attention inputs
+        curr_pos: torch.Tensor | None = None,  # pos_enc for self-attention inputs
+        memory_pos: torch.Tensor | None = None,  # pos_enc for cross-attention inputs
+        num_obj_ptr_tokens: int = 0,  # number of object pointer *tokens*
+    ) -> torch.Tensor:
+        """
+        Process inputs through attention layers, applying self and cross-attention with positional encoding.
+
+        Args:
+            curr (torch.Tensor): Self-attention input tensor, representing the current state.
+            memory (torch.Tensor): Cross-attention input tensor, representing memory information.
+            curr_pos (Optional[torch.Tensor]): Positional encoding for self-attention inputs.
+            memory_pos (Optional[torch.Tensor]): Positional encoding for cross-attention inputs.
+            num_obj_ptr_tokens (int): Number of object pointer tokens to exclude from rotary position embedding.
+
+        Returns:
+            (torch.Tensor): Processed output tensor after applying attention layers and normalization.
+
+        Examples:
+            >>> d_model = 256
+            >>> layer = MemoryAttentionLayer(d_model)
+            >>> attention = MemoryAttention(d_model, pos_enc_at_input=True, layer=layer, num_layers=3)
+            >>> curr = torch.randn(10, 32, d_model)  # (seq_len, batch_size, d_model)
+            >>> memory = torch.randn(20, 32, d_model)  # (mem_len, batch_size, d_model)
+            >>> curr_pos = torch.randn(10, 32, d_model)
+            >>> memory_pos = torch.randn(20, 32, d_model)
+            >>> output = attention(curr, memory, curr_pos, memory_pos)
+            >>> print(output.shape)
+            torch.Size([10, 32, 256])
+        """
+        if isinstance(curr, list):
+            assert isinstance(curr_pos, list)
+            assert len(curr) == len(curr_pos) == 1
+            curr, curr_pos = curr[0], curr_pos[0]
+
+        assert curr.shape[1] == memory.shape[1], "Batch size must be the same for curr and memory"
+
+        output = curr
+        if self.pos_enc_at_input and curr_pos is not None:
+            output = output + 0.1 * curr_pos
+
+        if self.batch_first:
+            # Convert to batch first
+            output = output.transpose(0, 1)
+            curr_pos = curr_pos.transpose(0, 1)
+            memory = memory.transpose(0, 1)
+            memory_pos = memory_pos.transpose(0, 1)
+
+        for layer in self.layers:
+            kwds = {}
+            if isinstance(layer.cross_attn_image, RoPEAttention):
+                kwds = {"num_k_exclude_rope": num_obj_ptr_tokens}
+
+            output = layer(
+                tgt=output,
+                memory=memory,
+                pos=memory_pos,
+                query_pos=curr_pos,
+                **kwds,
+            )
+        normed_output = self.norm(output)
+
+        if self.batch_first:
+            # Convert back to seq first
+            normed_output = normed_output.transpose(0, 1)
+            curr_pos = curr_pos.transpose(0, 1)
+
+        return normed_output

ultralytics/models/sam/modules/tiny_encoder.py

@@ -0,0 +1,998 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# --------------------------------------------------------
+# TinyViT Model Architecture
+# Copyright (c) 2022 Microsoft
+# Adapted from LeViT and Swin Transformer
+#   LeViT: (https://github.com/facebookresearch/levit)
+#   Swin: (https://github.com/microsoft/swin-transformer)
+# Build the TinyViT Model
+# --------------------------------------------------------
+
+from __future__ import annotations
+
+import itertools
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ultralytics.nn.modules import LayerNorm2d
+from ultralytics.utils.instance import to_2tuple
+
+
+class Conv2d_BN(torch.nn.Sequential):
+    """
+    A sequential container that performs 2D convolution followed by batch normalization.
+
+    This module combines a 2D convolution layer with batch normalization, providing a common building block
+    for convolutional neural networks. The batch normalization weights and biases are initialized to specific
+    values for optimal training performance.
+
+    Attributes:
+        c (torch.nn.Conv2d): 2D convolution layer.
+        bn (torch.nn.BatchNorm2d): Batch normalization layer.
+
+    Examples:
+        >>> conv_bn = Conv2d_BN(3, 64, ks=3, stride=1, pad=1)
+        >>> input_tensor = torch.randn(1, 3, 224, 224)
+        >>> output = conv_bn(input_tensor)
+        >>> print(output.shape)
+        torch.Size([1, 64, 224, 224])
+    """
+
+    def __init__(
+        self,
+        a: int,
+        b: int,
+        ks: int = 1,
+        stride: int = 1,
+        pad: int = 0,
+        dilation: int = 1,
+        groups: int = 1,
+        bn_weight_init: float = 1,
+    ):
+        """
+        Initialize a sequential container with 2D convolution followed by batch normalization.
+
+        Args:
+            a (int): Number of input channels.
+            b (int): Number of output channels.
+            ks (int, optional): Kernel size for the convolution.
+            stride (int, optional): Stride for the convolution.
+            pad (int, optional): Padding for the convolution.
+            dilation (int, optional): Dilation factor for the convolution.
+            groups (int, optional): Number of groups for the convolution.
+            bn_weight_init (float, optional): Initial value for batch normalization weight.
+        """
+        super().__init__()
+        self.add_module("c", torch.nn.Conv2d(a, b, ks, stride, pad, dilation, groups, bias=False))
+        bn = torch.nn.BatchNorm2d(b)
+        torch.nn.init.constant_(bn.weight, bn_weight_init)
+        torch.nn.init.constant_(bn.bias, 0)
+        self.add_module("bn", bn)
+
+
+class PatchEmbed(nn.Module):
+    """
+    Embed images into patches and project them into a specified embedding dimension.
+
+    This module converts input images into patch embeddings using a sequence of convolutional layers,
+    effectively downsampling the spatial dimensions while increasing the channel dimension.
+
+    Attributes:
+        patches_resolution (tuple[int, int]): Resolution of the patches after embedding.
+        num_patches (int): Total number of patches.
+        in_chans (int): Number of input channels.
+        embed_dim (int): Dimension of the embedding.
+        seq (nn.Sequential): Sequence of convolutional and activation layers for patch embedding.
+
+    Examples:
+        >>> import torch
+        >>> patch_embed = PatchEmbed(in_chans=3, embed_dim=96, resolution=224, activation=nn.GELU)
+        >>> x = torch.randn(1, 3, 224, 224)
+        >>> output = patch_embed(x)
+        >>> print(output.shape)
+        torch.Size([1, 96, 56, 56])
+    """
+
+    def __init__(self, in_chans: int, embed_dim: int, resolution: int, activation):
+        """
+        Initialize patch embedding with convolutional layers for image-to-patch conversion and projection.
+
+        Args:
+            in_chans (int): Number of input channels.
+            embed_dim (int): Dimension of the embedding.
+            resolution (int): Input image resolution.
+            activation (nn.Module): Activation function to use between convolutions.
+        """
+        super().__init__()
+        img_size: tuple[int, int] = to_2tuple(resolution)
+        self.patches_resolution = (img_size[0] // 4, img_size[1] // 4)
+        self.num_patches = self.patches_resolution[0] * self.patches_resolution[1]
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+        n = embed_dim
+        self.seq = nn.Sequential(
+            Conv2d_BN(in_chans, n // 2, 3, 2, 1),
+            activation(),
+            Conv2d_BN(n // 2, n, 3, 2, 1),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Process input tensor through patch embedding sequence, converting images to patch embeddings."""
+        return self.seq(x)
+
+
+class MBConv(nn.Module):
+    """
+    Mobile Inverted Bottleneck Conv (MBConv) layer, part of the EfficientNet architecture.
+
+    This module implements the mobile inverted bottleneck convolution with expansion, depthwise convolution,
+    and projection phases, along with residual connections for improved gradient flow.
+
+    Attributes:
+        in_chans (int): Number of input channels.
+        hidden_chans (int): Number of hidden channels after expansion.
+        out_chans (int): Number of output channels.
+        conv1 (Conv2d_BN): First convolutional layer for channel expansion.
+        act1 (nn.Module): First activation function.
+        conv2 (Conv2d_BN): Depthwise convolutional layer.
+        act2 (nn.Module): Second activation function.
+        conv3 (Conv2d_BN): Final convolutional layer for projection.
+        act3 (nn.Module): Third activation function.
+        drop_path (nn.Module): Drop path layer (Identity for inference).
+
+    Examples:
+        >>> in_chans, out_chans = 32, 64
+        >>> mbconv = MBConv(in_chans, out_chans, expand_ratio=4, activation=nn.ReLU, drop_path=0.1)
+        >>> x = torch.randn(1, in_chans, 56, 56)
+        >>> output = mbconv(x)
+        >>> print(output.shape)
+        torch.Size([1, 64, 56, 56])
+    """
+
+    def __init__(self, in_chans: int, out_chans: int, expand_ratio: float, activation, drop_path: float):
+        """
+        Initialize the MBConv layer with specified input/output channels, expansion ratio, and activation.
+
+        Args:
+            in_chans (int): Number of input channels.
+            out_chans (int): Number of output channels.
+            expand_ratio (float): Channel expansion ratio for the hidden layer.
+            activation (nn.Module): Activation function to use.
+            drop_path (float): Drop path rate for stochastic depth.
+        """
+        super().__init__()
+        self.in_chans = in_chans
+        self.hidden_chans = int(in_chans * expand_ratio)
+        self.out_chans = out_chans
+
+        self.conv1 = Conv2d_BN(in_chans, self.hidden_chans, ks=1)
+        self.act1 = activation()
+
+        self.conv2 = Conv2d_BN(self.hidden_chans, self.hidden_chans, ks=3, stride=1, pad=1, groups=self.hidden_chans)
+        self.act2 = activation()
+
+        self.conv3 = Conv2d_BN(self.hidden_chans, out_chans, ks=1, bn_weight_init=0.0)
+        self.act3 = activation()
+
+        # NOTE: `DropPath` is needed only for training.
+        # self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.drop_path = nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Implement the forward pass of MBConv, applying convolutions and skip connection."""
+        shortcut = x
+        x = self.conv1(x)
+        x = self.act1(x)
+        x = self.conv2(x)
+        x = self.act2(x)
+        x = self.conv3(x)
+        x = self.drop_path(x)
+        x += shortcut
+        return self.act3(x)
+
+
+class PatchMerging(nn.Module):
+    """
+    Merge neighboring patches in the feature map and project to a new dimension.
+
+    This class implements a patch merging operation that combines spatial information and adjusts the feature
+    dimension using a series of convolutional layers with batch normalization. It effectively reduces spatial
+    resolution while potentially increasing channel dimensions.
+
+    Attributes:
+        input_resolution (tuple[int, int]): The input resolution (height, width) of the feature map.
+        dim (int): The input dimension of the feature map.
+        out_dim (int): The output dimension after merging and projection.
+        act (nn.Module): The activation function used between convolutions.
+        conv1 (Conv2d_BN): The first convolutional layer for dimension projection.
+        conv2 (Conv2d_BN): The second convolutional layer for spatial merging.
+        conv3 (Conv2d_BN): The third convolutional layer for final projection.
+
+    Examples:
+        >>> input_resolution = (56, 56)
+        >>> patch_merging = PatchMerging(input_resolution, dim=64, out_dim=128, activation=nn.ReLU)
+        >>> x = torch.randn(4, 64, 56, 56)
+        >>> output = patch_merging(x)
+        >>> print(output.shape)
+        torch.Size([4, 3136, 128])
+    """
+
+    def __init__(self, input_resolution: tuple[int, int], dim: int, out_dim: int, activation):
+        """
+        Initialize the PatchMerging module for merging and projecting neighboring patches in feature maps.
+
+        Args:
+            input_resolution (tuple[int, int]): The input resolution (height, width) of the feature map.
+            dim (int): The input dimension of the feature map.
+            out_dim (int): The output dimension after merging and projection.
+            activation (nn.Module): The activation function used between convolutions.
+        """
+        super().__init__()
+
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.out_dim = out_dim
+        self.act = activation()
+        self.conv1 = Conv2d_BN(dim, out_dim, 1, 1, 0)
+        stride_c = 1 if out_dim in {320, 448, 576} else 2
+        self.conv2 = Conv2d_BN(out_dim, out_dim, 3, stride_c, 1, groups=out_dim)
+        self.conv3 = Conv2d_BN(out_dim, out_dim, 1, 1, 0)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply patch merging and dimension projection to the input feature map."""
+        if x.ndim == 3:
+            H, W = self.input_resolution
+            B = len(x)
+            # (B, C, H, W)
+            x = x.view(B, H, W, -1).permute(0, 3, 1, 2)
+
+        x = self.conv1(x)
+        x = self.act(x)
+
+        x = self.conv2(x)
+        x = self.act(x)
+        x = self.conv3(x)
+        return x.flatten(2).transpose(1, 2)
+
+
+class ConvLayer(nn.Module):
+    """
+    Convolutional Layer featuring multiple MobileNetV3-style inverted bottleneck convolutions (MBConv).
+
+    This layer optionally applies downsample operations to the output and supports gradient checkpointing
+    for memory efficiency during training.
+
+    Attributes:
+        dim (int): Dimensionality of the input and output.
+        input_resolution (tuple[int, int]): Resolution of the input image.
+        depth (int): Number of MBConv layers in the block.
+        use_checkpoint (bool): Whether to use gradient checkpointing to save memory.
+        blocks (nn.ModuleList): List of MBConv layers.
+        downsample (Optional[nn.Module]): Function for downsampling the output.
+
+    Examples:
+        >>> input_tensor = torch.randn(1, 64, 56, 56)
+        >>> conv_layer = ConvLayer(64, (56, 56), depth=3, activation=nn.ReLU)
+        >>> output = conv_layer(input_tensor)
+        >>> print(output.shape)
+        torch.Size([1, 3136, 128])
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        input_resolution: tuple[int, int],
+        depth: int,
+        activation,
+        drop_path: float | list[float] = 0.0,
+        downsample: nn.Module | None = None,
+        use_checkpoint: bool = False,
+        out_dim: int | None = None,
+        conv_expand_ratio: float = 4.0,
+    ):
+        """
+        Initialize the ConvLayer with the given dimensions and settings.
+
+        This layer consists of multiple MobileNetV3-style inverted bottleneck convolutions (MBConv) and
+        optionally applies downsampling to the output.
+
+        Args:
+            dim (int): The dimensionality of the input and output.
+            input_resolution (tuple[int, int]): The resolution of the input image.
+            depth (int): The number of MBConv layers in the block.
+            activation (nn.Module): Activation function applied after each convolution.
+            drop_path (float | list[float], optional): Drop path rate. Single float or a list of floats for each MBConv.
+            downsample (Optional[nn.Module], optional): Function for downsampling the output. None to skip downsampling.
+            use_checkpoint (bool, optional): Whether to use gradient checkpointing to save memory.
+            out_dim (Optional[int], optional): The dimensionality of the output. None means it will be the same as `dim`.
+            conv_expand_ratio (float, optional): Expansion ratio for the MBConv layers.
+        """
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # Build blocks
+        self.blocks = nn.ModuleList(
+            [
+                MBConv(
+                    dim,
+                    dim,
+                    conv_expand_ratio,
+                    activation,
+                    drop_path[i] if isinstance(drop_path, list) else drop_path,
+                )
+                for i in range(depth)
+            ]
+        )
+
+        # Patch merging layer
+        self.downsample = (
+            None
+            if downsample is None
+            else downsample(input_resolution, dim=dim, out_dim=out_dim, activation=activation)
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Process input through convolutional layers, applying MBConv blocks and optional downsampling."""
+        for blk in self.blocks:
+            x = torch.utils.checkpoint(blk, x) if self.use_checkpoint else blk(x)  # warn: checkpoint is slow import
+        return x if self.downsample is None else self.downsample(x)
+
+
+class MLP(nn.Module):
+    """
+    Multi-layer Perceptron (MLP) module for transformer architectures.
+
+    This module applies layer normalization, two fully-connected layers with an activation function in between,
+    and dropout. It is commonly used in transformer-based architectures for processing token embeddings.
+
+    Attributes:
+        norm (nn.LayerNorm): Layer normalization applied to the input.
+        fc1 (nn.Linear): First fully-connected layer.
+        fc2 (nn.Linear): Second fully-connected layer.
+        act (nn.Module): Activation function applied after the first fully-connected layer.
+        drop (nn.Dropout): Dropout layer applied after the activation function.
+
+    Examples:
+        >>> import torch
+        >>> from torch import nn
+        >>> mlp = MLP(in_features=256, hidden_features=512, out_features=256, activation=nn.GELU, drop=0.1)
+        >>> x = torch.randn(32, 100, 256)
+        >>> output = mlp(x)
+        >>> print(output.shape)
+        torch.Size([32, 100, 256])
+    """
+
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: int | None = None,
+        out_features: int | None = None,
+        activation=nn.GELU,
+        drop: float = 0.0,
+    ):
+        """
+        Initialize a multi-layer perceptron with configurable input, hidden, and output dimensions.
+
+        Args:
+            in_features (int): Number of input features.
+            hidden_features (Optional[int], optional): Number of hidden features.
+            out_features (Optional[int], optional): Number of output features.
+            activation (nn.Module): Activation function applied after the first fully-connected layer.
+            drop (float, optional): Dropout probability.
+        """
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.norm = nn.LayerNorm(in_features)
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.act = activation()
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply MLP operations: layer norm, FC layers, activation, and dropout to the input tensor."""
+        x = self.norm(x)
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        return self.drop(x)
+
+
+class Attention(torch.nn.Module):
+    """
+    Multi-head attention module with spatial awareness and trainable attention biases.
+
+    This module implements a multi-head attention mechanism with support for spatial awareness, applying
+    attention biases based on spatial resolution. It includes trainable attention biases for each unique
+    offset between spatial positions in the resolution grid.
+
+    Attributes:
+        num_heads (int): Number of attention heads.
+        scale (float): Scaling factor for attention scores.
+        key_dim (int): Dimensionality of the keys and queries.
+        nh_kd (int): Product of num_heads and key_dim.
+        d (int): Dimensionality of the value vectors.
+        dh (int): Product of d and num_heads.
+        attn_ratio (float): Attention ratio affecting the dimensions of the value vectors.
+        norm (nn.LayerNorm): Layer normalization applied to input.
+        qkv (nn.Linear): Linear layer for computing query, key, and value projections.
+        proj (nn.Linear): Linear layer for final projection.
+        attention_biases (nn.Parameter): Learnable attention biases.
+        attention_bias_idxs (torch.Tensor): Indices for attention biases.
+        ab (torch.Tensor): Cached attention biases for inference, deleted during training.
+
+    Examples:
+        >>> attn = Attention(dim=256, key_dim=64, num_heads=8, resolution=(14, 14))
+        >>> x = torch.randn(1, 196, 256)
+        >>> output = attn(x)
+        >>> print(output.shape)
+        torch.Size([1, 196, 256])
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        key_dim: int,
+        num_heads: int = 8,
+        attn_ratio: float = 4,
+        resolution: tuple[int, int] = (14, 14),
+    ):
+        """
+        Initialize the Attention module for multi-head attention with spatial awareness.
+
+        This module implements a multi-head attention mechanism with support for spatial awareness, applying
+        attention biases based on spatial resolution. It includes trainable attention biases for each unique
+        offset between spatial positions in the resolution grid.
+
+        Args:
+            dim (int): The dimensionality of the input and output.
+            key_dim (int): The dimensionality of the keys and queries.
+            num_heads (int, optional): Number of attention heads.
+            attn_ratio (float, optional): Attention ratio, affecting the dimensions of the value vectors.
+            resolution (tuple[int, int], optional): Spatial resolution of the input feature map.
+        """
+        super().__init__()
+
+        assert isinstance(resolution, tuple) and len(resolution) == 2, "'resolution' argument not tuple of length 2"
+        self.num_heads = num_heads
+        self.scale = key_dim**-0.5
+        self.key_dim = key_dim
+        self.nh_kd = nh_kd = key_dim * num_heads
+        self.d = int(attn_ratio * key_dim)
+        self.dh = int(attn_ratio * key_dim) * num_heads
+        self.attn_ratio = attn_ratio
+        h = self.dh + nh_kd * 2
+
+        self.norm = nn.LayerNorm(dim)
+        self.qkv = nn.Linear(dim, h)
+        self.proj = nn.Linear(self.dh, dim)
+
+        points = list(itertools.product(range(resolution[0]), range(resolution[1])))
+        N = len(points)
+        attention_offsets = {}
+        idxs = []
+        for p1 in points:
+            for p2 in points:
+                offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
+                if offset not in attention_offsets:
+                    attention_offsets[offset] = len(attention_offsets)
+                idxs.append(attention_offsets[offset])
+        self.attention_biases = torch.nn.Parameter(torch.zeros(num_heads, len(attention_offsets)))
+        self.register_buffer("attention_bias_idxs", torch.LongTensor(idxs).view(N, N), persistent=False)
+
+    @torch.no_grad()
+    def train(self, mode: bool = True):
+        """Set the module in training mode and handle the 'ab' attribute for cached attention biases."""
+        super().train(mode)
+        if mode and hasattr(self, "ab"):
+            del self.ab
+        else:
+            self.ab = self.attention_biases[:, self.attention_bias_idxs]
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply multi-head attention with spatial awareness and trainable attention biases."""
+        B, N, _ = x.shape  # B, N, C
+
+        # Normalization
+        x = self.norm(x)
+
+        qkv = self.qkv(x)
+        # (B, N, num_heads, d)
+        q, k, v = qkv.view(B, N, self.num_heads, -1).split([self.key_dim, self.key_dim, self.d], dim=3)
+        # (B, num_heads, N, d)
+        q = q.permute(0, 2, 1, 3)
+        k = k.permute(0, 2, 1, 3)
+        v = v.permute(0, 2, 1, 3)
+        self.ab = self.ab.to(self.attention_biases.device)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale + (
+            self.attention_biases[:, self.attention_bias_idxs] if self.training else self.ab
+        )
+        attn = attn.softmax(dim=-1)
+        x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh)
+        return self.proj(x)
+
+
+class TinyViTBlock(nn.Module):
+    """
+    TinyViT Block that applies self-attention and a local convolution to the input.
+
+    This block is a key component of the TinyViT architecture, combining self-attention mechanisms with
+    local convolutions to process input features efficiently. It supports windowed attention for
+    computational efficiency and includes residual connections.
+
+    Attributes:
+        dim (int): The dimensionality of the input and output.
+        input_resolution (tuple[int, int]): Spatial resolution of the input feature map.
+        num_heads (int): Number of attention heads.
+        window_size (int): Size of the attention window.
+        mlp_ratio (float): Ratio of MLP hidden dimension to embedding dimension.
+        drop_path (nn.Module): Stochastic depth layer, identity function during inference.
+        attn (Attention): Self-attention module.
+        mlp (MLP): Multi-layer perceptron module.
+        local_conv (Conv2d_BN): Depth-wise local convolution layer.
+
+    Examples:
+        >>> input_tensor = torch.randn(1, 196, 192)
+        >>> block = TinyViTBlock(dim=192, input_resolution=(14, 14), num_heads=3)
+        >>> output = block(input_tensor)
+        >>> print(output.shape)
+        torch.Size([1, 196, 192])
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        input_resolution: tuple[int, int],
+        num_heads: int,
+        window_size: int = 7,
+        mlp_ratio: float = 4.0,
+        drop: float = 0.0,
+        drop_path: float = 0.0,
+        local_conv_size: int = 3,
+        activation=nn.GELU,
+    ):
+        """
+        Initialize a TinyViT block with self-attention and local convolution.
+
+        This block is a key component of the TinyViT architecture, combining self-attention mechanisms with
+        local convolutions to process input features efficiently.
+
+        Args:
+            dim (int): Dimensionality of the input and output features.
+            input_resolution (tuple[int, int]): Spatial resolution of the input feature map (height, width).
+            num_heads (int): Number of attention heads.
+            window_size (int, optional): Size of the attention window. Must be greater than 0.
+            mlp_ratio (float, optional): Ratio of MLP hidden dimension to embedding dimension.
+            drop (float, optional): Dropout rate.
+            drop_path (float, optional): Stochastic depth rate.
+            local_conv_size (int, optional): Kernel size of the local convolution.
+            activation (nn.Module): Activation function for MLP.
+        """
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        assert window_size > 0, "window_size must be greater than 0"
+        self.window_size = window_size
+        self.mlp_ratio = mlp_ratio
+
+        # NOTE: `DropPath` is needed only for training.
+        # self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.drop_path = nn.Identity()
+
+        assert dim % num_heads == 0, "dim must be divisible by num_heads"
+        head_dim = dim // num_heads
+
+        window_resolution = (window_size, window_size)
+        self.attn = Attention(dim, head_dim, num_heads, attn_ratio=1, resolution=window_resolution)
+
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        mlp_activation = activation
+        self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, activation=mlp_activation, drop=drop)
+
+        pad = local_conv_size // 2
+        self.local_conv = Conv2d_BN(dim, dim, ks=local_conv_size, stride=1, pad=pad, groups=dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply self-attention, local convolution, and MLP operations to the input tensor."""
+        h, w = self.input_resolution
+        b, hw, c = x.shape  # batch, height*width, channels
+        assert hw == h * w, "input feature has wrong size"
+        res_x = x
+        if h == self.window_size and w == self.window_size:
+            x = self.attn(x)
+        else:
+            x = x.view(b, h, w, c)
+            pad_b = (self.window_size - h % self.window_size) % self.window_size
+            pad_r = (self.window_size - w % self.window_size) % self.window_size
+            padding = pad_b > 0 or pad_r > 0
+            if padding:
+                x = F.pad(x, (0, 0, 0, pad_r, 0, pad_b))
+
+            pH, pW = h + pad_b, w + pad_r
+            nH = pH // self.window_size
+            nW = pW // self.window_size
+
+            # Window partition
+            x = (
+                x.view(b, nH, self.window_size, nW, self.window_size, c)
+                .transpose(2, 3)
+                .reshape(b * nH * nW, self.window_size * self.window_size, c)
+            )
+            x = self.attn(x)
+
+            # Window reverse
+            x = x.view(b, nH, nW, self.window_size, self.window_size, c).transpose(2, 3).reshape(b, pH, pW, c)
+            if padding:
+                x = x[:, :h, :w].contiguous()
+
+            x = x.view(b, hw, c)
+
+        x = res_x + self.drop_path(x)
+        x = x.transpose(1, 2).reshape(b, c, h, w)
+        x = self.local_conv(x)
+        x = x.view(b, c, hw).transpose(1, 2)
+
+        return x + self.drop_path(self.mlp(x))
+
+    def extra_repr(self) -> str:
+        """
+        Return a string representation of the TinyViTBlock's parameters.
+
+        This method provides a formatted string containing key information about the TinyViTBlock, including its
+        dimension, input resolution, number of attention heads, window size, and MLP ratio.
+
+        Returns:
+            (str): A formatted string containing the block's parameters.
+
+        Examples:
+            >>> block = TinyViTBlock(dim=192, input_resolution=(14, 14), num_heads=3, window_size=7, mlp_ratio=4.0)
+            >>> print(block.extra_repr())
+            dim=192, input_resolution=(14, 14), num_heads=3, window_size=7, mlp_ratio=4.0
+        """
+        return (
+            f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
+            f"window_size={self.window_size}, mlp_ratio={self.mlp_ratio}"
+        )
+
+
+class BasicLayer(nn.Module):
+    """
+    A basic TinyViT layer for one stage in a TinyViT architecture.
+
+    This class represents a single layer in the TinyViT model, consisting of multiple TinyViT blocks
+    and an optional downsampling operation. It processes features at a specific resolution and
+    dimensionality within the overall architecture.
+
+    Attributes:
+        dim (int): The dimensionality of the input and output features.
+        input_resolution (tuple[int, int]): Spatial resolution of the input feature map.
+        depth (int): Number of TinyViT blocks in this layer.
+        use_checkpoint (bool): Whether to use gradient checkpointing to save memory.
+        blocks (nn.ModuleList): List of TinyViT blocks that make up this layer.
+        downsample (nn.Module | None): Downsample layer at the end of the layer, if specified.
+
+    Examples:
+        >>> input_tensor = torch.randn(1, 3136, 192)
+        >>> layer = BasicLayer(dim=192, input_resolution=(56, 56), depth=2, num_heads=3, window_size=7)
+        >>> output = layer(input_tensor)
+        >>> print(output.shape)
+        torch.Size([1, 784, 384])
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        input_resolution: tuple[int, int],
+        depth: int,
+        num_heads: int,
+        window_size: int,
+        mlp_ratio: float = 4.0,
+        drop: float = 0.0,
+        drop_path: float | list[float] = 0.0,
+        downsample: nn.Module | None = None,
+        use_checkpoint: bool = False,
+        local_conv_size: int = 3,
+        activation=nn.GELU,
+        out_dim: int | None = None,
+    ):
+        """
+        Initialize a BasicLayer in the TinyViT architecture.
+
+        This layer consists of multiple TinyViT blocks and an optional downsampling operation. It is designed to
+        process feature maps at a specific resolution and dimensionality within the TinyViT model.
+
+        Args:
+            dim (int): Dimensionality of the input and output features.
+            input_resolution (tuple[int, int]): Spatial resolution of the input feature map (height, width).
+            depth (int): Number of TinyViT blocks in this layer.
+            num_heads (int): Number of attention heads in each TinyViT block.
+            window_size (int): Size of the local window for attention computation.
+            mlp_ratio (float, optional): Ratio of MLP hidden dimension to embedding dimension.
+            drop (float, optional): Dropout rate.
+            drop_path (float | list[float], optional): Stochastic depth rate. Can be a float or a list of floats for each block.
+            downsample (nn.Module | None, optional): Downsampling layer at the end of the layer. None to skip downsampling.
+            use_checkpoint (bool, optional): Whether to use gradient checkpointing to save memory.
+            local_conv_size (int, optional): Kernel size for the local convolution in each TinyViT block.
+            activation (nn.Module): Activation function used in the MLP.
+            out_dim (int | None, optional): Output dimension after downsampling. None means it will be the same as `dim`.
+        """
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # Build blocks
+        self.blocks = nn.ModuleList(
+            [
+                TinyViTBlock(
+                    dim=dim,
+                    input_resolution=input_resolution,
+                    num_heads=num_heads,
+                    window_size=window_size,
+                    mlp_ratio=mlp_ratio,
+                    drop=drop,
+                    drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                    local_conv_size=local_conv_size,
+                    activation=activation,
+                )
+                for i in range(depth)
+            ]
+        )
+
+        # Patch merging layer
+        self.downsample = (
+            None
+            if downsample is None
+            else downsample(input_resolution, dim=dim, out_dim=out_dim, activation=activation)
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Process input through TinyViT blocks and optional downsampling."""
+        for blk in self.blocks:
+            x = torch.utils.checkpoint(blk, x) if self.use_checkpoint else blk(x)  # warn: checkpoint is slow import
+        return x if self.downsample is None else self.downsample(x)
+
+    def extra_repr(self) -> str:
+        """Return a string with the layer's parameters for printing."""
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+
+class TinyViT(nn.Module):
+    """
+    TinyViT: A compact vision transformer architecture for efficient image classification and feature extraction.
+
+    This class implements the TinyViT model, which combines elements of vision transformers and convolutional
+    neural networks for improved efficiency and performance on vision tasks. It features hierarchical processing
+    with patch embedding, multiple stages of attention and convolution blocks, and a feature refinement neck.
+
+    Attributes:
+        img_size (int): Input image size.
+        num_classes (int): Number of classification classes.
+        depths (tuple[int, int, int, int]): Number of blocks in each stage.
+        num_layers (int): Total number of layers in the network.
+        mlp_ratio (float): Ratio of MLP hidden dimension to embedding dimension.
+        patch_embed (PatchEmbed): Module for patch embedding.
+        patches_resolution (tuple[int, int]): Resolution of embedded patches.
+        layers (nn.ModuleList): List of network layers.
+        norm_head (nn.LayerNorm): Layer normalization for the classifier head.
+        head (nn.Linear): Linear layer for final classification.
+        neck (nn.Sequential): Neck module for feature refinement.
+
+    Examples:
+        >>> model = TinyViT(img_size=224, num_classes=1000)
+        >>> x = torch.randn(1, 3, 224, 224)
+        >>> features = model.forward_features(x)
+        >>> print(features.shape)
+        torch.Size([1, 256, 56, 56])
+    """
+
+    def __init__(
+        self,
+        img_size: int = 224,
+        in_chans: int = 3,
+        num_classes: int = 1000,
+        embed_dims: tuple[int, int, int, int] = (96, 192, 384, 768),
+        depths: tuple[int, int, int, int] = (2, 2, 6, 2),
+        num_heads: tuple[int, int, int, int] = (3, 6, 12, 24),
+        window_sizes: tuple[int, int, int, int] = (7, 7, 14, 7),
+        mlp_ratio: float = 4.0,
+        drop_rate: float = 0.0,
+        drop_path_rate: float = 0.1,
+        use_checkpoint: bool = False,
+        mbconv_expand_ratio: float = 4.0,
+        local_conv_size: int = 3,
+        layer_lr_decay: float = 1.0,
+    ):
+        """
+        Initialize the TinyViT model.
+
+        This constructor sets up the TinyViT architecture, including patch embedding, multiple layers of
+        attention and convolution blocks, and a classification head.
+
+        Args:
+            img_size (int, optional): Size of the input image.
+            in_chans (int, optional): Number of input channels.
+            num_classes (int, optional): Number of classes for classification.
+            embed_dims (tuple[int, int, int, int], optional): Embedding dimensions for each stage.
+            depths (tuple[int, int, int, int], optional): Number of blocks in each stage.
+            num_heads (tuple[int, int, int, int], optional): Number of attention heads in each stage.
+            window_sizes (tuple[int, int, int, int], optional): Window sizes for each stage.
+            mlp_ratio (float, optional): Ratio of MLP hidden dim to embedding dim.
+            drop_rate (float, optional): Dropout rate.
+            drop_path_rate (float, optional): Stochastic depth rate.
+            use_checkpoint (bool, optional): Whether to use checkpointing to save memory.
+            mbconv_expand_ratio (float, optional): Expansion ratio for MBConv layer.
+            local_conv_size (int, optional): Kernel size for local convolutions.
+            layer_lr_decay (float, optional): Layer-wise learning rate decay factor.
+        """
+        super().__init__()
+        self.img_size = img_size
+        self.num_classes = num_classes
+        self.depths = depths
+        self.num_layers = len(depths)
+        self.mlp_ratio = mlp_ratio
+
+        activation = nn.GELU
+
+        self.patch_embed = PatchEmbed(
+            in_chans=in_chans, embed_dim=embed_dims[0], resolution=img_size, activation=activation
+        )
+
+        patches_resolution = self.patch_embed.patches_resolution
+        self.patches_resolution = patches_resolution
+
+        # Stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+
+        # Build layers
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            kwargs = dict(
+                dim=embed_dims[i_layer],
+                input_resolution=(
+                    patches_resolution[0] // (2 ** (i_layer - 1 if i_layer == 3 else i_layer)),
+                    patches_resolution[1] // (2 ** (i_layer - 1 if i_layer == 3 else i_layer)),
+                ),
+                #   input_resolution=(patches_resolution[0] // (2 ** i_layer),
+                #                     patches_resolution[1] // (2 ** i_layer)),
+                depth=depths[i_layer],
+                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
+                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+                use_checkpoint=use_checkpoint,
+                out_dim=embed_dims[min(i_layer + 1, len(embed_dims) - 1)],
+                activation=activation,
+            )
+            if i_layer == 0:
+                layer = ConvLayer(conv_expand_ratio=mbconv_expand_ratio, **kwargs)
+            else:
+                layer = BasicLayer(
+                    num_heads=num_heads[i_layer],
+                    window_size=window_sizes[i_layer],
+                    mlp_ratio=self.mlp_ratio,
+                    drop=drop_rate,
+                    local_conv_size=local_conv_size,
+                    **kwargs,
+                )
+            self.layers.append(layer)
+
+        # Classifier head
+        self.norm_head = nn.LayerNorm(embed_dims[-1])
+        self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else torch.nn.Identity()
+
+        # Init weights
+        self.apply(self._init_weights)
+        self.set_layer_lr_decay(layer_lr_decay)
+        self.neck = nn.Sequential(
+            nn.Conv2d(
+                embed_dims[-1],
+                256,
+                kernel_size=1,
+                bias=False,
+            ),
+            LayerNorm2d(256),
+            nn.Conv2d(
+                256,
+                256,
+                kernel_size=3,
+                padding=1,
+                bias=False,
+            ),
+            LayerNorm2d(256),
+        )
+
+    def set_layer_lr_decay(self, layer_lr_decay: float):
+        """Set layer-wise learning rate decay for the TinyViT model based on depth."""
+        decay_rate = layer_lr_decay
+
+        # Layers -> blocks (depth)
+        depth = sum(self.depths)
+        lr_scales = [decay_rate ** (depth - i - 1) for i in range(depth)]
+
+        def _set_lr_scale(m, scale):
+            """Set the learning rate scale for each layer in the model based on the layer's depth."""
+            for p in m.parameters():
+                p.lr_scale = scale
+
+        self.patch_embed.apply(lambda x: _set_lr_scale(x, lr_scales[0]))
+        i = 0
+        for layer in self.layers:
+            for block in layer.blocks:
+                block.apply(lambda x: _set_lr_scale(x, lr_scales[i]))
+                i += 1
+            if layer.downsample is not None:
+                layer.downsample.apply(lambda x: _set_lr_scale(x, lr_scales[i - 1]))
+        assert i == depth
+        for m in {self.norm_head, self.head}:
+            m.apply(lambda x: _set_lr_scale(x, lr_scales[-1]))
+
+        for k, p in self.named_parameters():
+            p.param_name = k
+
+        def _check_lr_scale(m):
+            """Check if the learning rate scale attribute is present in module's parameters."""
+            for p in m.parameters():
+                assert hasattr(p, "lr_scale"), p.param_name
+
+        self.apply(_check_lr_scale)
+
+    @staticmethod
+    def _init_weights(m):
+        """Initialize weights for linear and normalization layers in the TinyViT model."""
+        if isinstance(m, nn.Linear):
+            # NOTE: This initialization is needed only for training.
+            # trunc_normal_(m.weight, std=.02)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        """Return a set of keywords for parameters that should not use weight decay."""
+        return {"attention_biases"}
+
+    def forward_features(self, x: torch.Tensor) -> torch.Tensor:
+        """Process input through feature extraction layers, returning spatial features."""
+        x = self.patch_embed(x)  # x input is (N, C, H, W)
+
+        x = self.layers[0](x)
+        start_i = 1
+
+        for i in range(start_i, len(self.layers)):
+            layer = self.layers[i]
+            x = layer(x)
+        batch, _, channel = x.shape
+        x = x.view(batch, self.patches_resolution[0] // 4, self.patches_resolution[1] // 4, channel)
+        x = x.permute(0, 3, 1, 2)
+        return self.neck(x)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Perform the forward pass through the TinyViT model, extracting features from the input image."""
+        return self.forward_features(x)
+
+    def set_imgsz(self, imgsz: list[int] = [1024, 1024]):
+        """Set image size to make model compatible with different image sizes."""
+        imgsz = [s // 4 for s in imgsz]
+        self.patches_resolution = imgsz
+        for i, layer in enumerate(self.layers):
+            input_resolution = (
+                imgsz[0] // (2 ** (i - 1 if i == 3 else i)),
+                imgsz[1] // (2 ** (i - 1 if i == 3 else i)),
+            )
+            layer.input_resolution = input_resolution
+            if layer.downsample is not None:
+                layer.downsample.input_resolution = input_resolution
+            if isinstance(layer, BasicLayer):
+                for b in layer.blocks:
+                    b.input_resolution = input_resolution

ultralytics/models/sam/modules/transformer.py

@@ -0,0 +1,354 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+import math
+
+import torch
+from torch import Tensor, nn
+
+from ultralytics.nn.modules import MLPBlock
+
+
+class TwoWayTransformer(nn.Module):
+    """
+    A Two-Way Transformer module for simultaneous attention to image and query points.
+
+    This class implements a specialized transformer decoder that attends to an input image using queries with
+    supplied positional embeddings. It's useful for tasks like object detection, image segmentation, and point
+    cloud processing.
+
+    Attributes:
+        depth (int): Number of layers in the transformer.
+        embedding_dim (int): Channel dimension for input embeddings.
+        num_heads (int): Number of heads for multihead attention.
+        mlp_dim (int): Internal channel dimension for the MLP block.
+        layers (nn.ModuleList): List of TwoWayAttentionBlock layers composing the transformer.
+        final_attn_token_to_image (Attention): Final attention layer from queries to image.
+        norm_final_attn (nn.LayerNorm): Layer normalization applied to final queries.
+
+    Methods:
+        forward: Process image and point embeddings through the transformer.
+
+    Examples:
+        >>> transformer = TwoWayTransformer(depth=6, embedding_dim=256, num_heads=8, mlp_dim=2048)
+        >>> image_embedding = torch.randn(1, 256, 32, 32)
+        >>> image_pe = torch.randn(1, 256, 32, 32)
+        >>> point_embedding = torch.randn(1, 100, 256)
+        >>> output_queries, output_image = transformer(image_embedding, image_pe, point_embedding)
+        >>> print(output_queries.shape, output_image.shape)
+    """
+
+    def __init__(
+        self,
+        depth: int,
+        embedding_dim: int,
+        num_heads: int,
+        mlp_dim: int,
+        activation: type[nn.Module] = nn.ReLU,
+        attention_downsample_rate: int = 2,
+    ) -> None:
+        """
+        Initialize a Two-Way Transformer for simultaneous attention to image and query points.
+
+        Args:
+            depth (int): Number of layers in the transformer.
+            embedding_dim (int): Channel dimension for input embeddings.
+            num_heads (int): Number of heads for multihead attention. Must divide embedding_dim.
+            mlp_dim (int): Internal channel dimension for the MLP block.
+            activation (Type[nn.Module], optional): Activation function to use in the MLP block.
+            attention_downsample_rate (int, optional): Downsampling rate for attention mechanism.
+        """
+        super().__init__()
+        self.depth = depth
+        self.embedding_dim = embedding_dim
+        self.num_heads = num_heads
+        self.mlp_dim = mlp_dim
+        self.layers = nn.ModuleList()
+
+        for i in range(depth):
+            self.layers.append(
+                TwoWayAttentionBlock(
+                    embedding_dim=embedding_dim,
+                    num_heads=num_heads,
+                    mlp_dim=mlp_dim,
+                    activation=activation,
+                    attention_downsample_rate=attention_downsample_rate,
+                    skip_first_layer_pe=(i == 0),
+                )
+            )
+
+        self.final_attn_token_to_image = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
+        self.norm_final_attn = nn.LayerNorm(embedding_dim)
+
+    def forward(
+        self,
+        image_embedding: torch.Tensor,
+        image_pe: torch.Tensor,
+        point_embedding: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Process image and point embeddings through the Two-Way Transformer.
+
+        Args:
+            image_embedding (torch.Tensor): Image to attend to, with shape (B, embedding_dim, H, W).
+            image_pe (torch.Tensor): Positional encoding to add to the image, with same shape as image_embedding.
+            point_embedding (torch.Tensor): Embedding to add to query points, with shape (B, N_points, embedding_dim).
+
+        Returns:
+            queries (torch.Tensor): Processed point embeddings with shape (B, N_points, embedding_dim).
+            keys (torch.Tensor): Processed image embeddings with shape (B, H*W, embedding_dim).
+        """
+        # BxCxHxW -> BxHWxC == B x N_image_tokens x C
+        image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
+        image_pe = image_pe.flatten(2).permute(0, 2, 1)
+
+        # Prepare queries
+        queries = point_embedding
+        keys = image_embedding
+
+        # Apply transformer blocks and final layernorm
+        for layer in self.layers:
+            queries, keys = layer(
+                queries=queries,
+                keys=keys,
+                query_pe=point_embedding,
+                key_pe=image_pe,
+            )
+
+        # Apply the final attention layer from the points to the image
+        q = queries + point_embedding
+        k = keys + image_pe
+        attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
+        queries = queries + attn_out
+        queries = self.norm_final_attn(queries)
+
+        return queries, keys
+
+
+class TwoWayAttentionBlock(nn.Module):
+    """
+    A two-way attention block for simultaneous attention to image and query points.
+
+    This class implements a specialized transformer block with four main layers: self-attention on sparse inputs,
+    cross-attention of sparse inputs to dense inputs, MLP block on sparse inputs, and cross-attention of dense
+    inputs to sparse inputs.
+
+    Attributes:
+        self_attn (Attention): Self-attention layer for queries.
+        norm1 (nn.LayerNorm): Layer normalization after self-attention.
+        cross_attn_token_to_image (Attention): Cross-attention layer from queries to keys.
+        norm2 (nn.LayerNorm): Layer normalization after token-to-image attention.
+        mlp (MLPBlock): MLP block for transforming query embeddings.
+        norm3 (nn.LayerNorm): Layer normalization after MLP block.
+        norm4 (nn.LayerNorm): Layer normalization after image-to-token attention.
+        cross_attn_image_to_token (Attention): Cross-attention layer from keys to queries.
+        skip_first_layer_pe (bool): Whether to skip positional encoding in the first layer.
+
+    Methods:
+        forward: Apply self-attention and cross-attention to queries and keys.
+
+    Examples:
+        >>> embedding_dim, num_heads = 256, 8
+        >>> block = TwoWayAttentionBlock(embedding_dim, num_heads)
+        >>> queries = torch.randn(1, 100, embedding_dim)
+        >>> keys = torch.randn(1, 1000, embedding_dim)
+        >>> query_pe = torch.randn(1, 100, embedding_dim)
+        >>> key_pe = torch.randn(1, 1000, embedding_dim)
+        >>> processed_queries, processed_keys = block(queries, keys, query_pe, key_pe)
+    """
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_heads: int,
+        mlp_dim: int = 2048,
+        activation: type[nn.Module] = nn.ReLU,
+        attention_downsample_rate: int = 2,
+        skip_first_layer_pe: bool = False,
+    ) -> None:
+        """
+        Initialize a TwoWayAttentionBlock for simultaneous attention to image and query points.
+
+        This block implements a specialized transformer layer with four main components: self-attention on sparse
+        inputs, cross-attention of sparse inputs to dense inputs, MLP block on sparse inputs, and cross-attention
+        of dense inputs to sparse inputs.
+
+        Args:
+            embedding_dim (int): Channel dimension of the embeddings.
+            num_heads (int): Number of attention heads in the attention layers.
+            mlp_dim (int, optional): Hidden dimension of the MLP block.
+            activation (Type[nn.Module], optional): Activation function for the MLP block.
+            attention_downsample_rate (int, optional): Downsampling rate for the attention mechanism.
+            skip_first_layer_pe (bool, optional): Whether to skip positional encoding in the first layer.
+        """
+        super().__init__()
+        self.self_attn = Attention(embedding_dim, num_heads)
+        self.norm1 = nn.LayerNorm(embedding_dim)
+
+        self.cross_attn_token_to_image = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
+        self.norm2 = nn.LayerNorm(embedding_dim)
+
+        self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
+        self.norm3 = nn.LayerNorm(embedding_dim)
+
+        self.norm4 = nn.LayerNorm(embedding_dim)
+        self.cross_attn_image_to_token = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
+
+        self.skip_first_layer_pe = skip_first_layer_pe
+
+    def forward(
+        self, queries: torch.Tensor, keys: torch.Tensor, query_pe: torch.Tensor, key_pe: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Apply two-way attention to process query and key embeddings in a transformer block.
+
+        Args:
+            queries (torch.Tensor): Query embeddings with shape (B, N_queries, embedding_dim).
+            keys (torch.Tensor): Key embeddings with shape (B, N_keys, embedding_dim).
+            query_pe (torch.Tensor): Positional encodings for queries with same shape as queries.
+            key_pe (torch.Tensor): Positional encodings for keys with same shape as keys.
+
+        Returns:
+            queries (torch.Tensor): Processed query embeddings with shape (B, N_queries, embedding_dim).
+            keys (torch.Tensor): Processed key embeddings with shape (B, N_keys, embedding_dim).
+        """
+        # Self attention block
+        if self.skip_first_layer_pe:
+            queries = self.self_attn(q=queries, k=queries, v=queries)
+        else:
+            q = queries + query_pe
+            attn_out = self.self_attn(q=q, k=q, v=queries)
+            queries = queries + attn_out
+        queries = self.norm1(queries)
+
+        # Cross attention block, tokens attending to image embedding
+        q = queries + query_pe
+        k = keys + key_pe
+        attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
+        queries = queries + attn_out
+        queries = self.norm2(queries)
+
+        # MLP block
+        mlp_out = self.mlp(queries)
+        queries = queries + mlp_out
+        queries = self.norm3(queries)
+
+        # Cross attention block, image embedding attending to tokens
+        q = queries + query_pe
+        k = keys + key_pe
+        attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
+        keys = keys + attn_out
+        keys = self.norm4(keys)
+
+        return queries, keys
+
+
+class Attention(nn.Module):
+    """
+    An attention layer with downscaling capability for embedding size after projection.
+
+    This class implements a multi-head attention mechanism with the option to downsample the internal
+    dimension of queries, keys, and values.
+
+    Attributes:
+        embedding_dim (int): Dimensionality of input embeddings.
+        kv_in_dim (int): Dimensionality of key and value inputs.
+        internal_dim (int): Internal dimension after downsampling.
+        num_heads (int): Number of attention heads.
+        q_proj (nn.Linear): Linear projection for queries.
+        k_proj (nn.Linear): Linear projection for keys.
+        v_proj (nn.Linear): Linear projection for values.
+        out_proj (nn.Linear): Linear projection for output.
+
+    Methods:
+        _separate_heads: Separate input tensor into attention heads.
+        _recombine_heads: Recombine separated attention heads.
+        forward: Compute attention output for given query, key, and value tensors.
+
+    Examples:
+        >>> attn = Attention(embedding_dim=256, num_heads=8, downsample_rate=2)
+        >>> q = torch.randn(1, 100, 256)
+        >>> k = v = torch.randn(1, 50, 256)
+        >>> output = attn(q, k, v)
+        >>> print(output.shape)
+        torch.Size([1, 100, 256])
+    """
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_heads: int,
+        downsample_rate: int = 1,
+        kv_in_dim: int = None,
+    ) -> None:
+        """
+        Initialize the Attention module with specified dimensions and settings.
+
+        Args:
+            embedding_dim (int): Dimensionality of input embeddings.
+            num_heads (int): Number of attention heads.
+            downsample_rate (int, optional): Factor by which internal dimensions are downsampled.
+            kv_in_dim (int | None, optional): Dimensionality of key and value inputs. If None, uses embedding_dim.
+
+        Raises:
+            AssertionError: If num_heads does not evenly divide the internal dim (embedding_dim / downsample_rate).
+        """
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.kv_in_dim = kv_in_dim if kv_in_dim is not None else embedding_dim
+        self.internal_dim = embedding_dim // downsample_rate
+        self.num_heads = num_heads
+        assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."
+
+        self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
+        self.k_proj = nn.Linear(self.kv_in_dim, self.internal_dim)
+        self.v_proj = nn.Linear(self.kv_in_dim, self.internal_dim)
+        self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
+
+    @staticmethod
+    def _separate_heads(x: torch.Tensor, num_heads: int) -> torch.Tensor:
+        """Separate the input tensor into the specified number of attention heads."""
+        b, n, c = x.shape
+        x = x.reshape(b, n, num_heads, c // num_heads)
+        return x.transpose(1, 2)  # B x N_heads x N_tokens x C_per_head
+
+    @staticmethod
+    def _recombine_heads(x: Tensor) -> Tensor:
+        """Recombine separated attention heads into a single tensor."""
+        b, n_heads, n_tokens, c_per_head = x.shape
+        x = x.transpose(1, 2)
+        return x.reshape(b, n_tokens, n_heads * c_per_head)  # B x N_tokens x C
+
+    def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
+        """
+        Apply multi-head attention to query, key, and value tensors with optional downsampling.
+
+        Args:
+            q (torch.Tensor): Query tensor with shape (B, N_q, embedding_dim).
+            k (torch.Tensor): Key tensor with shape (B, N_k, embedding_dim).
+            v (torch.Tensor): Value tensor with shape (B, N_k, embedding_dim).
+
+        Returns:
+            (torch.Tensor): Output tensor after attention with shape (B, N_q, embedding_dim).
+        """
+        # Input projections
+        q = self.q_proj(q)
+        k = self.k_proj(k)
+        v = self.v_proj(v)
+
+        # Separate into heads
+        q = self._separate_heads(q, self.num_heads)
+        k = self._separate_heads(k, self.num_heads)
+        v = self._separate_heads(v, self.num_heads)
+
+        # Attention
+        _, _, _, c_per_head = q.shape
+        attn = q @ k.permute(0, 1, 3, 2)  # B x N_heads x N_tokens x N_tokens
+        attn = attn / math.sqrt(c_per_head)
+        attn = torch.softmax(attn, dim=-1)
+
+        # Get output
+        out = attn @ v
+        out = self._recombine_heads(out)
+        return self.out_proj(out)

ultralytics/models/sam/modules/utils.py

@@ -0,0 +1,388 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+import torch.nn.functional as F
+
+
+def select_closest_cond_frames(frame_idx: int, cond_frame_outputs: dict[int, Any], max_cond_frame_num: int):
+    """
+    Select the closest conditioning frames to a given frame index.
+
+    Args:
+        frame_idx (int): Current frame index.
+        cond_frame_outputs (dict[int, Any]): Dictionary of conditioning frame outputs keyed by frame indices.
+        max_cond_frame_num (int): Maximum number of conditioning frames to select.
+
+    Returns:
+        selected_outputs (dict[int, Any]): Selected items from cond_frame_outputs.
+        unselected_outputs (dict[int, Any]): Items not selected from cond_frame_outputs.
+
+    Examples:
+        >>> frame_idx = 5
+        >>> cond_frame_outputs = {1: "a", 3: "b", 7: "c", 9: "d"}
+        >>> max_cond_frame_num = 2
+        >>> selected, unselected = select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num)
+        >>> print(selected)
+        {3: 'b', 7: 'c'}
+        >>> print(unselected)
+        {1: 'a', 9: 'd'}
+    """
+    if max_cond_frame_num == -1 or len(cond_frame_outputs) <= max_cond_frame_num:
+        selected_outputs = cond_frame_outputs
+        unselected_outputs = {}
+    else:
+        assert max_cond_frame_num >= 2, "we should allow using 2+ conditioning frames"
+        selected_outputs = {}
+
+        # The closest conditioning frame before `frame_idx` (if any)
+        idx_before = max((t for t in cond_frame_outputs if t < frame_idx), default=None)
+        if idx_before is not None:
+            selected_outputs[idx_before] = cond_frame_outputs[idx_before]
+
+        # The closest conditioning frame after `frame_idx` (if any)
+        idx_after = min((t for t in cond_frame_outputs if t >= frame_idx), default=None)
+        if idx_after is not None:
+            selected_outputs[idx_after] = cond_frame_outputs[idx_after]
+
+        # Add other temporally closest conditioning frames until reaching a total
+        # of `max_cond_frame_num` conditioning frames.
+        num_remain = max_cond_frame_num - len(selected_outputs)
+        inds_remain = sorted(
+            (t for t in cond_frame_outputs if t not in selected_outputs),
+            key=lambda x: abs(x - frame_idx),
+        )[:num_remain]
+        selected_outputs.update((t, cond_frame_outputs[t]) for t in inds_remain)
+        unselected_outputs = {t: v for t, v in cond_frame_outputs.items() if t not in selected_outputs}
+
+    return selected_outputs, unselected_outputs
+
+
+def get_1d_sine_pe(pos_inds: torch.Tensor, dim: int, temperature: float = 10000):
+    """
+    Generate 1D sinusoidal positional embeddings for given positions and dimensions.
+
+    Args:
+        pos_inds (torch.Tensor): Position indices for which to generate embeddings.
+        dim (int): Dimension of the positional embeddings. Should be an even number.
+        temperature (float, optional): Scaling factor for the frequency of the sinusoidal functions.
+
+    Returns:
+        (torch.Tensor): Sinusoidal positional embeddings with shape (pos_inds.shape, dim).
+
+    Examples:
+        >>> pos = torch.tensor([0, 1, 2, 3])
+        >>> embeddings = get_1d_sine_pe(pos, 128)
+        >>> embeddings.shape
+        torch.Size([4, 128])
+    """
+    pe_dim = dim // 2
+    dim_t = torch.arange(pe_dim, dtype=pos_inds.dtype, device=pos_inds.device)
+    dim_t = temperature ** (2 * (dim_t // 2) / pe_dim)
+
+    pos_embed = pos_inds.unsqueeze(-1) / dim_t
+    pos_embed = torch.cat([pos_embed.sin(), pos_embed.cos()], dim=-1)
+    return pos_embed
+
+
+def init_t_xy(end_x: int, end_y: int):
+    """
+    Initialize 1D and 2D coordinate tensors for a grid of specified dimensions.
+
+    This function creates coordinate tensors for a grid with dimensions end_x × end_y. It generates a linear index tensor
+    and corresponding x and y coordinate tensors.
+
+    Args:
+        end_x (int): Width of the grid (number of columns).
+        end_y (int): Height of the grid (number of rows).
+
+    Returns:
+        t_x (torch.Tensor): X-coordinates for each position, with shape (end_x * end_y).
+        t_y (torch.Tensor): Y-coordinates for each position, with shape (end_x * end_y).
+
+    Examples:
+        >>> t_x, t_y = init_t_xy(3, 2)
+        >>> print(t_x)
+        tensor([0., 1., 2., 0., 1., 2.])
+        >>> print(t_y)
+        tensor([0., 0., 0., 1., 1., 1.])
+    """
+    t = torch.arange(end_x * end_y, dtype=torch.float32)
+    t_x = (t % end_x).float()
+    t_y = torch.div(t, end_x, rounding_mode="floor").float()
+    return t_x, t_y
+
+
+def compute_axial_cis(dim: int, end_x: int, end_y: int, theta: float = 10000.0):
+    """
+    Compute axial complex exponential positional encodings for 2D spatial positions in a grid.
+
+    This function generates complex exponential positional encodings for a 2D grid of spatial positions,
+    using separate frequency components for the x and y dimensions.
+
+    Args:
+        dim (int): Dimension of the positional encoding.
+        end_x (int): Width of the 2D grid.
+        end_y (int): Height of the 2D grid.
+        theta (float, optional): Scaling factor for frequency computation.
+
+    Returns:
+        (torch.Tensor): Complex exponential positional encodings with shape (end_x*end_y, dim//2).
+
+    Examples:
+        >>> dim, end_x, end_y = 128, 8, 8
+        >>> freqs_cis = compute_axial_cis(dim, end_x, end_y)
+        >>> freqs_cis.shape
+        torch.Size([64, 64])
+    """
+    freqs_x = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim))
+    freqs_y = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim))
+
+    t_x, t_y = init_t_xy(end_x, end_y)
+    freqs_x = torch.outer(t_x, freqs_x)
+    freqs_y = torch.outer(t_y, freqs_y)
+    freqs_cis_x = torch.polar(torch.ones_like(freqs_x), freqs_x)
+    freqs_cis_y = torch.polar(torch.ones_like(freqs_y), freqs_y)
+    return torch.cat([freqs_cis_x, freqs_cis_y], dim=-1)
+
+
+def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
+    """
+    Reshape frequency tensor for broadcasting with input tensor.
+
+    Reshapes a frequency tensor to ensure dimensional compatibility for broadcasting with an input tensor.
+    This function is typically used in positional encoding operations.
+
+    Args:
+        freqs_cis (torch.Tensor): Frequency tensor with shape matching the last two dimensions of x.
+        x (torch.Tensor): Input tensor to broadcast with.
+
+    Returns:
+        (torch.Tensor): Reshaped frequency tensor ready for broadcasting with the input tensor.
+
+    Raises:
+        AssertionError: If the shape of freqs_cis doesn't match the last two dimensions of x.
+    """
+    ndim = x.ndim
+    assert 0 <= 1 < ndim
+    assert freqs_cis.shape == (x.shape[-2], x.shape[-1])
+    shape = [d if i >= ndim - 2 else 1 for i, d in enumerate(x.shape)]
+    return freqs_cis.view(*shape)
+
+
+def apply_rotary_enc(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    freqs_cis: torch.Tensor,
+    repeat_freqs_k: bool = False,
+):
+    """
+    Apply rotary positional encoding to query and key tensors.
+
+    This function applies rotary positional encoding (RoPE) to query and key tensors using complex-valued frequency
+    components. RoPE is a technique that injects relative position information into self-attention mechanisms.
+
+    Args:
+        xq (torch.Tensor): Query tensor to encode with positional information.
+        xk (torch.Tensor): Key tensor to encode with positional information.
+        freqs_cis (torch.Tensor): Complex-valued frequency components for rotary encoding with shape matching the
+            last two dimensions of xq.
+        repeat_freqs_k (bool, optional): Whether to repeat frequency components along sequence length dimension
+            to match key sequence length.
+
+    Returns:
+        xq_out (torch.Tensor): Query tensor with rotary positional encoding applied.
+        xk_out (torch.Tensor): Key tensor with rotary positional encoding applied, or original xk if xk is empty.
+
+    Examples:
+        >>> import torch
+        >>> xq = torch.randn(2, 8, 16, 64)  # [batch, heads, seq_len, dim]
+        >>> xk = torch.randn(2, 8, 16, 64)
+        >>> freqs_cis = compute_axial_cis(64, 4, 4)  # For a 4x4 spatial grid with dim=64
+        >>> q_encoded, k_encoded = apply_rotary_enc(xq, xk, freqs_cis)
+    """
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) if xk.shape[-2] != 0 else None
+    freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
+    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
+    if xk_ is None:
+        # No keys to rotate, due to dropout
+        return xq_out.type_as(xq).to(xq.device), xk
+    # Repeat freqs along seq_len dim to match k seq_len
+    if repeat_freqs_k:
+        r = xk_.shape[-2] // xq_.shape[-2]
+        freqs_cis = freqs_cis.repeat(*([1] * (freqs_cis.ndim - 2)), r, 1)
+    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
+    return xq_out.type_as(xq).to(xq.device), xk_out.type_as(xk).to(xk.device)
+
+
+def window_partition(x: torch.Tensor, window_size: int):
+    """
+    Partition input tensor into non-overlapping windows with padding if needed.
+
+    Args:
+        x (torch.Tensor): Input tensor with shape (B, H, W, C).
+        window_size (int): Size of each window.
+
+    Returns:
+        windows (torch.Tensor): Partitioned windows with shape (B * num_windows, window_size, window_size, C).
+        padded_h_w (tuple[int, int]): Padded height and width before partition.
+
+    Examples:
+        >>> x = torch.randn(1, 16, 16, 3)
+        >>> windows, (Hp, Wp) = window_partition(x, window_size=4)
+        >>> print(windows.shape, Hp, Wp)
+        torch.Size([16, 4, 4, 3]) 16 16
+    """
+    B, H, W, C = x.shape
+
+    pad_h = (window_size - H % window_size) % window_size
+    pad_w = (window_size - W % window_size) % window_size
+    if pad_h > 0 or pad_w > 0:
+        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
+    Hp, Wp = H + pad_h, W + pad_w
+
+    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows, (Hp, Wp)
+
+
+def window_unpartition(windows: torch.Tensor, window_size: int, pad_hw: tuple[int, int], hw: tuple[int, int]):
+    """
+    Unpartition windowed sequences into original sequences and remove padding.
+
+    This function reverses the windowing process, reconstructing the original input from windowed segments
+    and removing any padding that was added during the windowing process.
+
+    Args:
+        windows (torch.Tensor): Input tensor of windowed sequences with shape (B * num_windows, window_size,
+            window_size, C), where B is the batch size, num_windows is the number of windows, window_size is
+            the size of each window, and C is the number of channels.
+        window_size (int): Size of each window.
+        pad_hw (tuple[int, int]): Padded height and width (Hp, Wp) of the input before windowing.
+        hw (tuple[int, int]): Original height and width (H, W) of the input before padding and windowing.
+
+    Returns:
+        (torch.Tensor): Unpartitioned sequences with shape (B, H, W, C), where B is the batch size, H and W
+            are the original height and width, and C is the number of channels.
+
+    Examples:
+        >>> windows = torch.rand(32, 8, 8, 64)  # 32 windows of size 8x8 with 64 channels
+        >>> pad_hw = (16, 16)  # Padded height and width
+        >>> hw = (15, 14)  # Original height and width
+        >>> x = window_unpartition(windows, window_size=8, pad_hw=pad_hw, hw=hw)
+        >>> print(x.shape)
+        torch.Size([1, 15, 14, 64])
+    """
+    Hp, Wp = pad_hw
+    H, W = hw
+    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
+    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
+
+    if Hp > H or Wp > W:
+        x = x[:, :H, :W, :].contiguous()
+    return x
+
+
+def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
+    """
+    Extract relative positional embeddings based on query and key sizes.
+
+    Args:
+        q_size (int): Size of the query.
+        k_size (int): Size of the key.
+        rel_pos (torch.Tensor): Relative position embeddings with shape (L, C), where L is the maximum relative
+            distance and C is the embedding dimension.
+
+    Returns:
+        (torch.Tensor): Extracted positional embeddings according to relative positions, with shape (q_size,
+            k_size, C).
+
+    Examples:
+        >>> q_size, k_size = 8, 16
+        >>> rel_pos = torch.randn(31, 64)  # 31 = 2 * max(8, 16) - 1
+        >>> extracted_pos = get_rel_pos(q_size, k_size, rel_pos)
+        >>> print(extracted_pos.shape)
+        torch.Size([8, 16, 64])
+    """
+    max_rel_dist = int(2 * max(q_size, k_size) - 1)
+    # Interpolate rel pos if needed.
+    if rel_pos.shape[0] != max_rel_dist:
+        # Interpolate rel pos.
+        rel_pos_resized = F.interpolate(
+            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
+            size=max_rel_dist,
+            mode="linear",
+        )
+        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
+    else:
+        rel_pos_resized = rel_pos
+
+    # Scale the coords with short length if shapes for q and k are different.
+    q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
+    k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
+    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
+
+    return rel_pos_resized[relative_coords.long()]
+
+
+def add_decomposed_rel_pos(
+    attn: torch.Tensor,
+    q: torch.Tensor,
+    rel_pos_h: torch.Tensor,
+    rel_pos_w: torch.Tensor,
+    q_size: tuple[int, int],
+    k_size: tuple[int, int],
+) -> torch.Tensor:
+    """
+    Add decomposed Relative Positional Embeddings to the attention map.
+
+    This function calculates and applies decomposed Relative Positional Embeddings as described in the MVITv2
+    paper. It enhances the attention mechanism by incorporating spatial relationships between query and key
+    positions.
+
+    Args:
+        attn (torch.Tensor): Attention map with shape (B, q_h * q_w, k_h * k_w).
+        q (torch.Tensor): Query tensor in the attention layer with shape (B, q_h * q_w, C).
+        rel_pos_h (torch.Tensor): Relative position embeddings for height axis with shape (Lh, C).
+        rel_pos_w (torch.Tensor): Relative position embeddings for width axis with shape (Lw, C).
+        q_size (tuple[int, int]): Spatial sequence size of query q as (q_h, q_w).
+        k_size (tuple[int, int]): Spatial sequence size of key k as (k_h, k_w).
+
+    Returns:
+        (torch.Tensor): Updated attention map with added relative positional embeddings, shape
+            (B, q_h * q_w, k_h * k_w).
+
+    Examples:
+        >>> B, C, q_h, q_w, k_h, k_w = 1, 64, 8, 8, 8, 8
+        >>> attn = torch.rand(B, q_h * q_w, k_h * k_w)
+        >>> q = torch.rand(B, q_h * q_w, C)
+        >>> rel_pos_h = torch.rand(2 * max(q_h, k_h) - 1, C)
+        >>> rel_pos_w = torch.rand(2 * max(q_w, k_w) - 1, C)
+        >>> q_size, k_size = (q_h, q_w), (k_h, k_w)
+        >>> updated_attn = add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size)
+        >>> print(updated_attn.shape)
+        torch.Size([1, 64, 64])
+
+    References:
+        https://github.com/facebookresearch/mvit/blob/main/mvit/models/attention.py
+    """
+    q_h, q_w = q_size
+    k_h, k_w = k_size
+    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
+    Rw = get_rel_pos(q_w, k_w, rel_pos_w)
+
+    B, _, dim = q.shape
+    r_q = q.reshape(B, q_h, q_w, dim)
+    rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
+    rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
+
+    attn = (attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]).view(
+        B, q_h * q_w, k_h * k_w
+    )
+
+    return attn

ultralytics/models/utils/__init__.py

@@ -0,0 +1 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

ultralytics/models/utils/loss.py

@@ -0,0 +1,478 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ultralytics.utils.loss import FocalLoss, VarifocalLoss
+from ultralytics.utils.metrics import bbox_iou
+
+from .ops import HungarianMatcher
+
+
+class DETRLoss(nn.Module):
+    """
+    DETR (DEtection TRansformer) Loss class for calculating various loss components.
+
+    This class computes classification loss, bounding box loss, GIoU loss, and optionally auxiliary losses for the
+    DETR object detection model.
+
+    Attributes:
+        nc (int): Number of classes.
+        loss_gain (dict[str, float]): Coefficients for different loss components.
+        aux_loss (bool): Whether to compute auxiliary losses.
+        use_fl (bool): Whether to use FocalLoss.
+        use_vfl (bool): Whether to use VarifocalLoss.
+        use_uni_match (bool): Whether to use a fixed layer for auxiliary branch label assignment.
+        uni_match_ind (int): Index of fixed layer to use if use_uni_match is True.
+        matcher (HungarianMatcher): Object to compute matching cost and indices.
+        fl (FocalLoss | None): Focal Loss object if use_fl is True, otherwise None.
+        vfl (VarifocalLoss | None): Varifocal Loss object if use_vfl is True, otherwise None.
+        device (torch.device): Device on which tensors are stored.
+    """
+
+    def __init__(
+        self,
+        nc: int = 80,
+        loss_gain: dict[str, float] | None = None,
+        aux_loss: bool = True,
+        use_fl: bool = True,
+        use_vfl: bool = False,
+        use_uni_match: bool = False,
+        uni_match_ind: int = 0,
+        gamma: float = 1.5,
+        alpha: float = 0.25,
+    ):
+        """
+        Initialize DETR loss function with customizable components and gains.
+
+        Uses default loss_gain if not provided. Initializes HungarianMatcher with preset cost gains. Supports auxiliary
+        losses and various loss types.
+
+        Args:
+            nc (int): Number of classes.
+            loss_gain (dict[str, float], optional): Coefficients for different loss components.
+            aux_loss (bool): Whether to use auxiliary losses from each decoder layer.
+            use_fl (bool): Whether to use FocalLoss.
+            use_vfl (bool): Whether to use VarifocalLoss.
+            use_uni_match (bool): Whether to use fixed layer for auxiliary branch label assignment.
+            uni_match_ind (int): Index of fixed layer for uni_match.
+            gamma (float): The focusing parameter that controls how much the loss focuses on hard-to-classify examples.
+            alpha (float): The balancing factor used to address class imbalance.
+        """
+        super().__init__()
+
+        if loss_gain is None:
+            loss_gain = {"class": 1, "bbox": 5, "giou": 2, "no_object": 0.1, "mask": 1, "dice": 1}
+        self.nc = nc
+        self.matcher = HungarianMatcher(cost_gain={"class": 2, "bbox": 5, "giou": 2})
+        self.loss_gain = loss_gain
+        self.aux_loss = aux_loss
+        self.fl = FocalLoss(gamma, alpha) if use_fl else None
+        self.vfl = VarifocalLoss(gamma, alpha) if use_vfl else None
+
+        self.use_uni_match = use_uni_match
+        self.uni_match_ind = uni_match_ind
+        self.device = None
+
+    def _get_loss_class(
+        self, pred_scores: torch.Tensor, targets: torch.Tensor, gt_scores: torch.Tensor, num_gts: int, postfix: str = ""
+    ) -> dict[str, torch.Tensor]:
+        """
+        Compute classification loss based on predictions, target values, and ground truth scores.
+
+        Args:
+            pred_scores (torch.Tensor): Predicted class scores with shape (B, N, C).
+            targets (torch.Tensor): Target class indices with shape (B, N).
+            gt_scores (torch.Tensor): Ground truth confidence scores with shape (B, N).
+            num_gts (int): Number of ground truth objects.
+            postfix (str, optional): String to append to the loss name for identification in multi-loss scenarios.
+
+        Returns:
+            (dict[str, torch.Tensor]): Dictionary containing classification loss value.
+
+        Notes:
+            The function supports different classification loss types:
+            - Varifocal Loss (if self.vfl is True and num_gts > 0)
+            - Focal Loss (if self.fl is True)
+            - BCE Loss (default fallback)
+        """
+        # Logits: [b, query, num_classes], gt_class: list[[n, 1]]
+        name_class = f"loss_class{postfix}"
+        bs, nq = pred_scores.shape[:2]
+        # one_hot = F.one_hot(targets, self.nc + 1)[..., :-1]  # (bs, num_queries, num_classes)
+        one_hot = torch.zeros((bs, nq, self.nc + 1), dtype=torch.int64, device=targets.device)
+        one_hot.scatter_(2, targets.unsqueeze(-1), 1)
+        one_hot = one_hot[..., :-1]
+        gt_scores = gt_scores.view(bs, nq, 1) * one_hot
+
+        if self.fl:
+            if num_gts and self.vfl:
+                loss_cls = self.vfl(pred_scores, gt_scores, one_hot)
+            else:
+                loss_cls = self.fl(pred_scores, one_hot.float())
+            loss_cls /= max(num_gts, 1) / nq
+        else:
+            loss_cls = nn.BCEWithLogitsLoss(reduction="none")(pred_scores, gt_scores).mean(1).sum()  # YOLO CLS loss
+
+        return {name_class: loss_cls.squeeze() * self.loss_gain["class"]}
+
+    def _get_loss_bbox(
+        self, pred_bboxes: torch.Tensor, gt_bboxes: torch.Tensor, postfix: str = ""
+    ) -> dict[str, torch.Tensor]:
+        """
+        Compute bounding box and GIoU losses for predicted and ground truth bounding boxes.
+
+        Args:
+            pred_bboxes (torch.Tensor): Predicted bounding boxes with shape (N, 4).
+            gt_bboxes (torch.Tensor): Ground truth bounding boxes with shape (N, 4).
+            postfix (str, optional): String to append to the loss names for identification in multi-loss scenarios.
+
+        Returns:
+            (dict[str, torch.Tensor]): Dictionary containing:
+                - loss_bbox{postfix}: L1 loss between predicted and ground truth boxes, scaled by the bbox loss gain.
+                - loss_giou{postfix}: GIoU loss between predicted and ground truth boxes, scaled by the giou loss gain.
+
+        Notes:
+            If no ground truth boxes are provided (empty list), zero-valued tensors are returned for both losses.
+        """
+        # Boxes: [b, query, 4], gt_bbox: list[[n, 4]]
+        name_bbox = f"loss_bbox{postfix}"
+        name_giou = f"loss_giou{postfix}"
+
+        loss = {}
+        if len(gt_bboxes) == 0:
+            loss[name_bbox] = torch.tensor(0.0, device=self.device)
+            loss[name_giou] = torch.tensor(0.0, device=self.device)
+            return loss
+
+        loss[name_bbox] = self.loss_gain["bbox"] * F.l1_loss(pred_bboxes, gt_bboxes, reduction="sum") / len(gt_bboxes)
+        loss[name_giou] = 1.0 - bbox_iou(pred_bboxes, gt_bboxes, xywh=True, GIoU=True)
+        loss[name_giou] = loss[name_giou].sum() / len(gt_bboxes)
+        loss[name_giou] = self.loss_gain["giou"] * loss[name_giou]
+        return {k: v.squeeze() for k, v in loss.items()}
+
+    # This function is for future RT-DETR Segment models
+    # def _get_loss_mask(self, masks, gt_mask, match_indices, postfix=''):
+    #     # masks: [b, query, h, w], gt_mask: list[[n, H, W]]
+    #     name_mask = f'loss_mask{postfix}'
+    #     name_dice = f'loss_dice{postfix}'
+    #
+    #     loss = {}
+    #     if sum(len(a) for a in gt_mask) == 0:
+    #         loss[name_mask] = torch.tensor(0., device=self.device)
+    #         loss[name_dice] = torch.tensor(0., device=self.device)
+    #         return loss
+    #
+    #     num_gts = len(gt_mask)
+    #     src_masks, target_masks = self._get_assigned_bboxes(masks, gt_mask, match_indices)
+    #     src_masks = F.interpolate(src_masks.unsqueeze(0), size=target_masks.shape[-2:], mode='bilinear')[0]
+    #     # TODO: torch does not have `sigmoid_focal_loss`, but it's not urgent since we don't use mask branch for now.
+    #     loss[name_mask] = self.loss_gain['mask'] * F.sigmoid_focal_loss(src_masks, target_masks,
+    #                                                                     torch.tensor([num_gts], dtype=torch.float32))
+    #     loss[name_dice] = self.loss_gain['dice'] * self._dice_loss(src_masks, target_masks, num_gts)
+    #     return loss
+
+    # This function is for future RT-DETR Segment models
+    # @staticmethod
+    # def _dice_loss(inputs, targets, num_gts):
+    #     inputs = F.sigmoid(inputs).flatten(1)
+    #     targets = targets.flatten(1)
+    #     numerator = 2 * (inputs * targets).sum(1)
+    #     denominator = inputs.sum(-1) + targets.sum(-1)
+    #     loss = 1 - (numerator + 1) / (denominator + 1)
+    #     return loss.sum() / num_gts
+
+    def _get_loss_aux(
+        self,
+        pred_bboxes: torch.Tensor,
+        pred_scores: torch.Tensor,
+        gt_bboxes: torch.Tensor,
+        gt_cls: torch.Tensor,
+        gt_groups: list[int],
+        match_indices: list[tuple] | None = None,
+        postfix: str = "",
+        masks: torch.Tensor | None = None,
+        gt_mask: torch.Tensor | None = None,
+    ) -> dict[str, torch.Tensor]:
+        """
+        Get auxiliary losses for intermediate decoder layers.
+
+        Args:
+            pred_bboxes (torch.Tensor): Predicted bounding boxes from auxiliary layers.
+            pred_scores (torch.Tensor): Predicted scores from auxiliary layers.
+            gt_bboxes (torch.Tensor): Ground truth bounding boxes.
+            gt_cls (torch.Tensor): Ground truth classes.
+            gt_groups (list[int]): Number of ground truths per image.
+            match_indices (list[tuple], optional): Pre-computed matching indices.
+            postfix (str, optional): String to append to loss names.
+            masks (torch.Tensor, optional): Predicted masks if using segmentation.
+            gt_mask (torch.Tensor, optional): Ground truth masks if using segmentation.
+
+        Returns:
+            (dict[str, torch.Tensor]): Dictionary of auxiliary losses.
+        """
+        # NOTE: loss class, bbox, giou, mask, dice
+        loss = torch.zeros(5 if masks is not None else 3, device=pred_bboxes.device)
+        if match_indices is None and self.use_uni_match:
+            match_indices = self.matcher(
+                pred_bboxes[self.uni_match_ind],
+                pred_scores[self.uni_match_ind],
+                gt_bboxes,
+                gt_cls,
+                gt_groups,
+                masks=masks[self.uni_match_ind] if masks is not None else None,
+                gt_mask=gt_mask,
+            )
+        for i, (aux_bboxes, aux_scores) in enumerate(zip(pred_bboxes, pred_scores)):
+            aux_masks = masks[i] if masks is not None else None
+            loss_ = self._get_loss(
+                aux_bboxes,
+                aux_scores,
+                gt_bboxes,
+                gt_cls,
+                gt_groups,
+                masks=aux_masks,
+                gt_mask=gt_mask,
+                postfix=postfix,
+                match_indices=match_indices,
+            )
+            loss[0] += loss_[f"loss_class{postfix}"]
+            loss[1] += loss_[f"loss_bbox{postfix}"]
+            loss[2] += loss_[f"loss_giou{postfix}"]
+            # if masks is not None and gt_mask is not None:
+            #     loss_ = self._get_loss_mask(aux_masks, gt_mask, match_indices, postfix)
+            #     loss[3] += loss_[f'loss_mask{postfix}']
+            #     loss[4] += loss_[f'loss_dice{postfix}']
+
+        loss = {
+            f"loss_class_aux{postfix}": loss[0],
+            f"loss_bbox_aux{postfix}": loss[1],
+            f"loss_giou_aux{postfix}": loss[2],
+        }
+        # if masks is not None and gt_mask is not None:
+        #     loss[f'loss_mask_aux{postfix}'] = loss[3]
+        #     loss[f'loss_dice_aux{postfix}'] = loss[4]
+        return loss
+
+    @staticmethod
+    def _get_index(match_indices: list[tuple]) -> tuple[tuple[torch.Tensor, torch.Tensor], torch.Tensor]:
+        """
+        Extract batch indices, source indices, and destination indices from match indices.
+
+        Args:
+            match_indices (list[tuple]): List of tuples containing matched indices.
+
+        Returns:
+            batch_idx (tuple[torch.Tensor, torch.Tensor]): Tuple containing (batch_idx, src_idx).
+            dst_idx (torch.Tensor): Destination indices.
+        """
+        batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(match_indices)])
+        src_idx = torch.cat([src for (src, _) in match_indices])
+        dst_idx = torch.cat([dst for (_, dst) in match_indices])
+        return (batch_idx, src_idx), dst_idx
+
+    def _get_assigned_bboxes(
+        self, pred_bboxes: torch.Tensor, gt_bboxes: torch.Tensor, match_indices: list[tuple]
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Assign predicted bounding boxes to ground truth bounding boxes based on match indices.
+
+        Args:
+            pred_bboxes (torch.Tensor): Predicted bounding boxes.
+            gt_bboxes (torch.Tensor): Ground truth bounding boxes.
+            match_indices (list[tuple]): List of tuples containing matched indices.
+
+        Returns:
+            pred_assigned (torch.Tensor): Assigned predicted bounding boxes.
+            gt_assigned (torch.Tensor): Assigned ground truth bounding boxes.
+        """
+        pred_assigned = torch.cat(
+            [
+                t[i] if len(i) > 0 else torch.zeros(0, t.shape[-1], device=self.device)
+                for t, (i, _) in zip(pred_bboxes, match_indices)
+            ]
+        )
+        gt_assigned = torch.cat(
+            [
+                t[j] if len(j) > 0 else torch.zeros(0, t.shape[-1], device=self.device)
+                for t, (_, j) in zip(gt_bboxes, match_indices)
+            ]
+        )
+        return pred_assigned, gt_assigned
+
+    def _get_loss(
+        self,
+        pred_bboxes: torch.Tensor,
+        pred_scores: torch.Tensor,
+        gt_bboxes: torch.Tensor,
+        gt_cls: torch.Tensor,
+        gt_groups: list[int],
+        masks: torch.Tensor | None = None,
+        gt_mask: torch.Tensor | None = None,
+        postfix: str = "",
+        match_indices: list[tuple] | None = None,
+    ) -> dict[str, torch.Tensor]:
+        """
+        Calculate losses for a single prediction layer.
+
+        Args:
+            pred_bboxes (torch.Tensor): Predicted bounding boxes.
+            pred_scores (torch.Tensor): Predicted class scores.
+            gt_bboxes (torch.Tensor): Ground truth bounding boxes.
+            gt_cls (torch.Tensor): Ground truth classes.
+            gt_groups (list[int]): Number of ground truths per image.
+            masks (torch.Tensor, optional): Predicted masks if using segmentation.
+            gt_mask (torch.Tensor, optional): Ground truth masks if using segmentation.
+            postfix (str, optional): String to append to loss names.
+            match_indices (list[tuple], optional): Pre-computed matching indices.
+
+        Returns:
+            (dict[str, torch.Tensor]): Dictionary of losses.
+        """
+        if match_indices is None:
+            match_indices = self.matcher(
+                pred_bboxes, pred_scores, gt_bboxes, gt_cls, gt_groups, masks=masks, gt_mask=gt_mask
+            )
+
+        idx, gt_idx = self._get_index(match_indices)
+        pred_bboxes, gt_bboxes = pred_bboxes[idx], gt_bboxes[gt_idx]
+
+        bs, nq = pred_scores.shape[:2]
+        targets = torch.full((bs, nq), self.nc, device=pred_scores.device, dtype=gt_cls.dtype)
+        targets[idx] = gt_cls[gt_idx]
+
+        gt_scores = torch.zeros([bs, nq], device=pred_scores.device)
+        if len(gt_bboxes):
+            gt_scores[idx] = bbox_iou(pred_bboxes.detach(), gt_bboxes, xywh=True).squeeze(-1)
+
+        return {
+            **self._get_loss_class(pred_scores, targets, gt_scores, len(gt_bboxes), postfix),
+            **self._get_loss_bbox(pred_bboxes, gt_bboxes, postfix),
+            # **(self._get_loss_mask(masks, gt_mask, match_indices, postfix) if masks is not None and gt_mask is not None else {})
+        }
+
+    def forward(
+        self,
+        pred_bboxes: torch.Tensor,
+        pred_scores: torch.Tensor,
+        batch: dict[str, Any],
+        postfix: str = "",
+        **kwargs: Any,
+    ) -> dict[str, torch.Tensor]:
+        """
+        Calculate loss for predicted bounding boxes and scores.
+
+        Args:
+            pred_bboxes (torch.Tensor): Predicted bounding boxes, shape (L, B, N, 4).
+            pred_scores (torch.Tensor): Predicted class scores, shape (L, B, N, C).
+            batch (dict[str, Any]): Batch information containing cls, bboxes, and gt_groups.
+            postfix (str, optional): Postfix for loss names.
+            **kwargs (Any): Additional arguments, may include 'match_indices'.
+
+        Returns:
+            (dict[str, torch.Tensor]): Computed losses, including main and auxiliary (if enabled).
+
+        Notes:
+            Uses last elements of pred_bboxes and pred_scores for main loss, and the rest for auxiliary losses if
+            self.aux_loss is True.
+        """
+        self.device = pred_bboxes.device
+        match_indices = kwargs.get("match_indices", None)
+        gt_cls, gt_bboxes, gt_groups = batch["cls"], batch["bboxes"], batch["gt_groups"]
+
+        total_loss = self._get_loss(
+            pred_bboxes[-1], pred_scores[-1], gt_bboxes, gt_cls, gt_groups, postfix=postfix, match_indices=match_indices
+        )
+
+        if self.aux_loss:
+            total_loss.update(
+                self._get_loss_aux(
+                    pred_bboxes[:-1], pred_scores[:-1], gt_bboxes, gt_cls, gt_groups, match_indices, postfix
+                )
+            )
+
+        return total_loss
+
+
+class RTDETRDetectionLoss(DETRLoss):
+    """
+    Real-Time DeepTracker (RT-DETR) Detection Loss class that extends the DETRLoss.
+
+    This class computes the detection loss for the RT-DETR model, which includes the standard detection loss as well as
+    an additional denoising training loss when provided with denoising metadata.
+    """
+
+    def forward(
+        self,
+        preds: tuple[torch.Tensor, torch.Tensor],
+        batch: dict[str, Any],
+        dn_bboxes: torch.Tensor | None = None,
+        dn_scores: torch.Tensor | None = None,
+        dn_meta: dict[str, Any] | None = None,
+    ) -> dict[str, torch.Tensor]:
+        """
+        Forward pass to compute detection loss with optional denoising loss.
+
+        Args:
+            preds (tuple[torch.Tensor, torch.Tensor]): Tuple containing predicted bounding boxes and scores.
+            batch (dict[str, Any]): Batch data containing ground truth information.
+            dn_bboxes (torch.Tensor, optional): Denoising bounding boxes.
+            dn_scores (torch.Tensor, optional): Denoising scores.
+            dn_meta (dict[str, Any], optional): Metadata for denoising.
+
+        Returns:
+            (dict[str, torch.Tensor]): Dictionary containing total loss and denoising loss if applicable.
+        """
+        pred_bboxes, pred_scores = preds
+        total_loss = super().forward(pred_bboxes, pred_scores, batch)
+
+        # Check for denoising metadata to compute denoising training loss
+        if dn_meta is not None:
+            dn_pos_idx, dn_num_group = dn_meta["dn_pos_idx"], dn_meta["dn_num_group"]
+            assert len(batch["gt_groups"]) == len(dn_pos_idx)
+
+            # Get the match indices for denoising
+            match_indices = self.get_dn_match_indices(dn_pos_idx, dn_num_group, batch["gt_groups"])
+
+            # Compute the denoising training loss
+            dn_loss = super().forward(dn_bboxes, dn_scores, batch, postfix="_dn", match_indices=match_indices)
+            total_loss.update(dn_loss)
+        else:
+            # If no denoising metadata is provided, set denoising loss to zero
+            total_loss.update({f"{k}_dn": torch.tensor(0.0, device=self.device) for k in total_loss.keys()})
+
+        return total_loss
+
+    @staticmethod
+    def get_dn_match_indices(
+        dn_pos_idx: list[torch.Tensor], dn_num_group: int, gt_groups: list[int]
+    ) -> list[tuple[torch.Tensor, torch.Tensor]]:
+        """
+        Get match indices for denoising.
+
+        Args:
+            dn_pos_idx (list[torch.Tensor]): List of tensors containing positive indices for denoising.
+            dn_num_group (int): Number of denoising groups.
+            gt_groups (list[int]): List of integers representing number of ground truths per image.
+
+        Returns:
+            (list[tuple[torch.Tensor, torch.Tensor]]): List of tuples containing matched indices for denoising.
+        """
+        dn_match_indices = []
+        idx_groups = torch.as_tensor([0, *gt_groups[:-1]]).cumsum_(0)
+        for i, num_gt in enumerate(gt_groups):
+            if num_gt > 0:
+                gt_idx = torch.arange(end=num_gt, dtype=torch.long) + idx_groups[i]
+                gt_idx = gt_idx.repeat(dn_num_group)
+                assert len(dn_pos_idx[i]) == len(gt_idx), (
+                    f"Expected the same length, but got {len(dn_pos_idx[i])} and {len(gt_idx)} respectively."
+                )
+                dn_match_indices.append((dn_pos_idx[i], gt_idx))
+            else:
+                dn_match_indices.append((torch.zeros([0], dtype=torch.long), torch.zeros([0], dtype=torch.long)))
+        return dn_match_indices

ultralytics/models/utils/ops.py

@@ -0,0 +1,319 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from scipy.optimize import linear_sum_assignment
+
+from ultralytics.utils.metrics import bbox_iou
+from ultralytics.utils.ops import xywh2xyxy, xyxy2xywh
+
+
+class HungarianMatcher(nn.Module):
+    """
+    A module implementing the HungarianMatcher for optimal assignment between predictions and ground truth.
+
+    HungarianMatcher performs optimal bipartite assignment over predicted and ground truth bounding boxes using a cost
+    function that considers classification scores, bounding box coordinates, and optionally mask predictions. This is
+    used in end-to-end object detection models like DETR.
+
+    Attributes:
+        cost_gain (dict[str, float]): Dictionary of cost coefficients for 'class', 'bbox', 'giou', 'mask', and 'dice'
+            components.
+        use_fl (bool): Whether to use Focal Loss for classification cost calculation.
+        with_mask (bool): Whether the model makes mask predictions.
+        num_sample_points (int): Number of sample points used in mask cost calculation.
+        alpha (float): Alpha factor in Focal Loss calculation.
+        gamma (float): Gamma factor in Focal Loss calculation.
+
+    Methods:
+        forward: Compute optimal assignment between predictions and ground truths for a batch.
+        _cost_mask: Compute mask cost and dice cost if masks are predicted.
+
+    Examples:
+        Initialize a HungarianMatcher with custom cost gains
+        >>> matcher = HungarianMatcher(cost_gain={"class": 2, "bbox": 5, "giou": 2})
+
+        Perform matching between predictions and ground truth
+        >>> pred_boxes = torch.rand(2, 100, 4)  # batch_size=2, num_queries=100
+        >>> pred_scores = torch.rand(2, 100, 80)  # 80 classes
+        >>> gt_boxes = torch.rand(10, 4)  # 10 ground truth boxes
+        >>> gt_classes = torch.randint(0, 80, (10,))
+        >>> gt_groups = [5, 5]  # 5 GT boxes per image
+        >>> indices = matcher(pred_boxes, pred_scores, gt_boxes, gt_classes, gt_groups)
+    """
+
+    def __init__(
+        self,
+        cost_gain: dict[str, float] | None = None,
+        use_fl: bool = True,
+        with_mask: bool = False,
+        num_sample_points: int = 12544,
+        alpha: float = 0.25,
+        gamma: float = 2.0,
+    ):
+        """
+        Initialize HungarianMatcher for optimal assignment of predicted and ground truth bounding boxes.
+
+        Args:
+            cost_gain (dict[str, float], optional): Dictionary of cost coefficients for different matching cost
+                components. Should contain keys 'class', 'bbox', 'giou', 'mask', and 'dice'.
+            use_fl (bool): Whether to use Focal Loss for classification cost calculation.
+            with_mask (bool): Whether the model makes mask predictions.
+            num_sample_points (int): Number of sample points used in mask cost calculation.
+            alpha (float): Alpha factor in Focal Loss calculation.
+            gamma (float): Gamma factor in Focal Loss calculation.
+        """
+        super().__init__()
+        if cost_gain is None:
+            cost_gain = {"class": 1, "bbox": 5, "giou": 2, "mask": 1, "dice": 1}
+        self.cost_gain = cost_gain
+        self.use_fl = use_fl
+        self.with_mask = with_mask
+        self.num_sample_points = num_sample_points
+        self.alpha = alpha
+        self.gamma = gamma
+
+    def forward(
+        self,
+        pred_bboxes: torch.Tensor,
+        pred_scores: torch.Tensor,
+        gt_bboxes: torch.Tensor,
+        gt_cls: torch.Tensor,
+        gt_groups: list[int],
+        masks: torch.Tensor | None = None,
+        gt_mask: list[torch.Tensor] | None = None,
+    ) -> list[tuple[torch.Tensor, torch.Tensor]]:
+        """
+        Compute optimal assignment between predictions and ground truth using Hungarian algorithm.
+
+        This method calculates matching costs based on classification scores, bounding box coordinates, and optionally
+        mask predictions, then finds the optimal bipartite assignment between predictions and ground truth.
+
+        Args:
+            pred_bboxes (torch.Tensor): Predicted bounding boxes with shape (batch_size, num_queries, 4).
+            pred_scores (torch.Tensor): Predicted classification scores with shape (batch_size, num_queries,
+                num_classes).
+            gt_bboxes (torch.Tensor): Ground truth bounding boxes with shape (num_gts, 4).
+            gt_cls (torch.Tensor): Ground truth class labels with shape (num_gts,).
+            gt_groups (list[int]): Number of ground truth boxes for each image in the batch.
+            masks (torch.Tensor, optional): Predicted masks with shape (batch_size, num_queries, height, width).
+            gt_mask (list[torch.Tensor], optional): Ground truth masks, each with shape (num_masks, Height, Width).
+
+        Returns:
+            (list[tuple[torch.Tensor, torch.Tensor]]): A list of size batch_size, each element is a tuple
+                (index_i, index_j), where index_i is the tensor of indices of the selected predictions (in order)
+                and index_j is the tensor of indices of the corresponding selected ground truth targets (in order).
+                For each batch element, it holds: len(index_i) = len(index_j) = min(num_queries, num_target_boxes).
+        """
+        bs, nq, nc = pred_scores.shape
+
+        if sum(gt_groups) == 0:
+            return [(torch.tensor([], dtype=torch.long), torch.tensor([], dtype=torch.long)) for _ in range(bs)]
+
+        # Flatten to compute cost matrices in batch format
+        pred_scores = pred_scores.detach().view(-1, nc)
+        pred_scores = F.sigmoid(pred_scores) if self.use_fl else F.softmax(pred_scores, dim=-1)
+        pred_bboxes = pred_bboxes.detach().view(-1, 4)
+
+        # Compute classification cost
+        pred_scores = pred_scores[:, gt_cls]
+        if self.use_fl:
+            neg_cost_class = (1 - self.alpha) * (pred_scores**self.gamma) * (-(1 - pred_scores + 1e-8).log())
+            pos_cost_class = self.alpha * ((1 - pred_scores) ** self.gamma) * (-(pred_scores + 1e-8).log())
+            cost_class = pos_cost_class - neg_cost_class
+        else:
+            cost_class = -pred_scores
+
+        # Compute L1 cost between boxes
+        cost_bbox = (pred_bboxes.unsqueeze(1) - gt_bboxes.unsqueeze(0)).abs().sum(-1)  # (bs*num_queries, num_gt)
+
+        # Compute GIoU cost between boxes, (bs*num_queries, num_gt)
+        cost_giou = 1.0 - bbox_iou(pred_bboxes.unsqueeze(1), gt_bboxes.unsqueeze(0), xywh=True, GIoU=True).squeeze(-1)
+
+        # Combine costs into final cost matrix
+        C = (
+            self.cost_gain["class"] * cost_class
+            + self.cost_gain["bbox"] * cost_bbox
+            + self.cost_gain["giou"] * cost_giou
+        )
+
+        # Add mask costs if available
+        if self.with_mask:
+            C += self._cost_mask(bs, gt_groups, masks, gt_mask)
+
+        # Set invalid values (NaNs and infinities) to 0
+        C[C.isnan() | C.isinf()] = 0.0
+
+        C = C.view(bs, nq, -1).cpu()
+        indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(gt_groups, -1))]
+        gt_groups = torch.as_tensor([0, *gt_groups[:-1]]).cumsum_(0)  # (idx for queries, idx for gt)
+        return [
+            (torch.tensor(i, dtype=torch.long), torch.tensor(j, dtype=torch.long) + gt_groups[k])
+            for k, (i, j) in enumerate(indices)
+        ]
+
+    # This function is for future RT-DETR Segment models
+    # def _cost_mask(self, bs, num_gts, masks=None, gt_mask=None):
+    #     assert masks is not None and gt_mask is not None, 'Make sure the input has `mask` and `gt_mask`'
+    #     # all masks share the same set of points for efficient matching
+    #     sample_points = torch.rand([bs, 1, self.num_sample_points, 2])
+    #     sample_points = 2.0 * sample_points - 1.0
+    #
+    #     out_mask = F.grid_sample(masks.detach(), sample_points, align_corners=False).squeeze(-2)
+    #     out_mask = out_mask.flatten(0, 1)
+    #
+    #     tgt_mask = torch.cat(gt_mask).unsqueeze(1)
+    #     sample_points = torch.cat([a.repeat(b, 1, 1, 1) for a, b in zip(sample_points, num_gts) if b > 0])
+    #     tgt_mask = F.grid_sample(tgt_mask, sample_points, align_corners=False).squeeze([1, 2])
+    #
+    #     with torch.amp.autocast("cuda", enabled=False):
+    #         # binary cross entropy cost
+    #         pos_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.ones_like(out_mask), reduction='none')
+    #         neg_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.zeros_like(out_mask), reduction='none')
+    #         cost_mask = torch.matmul(pos_cost_mask, tgt_mask.T) + torch.matmul(neg_cost_mask, 1 - tgt_mask.T)
+    #         cost_mask /= self.num_sample_points
+    #
+    #         # dice cost
+    #         out_mask = F.sigmoid(out_mask)
+    #         numerator = 2 * torch.matmul(out_mask, tgt_mask.T)
+    #         denominator = out_mask.sum(-1, keepdim=True) + tgt_mask.sum(-1).unsqueeze(0)
+    #         cost_dice = 1 - (numerator + 1) / (denominator + 1)
+    #
+    #         C = self.cost_gain['mask'] * cost_mask + self.cost_gain['dice'] * cost_dice
+    #     return C
+
+
+def get_cdn_group(
+    batch: dict[str, Any],
+    num_classes: int,
+    num_queries: int,
+    class_embed: torch.Tensor,
+    num_dn: int = 100,
+    cls_noise_ratio: float = 0.5,
+    box_noise_scale: float = 1.0,
+    training: bool = False,
+) -> tuple[torch.Tensor | None, torch.Tensor | None, torch.Tensor | None, dict[str, Any] | None]:
+    """
+    Generate contrastive denoising training group with positive and negative samples from ground truths.
+
+    This function creates denoising queries for contrastive denoising training by adding noise to ground truth
+    bounding boxes and class labels. It generates both positive and negative samples to improve model robustness.
+
+    Args:
+        batch (dict[str, Any]): Batch dictionary containing 'gt_cls' (torch.Tensor with shape (num_gts,)),
+            'gt_bboxes' (torch.Tensor with shape (num_gts, 4)), and 'gt_groups' (list[int]) indicating number of
+            ground truths per image.
+        num_classes (int): Total number of object classes.
+        num_queries (int): Number of object queries.
+        class_embed (torch.Tensor): Class embedding weights to map labels to embedding space.
+        num_dn (int): Number of denoising queries to generate.
+        cls_noise_ratio (float): Noise ratio for class labels.
+        box_noise_scale (float): Noise scale for bounding box coordinates.
+        training (bool): Whether model is in training mode.
+
+    Returns:
+        padding_cls (torch.Tensor | None): Modified class embeddings for denoising with shape (bs, num_dn, embed_dim).
+        padding_bbox (torch.Tensor | None): Modified bounding boxes for denoising with shape (bs, num_dn, 4).
+        attn_mask (torch.Tensor | None): Attention mask for denoising with shape (tgt_size, tgt_size).
+        dn_meta (dict[str, Any] | None): Meta information dictionary containing denoising parameters.
+
+    Examples:
+        Generate denoising group for training
+        >>> batch = {
+        ...     "cls": torch.tensor([0, 1, 2]),
+        ...     "bboxes": torch.rand(3, 4),
+        ...     "batch_idx": torch.tensor([0, 0, 1]),
+        ...     "gt_groups": [2, 1],
+        ... }
+        >>> class_embed = torch.rand(80, 256)  # 80 classes, 256 embedding dim
+        >>> cdn_outputs = get_cdn_group(batch, 80, 100, class_embed, training=True)
+    """
+    if (not training) or num_dn <= 0 or batch is None:
+        return None, None, None, None
+    gt_groups = batch["gt_groups"]
+    total_num = sum(gt_groups)
+    max_nums = max(gt_groups)
+    if max_nums == 0:
+        return None, None, None, None
+
+    num_group = num_dn // max_nums
+    num_group = 1 if num_group == 0 else num_group
+    # Pad gt to max_num of a batch
+    bs = len(gt_groups)
+    gt_cls = batch["cls"]  # (bs*num, )
+    gt_bbox = batch["bboxes"]  # bs*num, 4
+    b_idx = batch["batch_idx"]
+
+    # Each group has positive and negative queries
+    dn_cls = gt_cls.repeat(2 * num_group)  # (2*num_group*bs*num, )
+    dn_bbox = gt_bbox.repeat(2 * num_group, 1)  # 2*num_group*bs*num, 4
+    dn_b_idx = b_idx.repeat(2 * num_group).view(-1)  # (2*num_group*bs*num, )
+
+    # Positive and negative mask
+    # (bs*num*num_group, ), the second total_num*num_group part as negative samples
+    neg_idx = torch.arange(total_num * num_group, dtype=torch.long, device=gt_bbox.device) + num_group * total_num
+
+    if cls_noise_ratio > 0:
+        # Apply class label noise to half of the samples
+        mask = torch.rand(dn_cls.shape) < (cls_noise_ratio * 0.5)
+        idx = torch.nonzero(mask).squeeze(-1)
+        # Randomly assign new class labels
+        new_label = torch.randint_like(idx, 0, num_classes, dtype=dn_cls.dtype, device=dn_cls.device)
+        dn_cls[idx] = new_label
+
+    if box_noise_scale > 0:
+        known_bbox = xywh2xyxy(dn_bbox)
+
+        diff = (dn_bbox[..., 2:] * 0.5).repeat(1, 2) * box_noise_scale  # 2*num_group*bs*num, 4
+
+        rand_sign = torch.randint_like(dn_bbox, 0, 2) * 2.0 - 1.0
+        rand_part = torch.rand_like(dn_bbox)
+        rand_part[neg_idx] += 1.0
+        rand_part *= rand_sign
+        known_bbox += rand_part * diff
+        known_bbox.clip_(min=0.0, max=1.0)
+        dn_bbox = xyxy2xywh(known_bbox)
+        dn_bbox = torch.logit(dn_bbox, eps=1e-6)  # inverse sigmoid
+
+    num_dn = int(max_nums * 2 * num_group)  # total denoising queries
+    dn_cls_embed = class_embed[dn_cls]  # bs*num * 2 * num_group, 256
+    padding_cls = torch.zeros(bs, num_dn, dn_cls_embed.shape[-1], device=gt_cls.device)
+    padding_bbox = torch.zeros(bs, num_dn, 4, device=gt_bbox.device)
+
+    map_indices = torch.cat([torch.tensor(range(num), dtype=torch.long) for num in gt_groups])
+    pos_idx = torch.stack([map_indices + max_nums * i for i in range(num_group)], dim=0)
+
+    map_indices = torch.cat([map_indices + max_nums * i for i in range(2 * num_group)])
+    padding_cls[(dn_b_idx, map_indices)] = dn_cls_embed
+    padding_bbox[(dn_b_idx, map_indices)] = dn_bbox
+
+    tgt_size = num_dn + num_queries
+    attn_mask = torch.zeros([tgt_size, tgt_size], dtype=torch.bool)
+    # Match query cannot see the reconstruct
+    attn_mask[num_dn:, :num_dn] = True
+    # Reconstruct cannot see each other
+    for i in range(num_group):
+        if i == 0:
+            attn_mask[max_nums * 2 * i : max_nums * 2 * (i + 1), max_nums * 2 * (i + 1) : num_dn] = True
+        if i == num_group - 1:
+            attn_mask[max_nums * 2 * i : max_nums * 2 * (i + 1), : max_nums * i * 2] = True
+        else:
+            attn_mask[max_nums * 2 * i : max_nums * 2 * (i + 1), max_nums * 2 * (i + 1) : num_dn] = True
+            attn_mask[max_nums * 2 * i : max_nums * 2 * (i + 1), : max_nums * 2 * i] = True
+    dn_meta = {
+        "dn_pos_idx": [p.reshape(-1) for p in pos_idx.cpu().split(list(gt_groups), dim=1)],
+        "dn_num_group": num_group,
+        "dn_num_split": [num_dn, num_queries],
+    }
+
+    return (
+        padding_cls.to(class_embed.device),
+        padding_bbox.to(class_embed.device),
+        attn_mask.to(class_embed.device),
+        dn_meta,
+    )

ultralytics/models/yolo/__init__.py

@@ -0,0 +1,7 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from ultralytics.models.yolo import classify, detect, obb, pose, segment, world, yoloe
+
+from .model import YOLO, YOLOE, YOLOWorld
+
+__all__ = "classify", "segment", "detect", "pose", "obb", "world", "yoloe", "YOLO", "YOLOWorld", "YOLOE"

ultralytics/models/yolo/classify/__init__.py

@@ -0,0 +1,7 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from ultralytics.models.yolo.classify.predict import ClassificationPredictor
+from ultralytics.models.yolo.classify.train import ClassificationTrainer
+from ultralytics.models.yolo.classify.val import ClassificationValidator
+
+__all__ = "ClassificationPredictor", "ClassificationTrainer", "ClassificationValidator"

ultralytics/models/yolo/classify/predict.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import cv2
+import torch
+from PIL import Image
+
+from ultralytics.data.augment import classify_transforms
+from ultralytics.engine.predictor import BasePredictor
+from ultralytics.engine.results import Results
+from ultralytics.utils import DEFAULT_CFG, ops
+
+
+class ClassificationPredictor(BasePredictor):
+    """
+    A class extending the BasePredictor class for prediction based on a classification model.
+
+    This predictor handles the specific requirements of classification models, including preprocessing images
+    and postprocessing predictions to generate classification results.
+
+    Attributes:
+        args (dict): Configuration arguments for the predictor.
+
+    Methods:
+        preprocess: Convert input images to model-compatible format.
+        postprocess: Process model predictions into Results objects.
+
+    Notes:
+        - Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.
+
+    Examples:
+        >>> from ultralytics.utils import ASSETS
+        >>> from ultralytics.models.yolo.classify import ClassificationPredictor
+        >>> args = dict(model="yolo11n-cls.pt", source=ASSETS)
+        >>> predictor = ClassificationPredictor(overrides=args)
+        >>> predictor.predict_cli()
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """
+        Initialize the ClassificationPredictor with the specified configuration and set task to 'classify'.
+
+        This constructor initializes a ClassificationPredictor instance, which extends BasePredictor for classification
+        tasks. It ensures the task is set to 'classify' regardless of input configuration.
+
+        Args:
+            cfg (dict): Default configuration dictionary containing prediction settings.
+            overrides (dict, optional): Configuration overrides that take precedence over cfg.
+            _callbacks (list, optional): List of callback functions to be executed during prediction.
+        """
+        super().__init__(cfg, overrides, _callbacks)
+        self.args.task = "classify"
+
+    def setup_source(self, source):
+        """Set up source and inference mode and classify transforms."""
+        super().setup_source(source)
+        updated = (
+            self.model.model.transforms.transforms[0].size != max(self.imgsz)
+            if hasattr(self.model.model, "transforms") and hasattr(self.model.model.transforms.transforms[0], "size")
+            else False
+        )
+        self.transforms = (
+            classify_transforms(self.imgsz) if updated or not self.model.pt else self.model.model.transforms
+        )
+
+    def preprocess(self, img):
+        """Convert input images to model-compatible tensor format with appropriate normalization."""
+        if not isinstance(img, torch.Tensor):
+            img = torch.stack(
+                [self.transforms(Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))) for im in img], dim=0
+            )
+        img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
+        return img.half() if self.model.fp16 else img.float()  # Convert uint8 to fp16/32
+
+    def postprocess(self, preds, img, orig_imgs):
+        """
+        Process predictions to return Results objects with classification probabilities.
+
+        Args:
+            preds (torch.Tensor): Raw predictions from the model.
+            img (torch.Tensor): Input images after preprocessing.
+            orig_imgs (list[np.ndarray] | torch.Tensor): Original images before preprocessing.
+
+        Returns:
+            (list[Results]): List of Results objects containing classification results for each image.
+        """
+        if not isinstance(orig_imgs, list):  # Input images are a torch.Tensor, not a list
+            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
+
+        preds = preds[0] if isinstance(preds, (list, tuple)) else preds
+        return [
+            Results(orig_img, path=img_path, names=self.model.names, probs=pred)
+            for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0])
+        ]

ultralytics/models/yolo/classify/train.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from copy import copy
+from typing import Any
+
+import torch
+
+from ultralytics.data import ClassificationDataset, build_dataloader
+from ultralytics.engine.trainer import BaseTrainer
+from ultralytics.models import yolo
+from ultralytics.nn.tasks import ClassificationModel
+from ultralytics.utils import DEFAULT_CFG, LOGGER, RANK
+from ultralytics.utils.plotting import plot_images
+from ultralytics.utils.torch_utils import is_parallel, strip_optimizer, torch_distributed_zero_first
+
+
+class ClassificationTrainer(BaseTrainer):
+    """
+    A trainer class extending BaseTrainer for training image classification models.
+
+    This trainer handles the training process for image classification tasks, supporting both YOLO classification models
+    and torchvision models with comprehensive dataset handling and validation.
+
+    Attributes:
+        model (ClassificationModel): The classification model to be trained.
+        data (dict[str, Any]): Dictionary containing dataset information including class names and number of classes.
+        loss_names (list[str]): Names of the loss functions used during training.
+        validator (ClassificationValidator): Validator instance for model evaluation.
+
+    Methods:
+        set_model_attributes: Set the model's class names from the loaded dataset.
+        get_model: Return a modified PyTorch model configured for training.
+        setup_model: Load, create or download model for classification.
+        build_dataset: Create a ClassificationDataset instance.
+        get_dataloader: Return PyTorch DataLoader with transforms for image preprocessing.
+        preprocess_batch: Preprocess a batch of images and classes.
+        progress_string: Return a formatted string showing training progress.
+        get_validator: Return an instance of ClassificationValidator.
+        label_loss_items: Return a loss dict with labelled training loss items.
+        final_eval: Evaluate trained model and save validation results.
+        plot_training_samples: Plot training samples with their annotations.
+
+    Examples:
+        Initialize and train a classification model
+        >>> from ultralytics.models.yolo.classify import ClassificationTrainer
+        >>> args = dict(model="yolo11n-cls.pt", data="imagenet10", epochs=3)
+        >>> trainer = ClassificationTrainer(overrides=args)
+        >>> trainer.train()
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides: dict[str, Any] | None = None, _callbacks=None):
+        """
+        Initialize a ClassificationTrainer object.
+
+        Args:
+            cfg (dict[str, Any], optional): Default configuration dictionary containing training parameters.
+            overrides (dict[str, Any], optional): Dictionary of parameter overrides for the default configuration.
+            _callbacks (list[Any], optional): List of callback functions to be executed during training.
+        """
+        if overrides is None:
+            overrides = {}
+        overrides["task"] = "classify"
+        if overrides.get("imgsz") is None:
+            overrides["imgsz"] = 224
+        super().__init__(cfg, overrides, _callbacks)
+
+    def set_model_attributes(self):
+        """Set the YOLO model's class names from the loaded dataset."""
+        self.model.names = self.data["names"]
+
+    def get_model(self, cfg=None, weights=None, verbose: bool = True):
+        """
+        Return a modified PyTorch model configured for training YOLO classification.
+
+        Args:
+            cfg (Any, optional): Model configuration.
+            weights (Any, optional): Pre-trained model weights.
+            verbose (bool, optional): Whether to display model information.
+
+        Returns:
+            (ClassificationModel): Configured PyTorch model for classification.
+        """
+        model = ClassificationModel(cfg, nc=self.data["nc"], ch=self.data["channels"], verbose=verbose and RANK == -1)
+        if weights:
+            model.load(weights)
+
+        for m in model.modules():
+            if not self.args.pretrained and hasattr(m, "reset_parameters"):
+                m.reset_parameters()
+            if isinstance(m, torch.nn.Dropout) and self.args.dropout:
+                m.p = self.args.dropout  # set dropout
+        for p in model.parameters():
+            p.requires_grad = True  # for training
+        return model
+
+    def setup_model(self):
+        """
+        Load, create or download model for classification tasks.
+
+        Returns:
+            (Any): Model checkpoint if applicable, otherwise None.
+        """
+        import torchvision  # scope for faster 'import ultralytics'
+
+        if str(self.model) in torchvision.models.__dict__:
+            self.model = torchvision.models.__dict__[self.model](
+                weights="IMAGENET1K_V1" if self.args.pretrained else None
+            )
+            ckpt = None
+        else:
+            ckpt = super().setup_model()
+        ClassificationModel.reshape_outputs(self.model, self.data["nc"])
+        return ckpt
+
+    def build_dataset(self, img_path: str, mode: str = "train", batch=None):
+        """
+        Create a ClassificationDataset instance given an image path and mode.
+
+        Args:
+            img_path (str): Path to the dataset images.
+            mode (str, optional): Dataset mode ('train', 'val', or 'test').
+            batch (Any, optional): Batch information (unused in this implementation).
+
+        Returns:
+            (ClassificationDataset): Dataset for the specified mode.
+        """
+        return ClassificationDataset(root=img_path, args=self.args, augment=mode == "train", prefix=mode)
+
+    def get_dataloader(self, dataset_path: str, batch_size: int = 16, rank: int = 0, mode: str = "train"):
+        """
+        Return PyTorch DataLoader with transforms to preprocess images.
+
+        Args:
+            dataset_path (str): Path to the dataset.
+            batch_size (int, optional): Number of images per batch.
+            rank (int, optional): Process rank for distributed training.
+            mode (str, optional): 'train', 'val', or 'test' mode.
+
+        Returns:
+            (torch.utils.data.DataLoader): DataLoader for the specified dataset and mode.
+        """
+        with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
+            dataset = self.build_dataset(dataset_path, mode)
+
+        loader = build_dataloader(dataset, batch_size, self.args.workers, rank=rank, drop_last=self.args.compile)
+        # Attach inference transforms
+        if mode != "train":
+            if is_parallel(self.model):
+                self.model.module.transforms = loader.dataset.torch_transforms
+            else:
+                self.model.transforms = loader.dataset.torch_transforms
+        return loader
+
+    def preprocess_batch(self, batch: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
+        """Preprocess a batch of images and classes."""
+        batch["img"] = batch["img"].to(self.device, non_blocking=self.device.type == "cuda")
+        batch["cls"] = batch["cls"].to(self.device, non_blocking=self.device.type == "cuda")
+        return batch
+
+    def progress_string(self) -> str:
+        """Return a formatted string showing training progress."""
+        return ("\n" + "%11s" * (4 + len(self.loss_names))) % (
+            "Epoch",
+            "GPU_mem",
+            *self.loss_names,
+            "Instances",
+            "Size",
+        )
+
+    def get_validator(self):
+        """Return an instance of ClassificationValidator for validation."""
+        self.loss_names = ["loss"]
+        return yolo.classify.ClassificationValidator(
+            self.test_loader, self.save_dir, args=copy(self.args), _callbacks=self.callbacks
+        )
+
+    def label_loss_items(self, loss_items: torch.Tensor | None = None, prefix: str = "train"):
+        """
+        Return a loss dict with labelled training loss items tensor.
+
+        Args:
+            loss_items (torch.Tensor, optional): Loss tensor items.
+            prefix (str, optional): Prefix to prepend to loss names.
+
+        Returns:
+            keys (list[str]): List of loss keys if loss_items is None.
+            loss_dict (dict[str, float]): Dictionary of loss items if loss_items is provided.
+        """
+        keys = [f"{prefix}/{x}" for x in self.loss_names]
+        if loss_items is None:
+            return keys
+        loss_items = [round(float(loss_items), 5)]
+        return dict(zip(keys, loss_items))
+
+    def final_eval(self):
+        """Evaluate trained model and save validation results."""
+        for f in self.last, self.best:
+            if f.exists():
+                strip_optimizer(f)  # strip optimizers
+                if f is self.best:
+                    LOGGER.info(f"\nValidating {f}...")
+                    self.validator.args.data = self.args.data
+                    self.validator.args.plots = self.args.plots
+                    self.metrics = self.validator(model=f)
+                    self.metrics.pop("fitness", None)
+                    self.run_callbacks("on_fit_epoch_end")
+
+    def plot_training_samples(self, batch: dict[str, torch.Tensor], ni: int):
+        """
+        Plot training samples with their annotations.
+
+        Args:
+            batch (dict[str, torch.Tensor]): Batch containing images and class labels.
+            ni (int): Number of iterations.
+        """
+        batch["batch_idx"] = torch.arange(batch["img"].shape[0])  # add batch index for plotting
+        plot_images(
+            labels=batch,
+            fname=self.save_dir / f"train_batch{ni}.jpg",
+            on_plot=self.on_plot,
+        )

ultralytics/models/yolo/classify/val.py

@@ -0,0 +1,214 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import torch
+
+from ultralytics.data import ClassificationDataset, build_dataloader
+from ultralytics.engine.validator import BaseValidator
+from ultralytics.utils import LOGGER
+from ultralytics.utils.metrics import ClassifyMetrics, ConfusionMatrix
+from ultralytics.utils.plotting import plot_images
+
+
+class ClassificationValidator(BaseValidator):
+    """
+    A class extending the BaseValidator class for validation based on a classification model.
+
+    This validator handles the validation process for classification models, including metrics calculation,
+    confusion matrix generation, and visualization of results.
+
+    Attributes:
+        targets (list[torch.Tensor]): Ground truth class labels.
+        pred (list[torch.Tensor]): Model predictions.
+        metrics (ClassifyMetrics): Object to calculate and store classification metrics.
+        names (dict): Mapping of class indices to class names.
+        nc (int): Number of classes.
+        confusion_matrix (ConfusionMatrix): Matrix to evaluate model performance across classes.
+
+    Methods:
+        get_desc: Return a formatted string summarizing classification metrics.
+        init_metrics: Initialize confusion matrix, class names, and tracking containers.
+        preprocess: Preprocess input batch by moving data to device.
+        update_metrics: Update running metrics with model predictions and batch targets.
+        finalize_metrics: Finalize metrics including confusion matrix and processing speed.
+        postprocess: Extract the primary prediction from model output.
+        get_stats: Calculate and return a dictionary of metrics.
+        build_dataset: Create a ClassificationDataset instance for validation.
+        get_dataloader: Build and return a data loader for classification validation.
+        print_results: Print evaluation metrics for the classification model.
+        plot_val_samples: Plot validation image samples with their ground truth labels.
+        plot_predictions: Plot images with their predicted class labels.
+
+    Examples:
+        >>> from ultralytics.models.yolo.classify import ClassificationValidator
+        >>> args = dict(model="yolo11n-cls.pt", data="imagenet10")
+        >>> validator = ClassificationValidator(args=args)
+        >>> validator()
+
+    Notes:
+        Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.
+    """
+
+    def __init__(self, dataloader=None, save_dir=None, args=None, _callbacks=None) -> None:
+        """
+        Initialize ClassificationValidator with dataloader, save directory, and other parameters.
+
+        Args:
+            dataloader (torch.utils.data.DataLoader, optional): Dataloader to use for validation.
+            save_dir (str | Path, optional): Directory to save results.
+            args (dict, optional): Arguments containing model and validation configuration.
+            _callbacks (list, optional): List of callback functions to be called during validation.
+
+        Examples:
+            >>> from ultralytics.models.yolo.classify import ClassificationValidator
+            >>> args = dict(model="yolo11n-cls.pt", data="imagenet10")
+            >>> validator = ClassificationValidator(args=args)
+            >>> validator()
+        """
+        super().__init__(dataloader, save_dir, args, _callbacks)
+        self.targets = None
+        self.pred = None
+        self.args.task = "classify"
+        self.metrics = ClassifyMetrics()
+
+    def get_desc(self) -> str:
+        """Return a formatted string summarizing classification metrics."""
+        return ("%22s" + "%11s" * 2) % ("classes", "top1_acc", "top5_acc")
+
+    def init_metrics(self, model: torch.nn.Module) -> None:
+        """Initialize confusion matrix, class names, and tracking containers for predictions and targets."""
+        self.names = model.names
+        self.nc = len(model.names)
+        self.pred = []
+        self.targets = []
+        self.confusion_matrix = ConfusionMatrix(names=model.names)
+
+    def preprocess(self, batch: dict[str, Any]) -> dict[str, Any]:
+        """Preprocess input batch by moving data to device and converting to appropriate dtype."""
+        batch["img"] = batch["img"].to(self.device, non_blocking=self.device.type == "cuda")
+        batch["img"] = batch["img"].half() if self.args.half else batch["img"].float()
+        batch["cls"] = batch["cls"].to(self.device, non_blocking=self.device.type == "cuda")
+        return batch
+
+    def update_metrics(self, preds: torch.Tensor, batch: dict[str, Any]) -> None:
+        """
+        Update running metrics with model predictions and batch targets.
+
+        Args:
+            preds (torch.Tensor): Model predictions, typically logits or probabilities for each class.
+            batch (dict): Batch data containing images and class labels.
+
+        Notes:
+            This method appends the top-N predictions (sorted by confidence in descending order) to the
+            prediction list for later evaluation. N is limited to the minimum of 5 and the number of classes.
+        """
+        n5 = min(len(self.names), 5)
+        self.pred.append(preds.argsort(1, descending=True)[:, :n5].type(torch.int32).cpu())
+        self.targets.append(batch["cls"].type(torch.int32).cpu())
+
+    def finalize_metrics(self) -> None:
+        """
+        Finalize metrics including confusion matrix and processing speed.
+
+        Notes:
+            This method processes the accumulated predictions and targets to generate the confusion matrix,
+            optionally plots it, and updates the metrics object with speed information.
+
+        Examples:
+            >>> validator = ClassificationValidator()
+            >>> validator.pred = [torch.tensor([[0, 1, 2]])]  # Top-3 predictions for one sample
+            >>> validator.targets = [torch.tensor([0])]  # Ground truth class
+            >>> validator.finalize_metrics()
+            >>> print(validator.metrics.confusion_matrix)  # Access the confusion matrix
+        """
+        self.confusion_matrix.process_cls_preds(self.pred, self.targets)
+        if self.args.plots:
+            for normalize in True, False:
+                self.confusion_matrix.plot(save_dir=self.save_dir, normalize=normalize, on_plot=self.on_plot)
+        self.metrics.speed = self.speed
+        self.metrics.save_dir = self.save_dir
+        self.metrics.confusion_matrix = self.confusion_matrix
+
+    def postprocess(self, preds: torch.Tensor | list[torch.Tensor] | tuple[torch.Tensor]) -> torch.Tensor:
+        """Extract the primary prediction from model output if it's in a list or tuple format."""
+        return preds[0] if isinstance(preds, (list, tuple)) else preds
+
+    def get_stats(self) -> dict[str, float]:
+        """Calculate and return a dictionary of metrics by processing targets and predictions."""
+        self.metrics.process(self.targets, self.pred)
+        return self.metrics.results_dict
+
+    def build_dataset(self, img_path: str) -> ClassificationDataset:
+        """Create a ClassificationDataset instance for validation."""
+        return ClassificationDataset(root=img_path, args=self.args, augment=False, prefix=self.args.split)
+
+    def get_dataloader(self, dataset_path: Path | str, batch_size: int) -> torch.utils.data.DataLoader:
+        """
+        Build and return a data loader for classification validation.
+
+        Args:
+            dataset_path (str | Path): Path to the dataset directory.
+            batch_size (int): Number of samples per batch.
+
+        Returns:
+            (torch.utils.data.DataLoader): DataLoader object for the classification validation dataset.
+        """
+        dataset = self.build_dataset(dataset_path)
+        return build_dataloader(dataset, batch_size, self.args.workers, rank=-1)
+
+    def print_results(self) -> None:
+        """Print evaluation metrics for the classification model."""
+        pf = "%22s" + "%11.3g" * len(self.metrics.keys)  # print format
+        LOGGER.info(pf % ("all", self.metrics.top1, self.metrics.top5))
+
+    def plot_val_samples(self, batch: dict[str, Any], ni: int) -> None:
+        """
+        Plot validation image samples with their ground truth labels.
+
+        Args:
+            batch (dict[str, Any]): Dictionary containing batch data with 'img' (images) and 'cls' (class labels).
+            ni (int): Batch index used for naming the output file.
+
+        Examples:
+            >>> validator = ClassificationValidator()
+            >>> batch = {"img": torch.rand(16, 3, 224, 224), "cls": torch.randint(0, 10, (16,))}
+            >>> validator.plot_val_samples(batch, 0)
+        """
+        batch["batch_idx"] = torch.arange(batch["img"].shape[0])  # add batch index for plotting
+        plot_images(
+            labels=batch,
+            fname=self.save_dir / f"val_batch{ni}_labels.jpg",
+            names=self.names,
+            on_plot=self.on_plot,
+        )
+
+    def plot_predictions(self, batch: dict[str, Any], preds: torch.Tensor, ni: int) -> None:
+        """
+        Plot images with their predicted class labels and save the visualization.
+
+        Args:
+            batch (dict[str, Any]): Batch data containing images and other information.
+            preds (torch.Tensor): Model predictions with shape (batch_size, num_classes).
+            ni (int): Batch index used for naming the output file.
+
+        Examples:
+            >>> validator = ClassificationValidator()
+            >>> batch = {"img": torch.rand(16, 3, 224, 224)}
+            >>> preds = torch.rand(16, 10)  # 16 images, 10 classes
+            >>> validator.plot_predictions(batch, preds, 0)
+        """
+        batched_preds = dict(
+            img=batch["img"],
+            batch_idx=torch.arange(batch["img"].shape[0]),
+            cls=torch.argmax(preds, dim=1),
+        )
+        plot_images(
+            batched_preds,
+            fname=self.save_dir / f"val_batch{ni}_pred.jpg",
+            names=self.names,
+            on_plot=self.on_plot,
+        )  # pred

ultralytics/models/yolo/detect/__init__.py

@@ -0,0 +1,7 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from .predict import DetectionPredictor
+from .train import DetectionTrainer
+from .val import DetectionValidator
+
+__all__ = "DetectionPredictor", "DetectionTrainer", "DetectionValidator"

ultralytics/models/yolo/detect/predict.py

@@ -0,0 +1,125 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from ultralytics.engine.predictor import BasePredictor
+from ultralytics.engine.results import Results
+from ultralytics.utils import nms, ops
+
+
+class DetectionPredictor(BasePredictor):
+    """
+    A class extending the BasePredictor class for prediction based on a detection model.
+
+    This predictor specializes in object detection tasks, processing model outputs into meaningful detection results
+    with bounding boxes and class predictions.
+
+    Attributes:
+        args (namespace): Configuration arguments for the predictor.
+        model (nn.Module): The detection model used for inference.
+        batch (list): Batch of images and metadata for processing.
+
+    Methods:
+        postprocess: Process raw model predictions into detection results.
+        construct_results: Build Results objects from processed predictions.
+        construct_result: Create a single Result object from a prediction.
+        get_obj_feats: Extract object features from the feature maps.
+
+    Examples:
+        >>> from ultralytics.utils import ASSETS
+        >>> from ultralytics.models.yolo.detect import DetectionPredictor
+        >>> args = dict(model="yolo11n.pt", source=ASSETS)
+        >>> predictor = DetectionPredictor(overrides=args)
+        >>> predictor.predict_cli()
+    """
+
+    def postprocess(self, preds, img, orig_imgs, **kwargs):
+        """
+        Post-process predictions and return a list of Results objects.
+
+        This method applies non-maximum suppression to raw model predictions and prepares them for visualization and
+        further analysis.
+
+        Args:
+            preds (torch.Tensor): Raw predictions from the model.
+            img (torch.Tensor): Processed input image tensor in model input format.
+            orig_imgs (torch.Tensor | list): Original input images before preprocessing.
+            **kwargs (Any): Additional keyword arguments.
+
+        Returns:
+            (list): List of Results objects containing the post-processed predictions.
+
+        Examples:
+            >>> predictor = DetectionPredictor(overrides=dict(model="yolo11n.pt"))
+            >>> results = predictor.predict("path/to/image.jpg")
+            >>> processed_results = predictor.postprocess(preds, img, orig_imgs)
+        """
+        save_feats = getattr(self, "_feats", None) is not None
+        preds = nms.non_max_suppression(
+            preds,
+            self.args.conf,
+            self.args.iou,
+            self.args.classes,
+            self.args.agnostic_nms,
+            max_det=self.args.max_det,
+            nc=0 if self.args.task == "detect" else len(self.model.names),
+            end2end=getattr(self.model, "end2end", False),
+            rotated=self.args.task == "obb",
+            return_idxs=save_feats,
+        )
+
+        if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
+            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
+
+        if save_feats:
+            obj_feats = self.get_obj_feats(self._feats, preds[1])
+            preds = preds[0]
+
+        results = self.construct_results(preds, img, orig_imgs, **kwargs)
+
+        if save_feats:
+            for r, f in zip(results, obj_feats):
+                r.feats = f  # add object features to results
+
+        return results
+
+    def get_obj_feats(self, feat_maps, idxs):
+        """Extract object features from the feature maps."""
+        import torch
+
+        s = min(x.shape[1] for x in feat_maps)  # find shortest vector length
+        obj_feats = torch.cat(
+            [x.permute(0, 2, 3, 1).reshape(x.shape[0], -1, s, x.shape[1] // s).mean(dim=-1) for x in feat_maps], dim=1
+        )  # mean reduce all vectors to same length
+        return [feats[idx] if idx.shape[0] else [] for feats, idx in zip(obj_feats, idxs)]  # for each img in batch
+
+    def construct_results(self, preds, img, orig_imgs):
+        """
+        Construct a list of Results objects from model predictions.
+
+        Args:
+            preds (list[torch.Tensor]): List of predicted bounding boxes and scores for each image.
+            img (torch.Tensor): Batch of preprocessed images used for inference.
+            orig_imgs (list[np.ndarray]): List of original images before preprocessing.
+
+        Returns:
+            (list[Results]): List of Results objects containing detection information for each image.
+        """
+        return [
+            self.construct_result(pred, img, orig_img, img_path)
+            for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0])
+        ]
+
+    def construct_result(self, pred, img, orig_img, img_path):
+        """
+        Construct a single Results object from one image prediction.
+
+        Args:
+            pred (torch.Tensor): Predicted boxes and scores with shape (N, 6) where N is the number of detections.
+            img (torch.Tensor): Preprocessed image tensor used for inference.
+            orig_img (np.ndarray): Original image before preprocessing.
+            img_path (str): Path to the original image file.
+
+        Returns:
+            (Results): Results object containing the original image, image path, class names, and scaled bounding boxes.
+        """
+        pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
+        return Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6])

ultralytics/models/yolo/detect/train.py

@@ -0,0 +1,236 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+import math
+import random
+from copy import copy
+from typing import Any
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from ultralytics.data import build_dataloader, build_yolo_dataset
+from ultralytics.engine.trainer import BaseTrainer
+from ultralytics.models import yolo
+from ultralytics.nn.tasks import DetectionModel
+from ultralytics.utils import DEFAULT_CFG, LOGGER, RANK
+from ultralytics.utils.patches import override_configs
+from ultralytics.utils.plotting import plot_images, plot_labels
+from ultralytics.utils.torch_utils import torch_distributed_zero_first, unwrap_model
+
+
+class DetectionTrainer(BaseTrainer):
+    """
+    A class extending the BaseTrainer class for training based on a detection model.
+
+    This trainer specializes in object detection tasks, handling the specific requirements for training YOLO models
+    for object detection including dataset building, data loading, preprocessing, and model configuration.
+
+    Attributes:
+        model (DetectionModel): The YOLO detection model being trained.
+        data (dict): Dictionary containing dataset information including class names and number of classes.
+        loss_names (tuple): Names of the loss components used in training (box_loss, cls_loss, dfl_loss).
+
+    Methods:
+        build_dataset: Build YOLO dataset for training or validation.
+        get_dataloader: Construct and return dataloader for the specified mode.
+        preprocess_batch: Preprocess a batch of images by scaling and converting to float.
+        set_model_attributes: Set model attributes based on dataset information.
+        get_model: Return a YOLO detection model.
+        get_validator: Return a validator for model evaluation.
+        label_loss_items: Return a loss dictionary with labeled training loss items.
+        progress_string: Return a formatted string of training progress.
+        plot_training_samples: Plot training samples with their annotations.
+        plot_training_labels: Create a labeled training plot of the YOLO model.
+        auto_batch: Calculate optimal batch size based on model memory requirements.
+
+    Examples:
+        >>> from ultralytics.models.yolo.detect import DetectionTrainer
+        >>> args = dict(model="yolo11n.pt", data="coco8.yaml", epochs=3)
+        >>> trainer = DetectionTrainer(overrides=args)
+        >>> trainer.train()
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides: dict[str, Any] | None = None, _callbacks=None):
+        """
+        Initialize a DetectionTrainer object for training YOLO object detection model training.
+
+        Args:
+            cfg (dict, optional): Default configuration dictionary containing training parameters.
+            overrides (dict, optional): Dictionary of parameter overrides for the default configuration.
+            _callbacks (list, optional): List of callback functions to be executed during training.
+        """
+        super().__init__(cfg, overrides, _callbacks)
+
+    def build_dataset(self, img_path: str, mode: str = "train", batch: int | None = None):
+        """
+        Build YOLO Dataset for training or validation.
+
+        Args:
+            img_path (str): Path to the folder containing images.
+            mode (str): 'train' mode or 'val' mode, users are able to customize different augmentations for each mode.
+            batch (int, optional): Size of batches, this is for 'rect' mode.
+
+        Returns:
+            (Dataset): YOLO dataset object configured for the specified mode.
+        """
+        gs = max(int(unwrap_model(self.model).stride.max() if self.model else 0), 32)
+        return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, rect=mode == "val", stride=gs)
+
+    def get_dataloader(self, dataset_path: str, batch_size: int = 16, rank: int = 0, mode: str = "train"):
+        """
+        Construct and return dataloader for the specified mode.
+
+        Args:
+            dataset_path (str): Path to the dataset.
+            batch_size (int): Number of images per batch.
+            rank (int): Process rank for distributed training.
+            mode (str): 'train' for training dataloader, 'val' for validation dataloader.
+
+        Returns:
+            (DataLoader): PyTorch dataloader object.
+        """
+        assert mode in {"train", "val"}, f"Mode must be 'train' or 'val', not {mode}."
+        with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
+            dataset = self.build_dataset(dataset_path, mode, batch_size)
+        shuffle = mode == "train"
+        if getattr(dataset, "rect", False) and shuffle:
+            LOGGER.warning("'rect=True' is incompatible with DataLoader shuffle, setting shuffle=False")
+            shuffle = False
+        return build_dataloader(
+            dataset,
+            batch=batch_size,
+            workers=self.args.workers if mode == "train" else self.args.workers * 2,
+            shuffle=shuffle,
+            rank=rank,
+            drop_last=self.args.compile and mode == "train",
+        )
+
+    def preprocess_batch(self, batch: dict) -> dict:
+        """
+        Preprocess a batch of images by scaling and converting to float.
+
+        Args:
+            batch (dict): Dictionary containing batch data with 'img' tensor.
+
+        Returns:
+            (dict): Preprocessed batch with normalized images.
+        """
+        for k, v in batch.items():
+            if isinstance(v, torch.Tensor):
+                batch[k] = v.to(self.device, non_blocking=self.device.type == "cuda")
+        batch["img"] = batch["img"].float() / 255
+        if self.args.multi_scale:
+            imgs = batch["img"]
+            sz = (
+                random.randrange(int(self.args.imgsz * 0.5), int(self.args.imgsz * 1.5 + self.stride))
+                // self.stride
+                * self.stride
+            )  # size
+            sf = sz / max(imgs.shape[2:])  # scale factor
+            if sf != 1:
+                ns = [
+                    math.ceil(x * sf / self.stride) * self.stride for x in imgs.shape[2:]
+                ]  # new shape (stretched to gs-multiple)
+                imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False)
+            batch["img"] = imgs
+        return batch
+
+    def set_model_attributes(self):
+        """Set model attributes based on dataset information."""
+        # Nl = de_parallel(self.model).model[-1].nl  # number of detection layers (to scale hyps)
+        # self.args.box *= 3 / nl  # scale to layers
+        # self.args.cls *= self.data["nc"] / 80 * 3 / nl  # scale to classes and layers
+        # self.args.cls *= (self.args.imgsz / 640) ** 2 * 3 / nl  # scale to image size and layers
+        self.model.nc = self.data["nc"]  # attach number of classes to model
+        self.model.names = self.data["names"]  # attach class names to model
+        self.model.args = self.args  # attach hyperparameters to model
+        # TODO: self.model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc
+
+    def get_model(self, cfg: str | None = None, weights: str | None = None, verbose: bool = True):
+        """
+        Return a YOLO detection model.
+
+        Args:
+            cfg (str, optional): Path to model configuration file.
+            weights (str, optional): Path to model weights.
+            verbose (bool): Whether to display model information.
+
+        Returns:
+            (DetectionModel): YOLO detection model.
+        """
+        model = DetectionModel(cfg, nc=self.data["nc"], ch=self.data["channels"], verbose=verbose and RANK == -1)
+        if weights:
+            model.load(weights)
+        return model
+
+    def get_validator(self):
+        """Return a DetectionValidator for YOLO model validation."""
+        self.loss_names = "box_loss", "cls_loss", "dfl_loss"
+        return yolo.detect.DetectionValidator(
+            self.test_loader, save_dir=self.save_dir, args=copy(self.args), _callbacks=self.callbacks
+        )
+
+    def label_loss_items(self, loss_items: list[float] | None = None, prefix: str = "train"):
+        """
+        Return a loss dict with labeled training loss items tensor.
+
+        Args:
+            loss_items (list[float], optional): List of loss values.
+            prefix (str): Prefix for keys in the returned dictionary.
+
+        Returns:
+            (dict | list): Dictionary of labeled loss items if loss_items is provided, otherwise list of keys.
+        """
+        keys = [f"{prefix}/{x}" for x in self.loss_names]
+        if loss_items is not None:
+            loss_items = [round(float(x), 5) for x in loss_items]  # convert tensors to 5 decimal place floats
+            return dict(zip(keys, loss_items))
+        else:
+            return keys
+
+    def progress_string(self):
+        """Return a formatted string of training progress with epoch, GPU memory, loss, instances and size."""
+        return ("\n" + "%11s" * (4 + len(self.loss_names))) % (
+            "Epoch",
+            "GPU_mem",
+            *self.loss_names,
+            "Instances",
+            "Size",
+        )
+
+    def plot_training_samples(self, batch: dict[str, Any], ni: int) -> None:
+        """
+        Plot training samples with their annotations.
+
+        Args:
+            batch (dict[str, Any]): Dictionary containing batch data.
+            ni (int): Number of iterations.
+        """
+        plot_images(
+            labels=batch,
+            paths=batch["im_file"],
+            fname=self.save_dir / f"train_batch{ni}.jpg",
+            on_plot=self.on_plot,
+        )
+
+    def plot_training_labels(self):
+        """Create a labeled training plot of the YOLO model."""
+        boxes = np.concatenate([lb["bboxes"] for lb in self.train_loader.dataset.labels], 0)
+        cls = np.concatenate([lb["cls"] for lb in self.train_loader.dataset.labels], 0)
+        plot_labels(boxes, cls.squeeze(), names=self.data["names"], save_dir=self.save_dir, on_plot=self.on_plot)
+
+    def auto_batch(self):
+        """
+        Get optimal batch size by calculating memory occupation of model.
+
+        Returns:
+            (int): Optimal batch size.
+        """
+        with override_configs(self.args, overrides={"cache": False}) as self.args:
+            train_dataset = self.build_dataset(self.data["train"], mode="train", batch=16)
+        max_num_obj = max(len(label["cls"]) for label in train_dataset.labels) * 4  # 4 for mosaic augmentation
+        del train_dataset  # free memory
+        return super().auto_batch(max_num_obj)

ultralytics/models/yolo/detect/val.py

@@ -0,0 +1,495 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+
+from ultralytics.data import build_dataloader, build_yolo_dataset, converter
+from ultralytics.engine.validator import BaseValidator
+from ultralytics.utils import LOGGER, nms, ops
+from ultralytics.utils.checks import check_requirements
+from ultralytics.utils.metrics import ConfusionMatrix, DetMetrics, box_iou
+from ultralytics.utils.plotting import plot_images
+
+
+class DetectionValidator(BaseValidator):
+    """
+    A class extending the BaseValidator class for validation based on a detection model.
+
+    This class implements validation functionality specific to object detection tasks, including metrics calculation,
+    prediction processing, and visualization of results.
+
+    Attributes:
+        is_coco (bool): Whether the dataset is COCO.
+        is_lvis (bool): Whether the dataset is LVIS.
+        class_map (list[int]): Mapping from model class indices to dataset class indices.
+        metrics (DetMetrics): Object detection metrics calculator.
+        iouv (torch.Tensor): IoU thresholds for mAP calculation.
+        niou (int): Number of IoU thresholds.
+        lb (list[Any]): List for storing ground truth labels for hybrid saving.
+        jdict (list[dict[str, Any]]): List for storing JSON detection results.
+        stats (dict[str, list[torch.Tensor]]): Dictionary for storing statistics during validation.
+
+    Examples:
+        >>> from ultralytics.models.yolo.detect import DetectionValidator
+        >>> args = dict(model="yolo11n.pt", data="coco8.yaml")
+        >>> validator = DetectionValidator(args=args)
+        >>> validator()
+    """
+
+    def __init__(self, dataloader=None, save_dir=None, args=None, _callbacks=None) -> None:
+        """
+        Initialize detection validator with necessary variables and settings.
+
+        Args:
+            dataloader (torch.utils.data.DataLoader, optional): Dataloader to use for validation.
+            save_dir (Path, optional): Directory to save results.
+            args (dict[str, Any], optional): Arguments for the validator.
+            _callbacks (list[Any], optional): List of callback functions.
+        """
+        super().__init__(dataloader, save_dir, args, _callbacks)
+        self.is_coco = False
+        self.is_lvis = False
+        self.class_map = None
+        self.args.task = "detect"
+        self.iouv = torch.linspace(0.5, 0.95, 10)  # IoU vector for mAP@0.5:0.95
+        self.niou = self.iouv.numel()
+        self.metrics = DetMetrics()
+
+    def preprocess(self, batch: dict[str, Any]) -> dict[str, Any]:
+        """
+        Preprocess batch of images for YOLO validation.
+
+        Args:
+            batch (dict[str, Any]): Batch containing images and annotations.
+
+        Returns:
+            (dict[str, Any]): Preprocessed batch.
+        """
+        for k, v in batch.items():
+            if isinstance(v, torch.Tensor):
+                batch[k] = v.to(self.device, non_blocking=self.device.type == "cuda")
+        batch["img"] = (batch["img"].half() if self.args.half else batch["img"].float()) / 255
+        return batch
+
+    def init_metrics(self, model: torch.nn.Module) -> None:
+        """
+        Initialize evaluation metrics for YOLO detection validation.
+
+        Args:
+            model (torch.nn.Module): Model to validate.
+        """
+        val = self.data.get(self.args.split, "")  # validation path
+        self.is_coco = (
+            isinstance(val, str)
+            and "coco" in val
+            and (val.endswith(f"{os.sep}val2017.txt") or val.endswith(f"{os.sep}test-dev2017.txt"))
+        )  # is COCO
+        self.is_lvis = isinstance(val, str) and "lvis" in val and not self.is_coco  # is LVIS
+        self.class_map = converter.coco80_to_coco91_class() if self.is_coco else list(range(1, len(model.names) + 1))
+        self.args.save_json |= self.args.val and (self.is_coco or self.is_lvis) and not self.training  # run final val
+        self.names = model.names
+        self.nc = len(model.names)
+        self.end2end = getattr(model, "end2end", False)
+        self.seen = 0
+        self.jdict = []
+        self.metrics.names = model.names
+        self.confusion_matrix = ConfusionMatrix(names=model.names, save_matches=self.args.plots and self.args.visualize)
+
+    def get_desc(self) -> str:
+        """Return a formatted string summarizing class metrics of YOLO model."""
+        return ("%22s" + "%11s" * 6) % ("Class", "Images", "Instances", "Box(P", "R", "mAP50", "mAP50-95)")
+
+    def postprocess(self, preds: torch.Tensor) -> list[dict[str, torch.Tensor]]:
+        """
+        Apply Non-maximum suppression to prediction outputs.
+
+        Args:
+            preds (torch.Tensor): Raw predictions from the model.
+
+        Returns:
+            (list[dict[str, torch.Tensor]]): Processed predictions after NMS, where each dict contains
+                'bboxes', 'conf', 'cls', and 'extra' tensors.
+        """
+        outputs = nms.non_max_suppression(
+            preds,
+            self.args.conf,
+            self.args.iou,
+            nc=0 if self.args.task == "detect" else self.nc,
+            multi_label=True,
+            agnostic=self.args.single_cls or self.args.agnostic_nms,
+            max_det=self.args.max_det,
+            end2end=self.end2end,
+            rotated=self.args.task == "obb",
+        )
+        return [{"bboxes": x[:, :4], "conf": x[:, 4], "cls": x[:, 5], "extra": x[:, 6:]} for x in outputs]
+
+    def _prepare_batch(self, si: int, batch: dict[str, Any]) -> dict[str, Any]:
+        """
+        Prepare a batch of images and annotations for validation.
+
+        Args:
+            si (int): Batch index.
+            batch (dict[str, Any]): Batch data containing images and annotations.
+
+        Returns:
+            (dict[str, Any]): Prepared batch with processed annotations.
+        """
+        idx = batch["batch_idx"] == si
+        cls = batch["cls"][idx].squeeze(-1)
+        bbox = batch["bboxes"][idx]
+        ori_shape = batch["ori_shape"][si]
+        imgsz = batch["img"].shape[2:]
+        ratio_pad = batch["ratio_pad"][si]
+        if cls.shape[0]:
+            bbox = ops.xywh2xyxy(bbox) * torch.tensor(imgsz, device=self.device)[[1, 0, 1, 0]]  # target boxes
+        return {
+            "cls": cls,
+            "bboxes": bbox,
+            "ori_shape": ori_shape,
+            "imgsz": imgsz,
+            "ratio_pad": ratio_pad,
+            "im_file": batch["im_file"][si],
+        }
+
+    def _prepare_pred(self, pred: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
+        """
+        Prepare predictions for evaluation against ground truth.
+
+        Args:
+            pred (dict[str, torch.Tensor]): Post-processed predictions from the model.
+
+        Returns:
+            (dict[str, torch.Tensor]): Prepared predictions in native space.
+        """
+        if self.args.single_cls:
+            pred["cls"] *= 0
+        return pred
+
+    def update_metrics(self, preds: list[dict[str, torch.Tensor]], batch: dict[str, Any]) -> None:
+        """
+        Update metrics with new predictions and ground truth.
+
+        Args:
+            preds (list[dict[str, torch.Tensor]]): List of predictions from the model.
+            batch (dict[str, Any]): Batch data containing ground truth.
+        """
+        for si, pred in enumerate(preds):
+            self.seen += 1
+            pbatch = self._prepare_batch(si, batch)
+            predn = self._prepare_pred(pred)
+
+            cls = pbatch["cls"].cpu().numpy()
+            no_pred = predn["cls"].shape[0] == 0
+            self.metrics.update_stats(
+                {
+                    **self._process_batch(predn, pbatch),
+                    "target_cls": cls,
+                    "target_img": np.unique(cls),
+                    "conf": np.zeros(0) if no_pred else predn["conf"].cpu().numpy(),
+                    "pred_cls": np.zeros(0) if no_pred else predn["cls"].cpu().numpy(),
+                }
+            )
+            # Evaluate
+            if self.args.plots:
+                self.confusion_matrix.process_batch(predn, pbatch, conf=self.args.conf)
+                if self.args.visualize:
+                    self.confusion_matrix.plot_matches(batch["img"][si], pbatch["im_file"], self.save_dir)
+
+            if no_pred:
+                continue
+
+            # Save
+            if self.args.save_json or self.args.save_txt:
+                predn_scaled = self.scale_preds(predn, pbatch)
+            if self.args.save_json:
+                self.pred_to_json(predn_scaled, pbatch)
+            if self.args.save_txt:
+                self.save_one_txt(
+                    predn_scaled,
+                    self.args.save_conf,
+                    pbatch["ori_shape"],
+                    self.save_dir / "labels" / f"{Path(pbatch['im_file']).stem}.txt",
+                )
+
+    def finalize_metrics(self) -> None:
+        """Set final values for metrics speed and confusion matrix."""
+        if self.args.plots:
+            for normalize in True, False:
+                self.confusion_matrix.plot(save_dir=self.save_dir, normalize=normalize, on_plot=self.on_plot)
+        self.metrics.speed = self.speed
+        self.metrics.confusion_matrix = self.confusion_matrix
+        self.metrics.save_dir = self.save_dir
+
+    def get_stats(self) -> dict[str, Any]:
+        """
+        Calculate and return metrics statistics.
+
+        Returns:
+            (dict[str, Any]): Dictionary containing metrics results.
+        """
+        self.metrics.process(save_dir=self.save_dir, plot=self.args.plots, on_plot=self.on_plot)
+        self.metrics.clear_stats()
+        return self.metrics.results_dict
+
+    def print_results(self) -> None:
+        """Print training/validation set metrics per class."""
+        pf = "%22s" + "%11i" * 2 + "%11.3g" * len(self.metrics.keys)  # print format
+        LOGGER.info(pf % ("all", self.seen, self.metrics.nt_per_class.sum(), *self.metrics.mean_results()))
+        if self.metrics.nt_per_class.sum() == 0:
+            LOGGER.warning(f"no labels found in {self.args.task} set, can not compute metrics without labels")
+
+        # Print results per class
+        if self.args.verbose and not self.training and self.nc > 1 and len(self.metrics.stats):
+            for i, c in enumerate(self.metrics.ap_class_index):
+                LOGGER.info(
+                    pf
+                    % (
+                        self.names[c],
+                        self.metrics.nt_per_image[c],
+                        self.metrics.nt_per_class[c],
+                        *self.metrics.class_result(i),
+                    )
+                )
+
+    def _process_batch(self, preds: dict[str, torch.Tensor], batch: dict[str, Any]) -> dict[str, np.ndarray]:
+        """
+        Return correct prediction matrix.
+
+        Args:
+            preds (dict[str, torch.Tensor]): Dictionary containing prediction data with 'bboxes' and 'cls' keys.
+            batch (dict[str, Any]): Batch dictionary containing ground truth data with 'bboxes' and 'cls' keys.
+
+        Returns:
+            (dict[str, np.ndarray]): Dictionary containing 'tp' key with correct prediction matrix of shape (N, 10) for 10 IoU levels.
+        """
+        if batch["cls"].shape[0] == 0 or preds["cls"].shape[0] == 0:
+            return {"tp": np.zeros((preds["cls"].shape[0], self.niou), dtype=bool)}
+        iou = box_iou(batch["bboxes"], preds["bboxes"])
+        return {"tp": self.match_predictions(preds["cls"], batch["cls"], iou).cpu().numpy()}
+
+    def build_dataset(self, img_path: str, mode: str = "val", batch: int | None = None) -> torch.utils.data.Dataset:
+        """
+        Build YOLO Dataset.
+
+        Args:
+            img_path (str): Path to the folder containing images.
+            mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
+            batch (int, optional): Size of batches, this is for `rect`.
+
+        Returns:
+            (Dataset): YOLO dataset.
+        """
+        return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, stride=self.stride)
+
+    def get_dataloader(self, dataset_path: str, batch_size: int) -> torch.utils.data.DataLoader:
+        """
+        Construct and return dataloader.
+
+        Args:
+            dataset_path (str): Path to the dataset.
+            batch_size (int): Size of each batch.
+
+        Returns:
+            (torch.utils.data.DataLoader): Dataloader for validation.
+        """
+        dataset = self.build_dataset(dataset_path, batch=batch_size, mode="val")
+        return build_dataloader(
+            dataset, batch_size, self.args.workers, shuffle=False, rank=-1, drop_last=self.args.compile
+        )
+
+    def plot_val_samples(self, batch: dict[str, Any], ni: int) -> None:
+        """
+        Plot validation image samples.
+
+        Args:
+            batch (dict[str, Any]): Batch containing images and annotations.
+            ni (int): Batch index.
+        """
+        plot_images(
+            labels=batch,
+            paths=batch["im_file"],
+            fname=self.save_dir / f"val_batch{ni}_labels.jpg",
+            names=self.names,
+            on_plot=self.on_plot,
+        )
+
+    def plot_predictions(
+        self, batch: dict[str, Any], preds: list[dict[str, torch.Tensor]], ni: int, max_det: int | None = None
+    ) -> None:
+        """
+        Plot predicted bounding boxes on input images and save the result.
+
+        Args:
+            batch (dict[str, Any]): Batch containing images and annotations.
+            preds (list[dict[str, torch.Tensor]]): List of predictions from the model.
+            ni (int): Batch index.
+            max_det (Optional[int]): Maximum number of detections to plot.
+        """
+        # TODO: optimize this
+        for i, pred in enumerate(preds):
+            pred["batch_idx"] = torch.ones_like(pred["conf"]) * i  # add batch index to predictions
+        keys = preds[0].keys()
+        max_det = max_det or self.args.max_det
+        batched_preds = {k: torch.cat([x[k][:max_det] for x in preds], dim=0) for k in keys}
+        # TODO: fix this
+        batched_preds["bboxes"][:, :4] = ops.xyxy2xywh(batched_preds["bboxes"][:, :4])  # convert to xywh format
+        plot_images(
+            images=batch["img"],
+            labels=batched_preds,
+            paths=batch["im_file"],
+            fname=self.save_dir / f"val_batch{ni}_pred.jpg",
+            names=self.names,
+            on_plot=self.on_plot,
+        )  # pred
+
+    def save_one_txt(self, predn: dict[str, torch.Tensor], save_conf: bool, shape: tuple[int, int], file: Path) -> None:
+        """
+        Save YOLO detections to a txt file in normalized coordinates in a specific format.
+
+        Args:
+            predn (dict[str, torch.Tensor]): Dictionary containing predictions with keys 'bboxes', 'conf', and 'cls'.
+            save_conf (bool): Whether to save confidence scores.
+            shape (tuple[int, int]): Shape of the original image (height, width).
+            file (Path): File path to save the detections.
+        """
+        from ultralytics.engine.results import Results
+
+        Results(
+            np.zeros((shape[0], shape[1]), dtype=np.uint8),
+            path=None,
+            names=self.names,
+            boxes=torch.cat([predn["bboxes"], predn["conf"].unsqueeze(-1), predn["cls"].unsqueeze(-1)], dim=1),
+        ).save_txt(file, save_conf=save_conf)
+
+    def pred_to_json(self, predn: dict[str, torch.Tensor], pbatch: dict[str, Any]) -> None:
+        """
+        Serialize YOLO predictions to COCO json format.
+
+        Args:
+            predn (dict[str, torch.Tensor]): Predictions dictionary containing 'bboxes', 'conf', and 'cls' keys
+                with bounding box coordinates, confidence scores, and class predictions.
+            pbatch (dict[str, Any]): Batch dictionary containing 'imgsz', 'ori_shape', 'ratio_pad', and 'im_file'.
+
+        Examples:
+             >>> result = {
+             ...     "image_id": 42,
+             ...     "file_name": "42.jpg",
+             ...     "category_id": 18,
+             ...     "bbox": [258.15, 41.29, 348.26, 243.78],
+             ...     "score": 0.236,
+             ... }
+        """
+        path = Path(pbatch["im_file"])
+        stem = path.stem
+        image_id = int(stem) if stem.isnumeric() else stem
+        box = ops.xyxy2xywh(predn["bboxes"])  # xywh
+        box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
+        for b, s, c in zip(box.tolist(), predn["conf"].tolist(), predn["cls"].tolist()):
+            self.jdict.append(
+                {
+                    "image_id": image_id,
+                    "file_name": path.name,
+                    "category_id": self.class_map[int(c)],
+                    "bbox": [round(x, 3) for x in b],
+                    "score": round(s, 5),
+                }
+            )
+
+    def scale_preds(self, predn: dict[str, torch.Tensor], pbatch: dict[str, Any]) -> dict[str, torch.Tensor]:
+        """Scales predictions to the original image size."""
+        return {
+            **predn,
+            "bboxes": ops.scale_boxes(
+                pbatch["imgsz"],
+                predn["bboxes"].clone(),
+                pbatch["ori_shape"],
+                ratio_pad=pbatch["ratio_pad"],
+            ),
+        }
+
+    def eval_json(self, stats: dict[str, Any]) -> dict[str, Any]:
+        """
+        Evaluate YOLO output in JSON format and return performance statistics.
+
+        Args:
+            stats (dict[str, Any]): Current statistics dictionary.
+
+        Returns:
+            (dict[str, Any]): Updated statistics dictionary with COCO/LVIS evaluation results.
+        """
+        pred_json = self.save_dir / "predictions.json"  # predictions
+        anno_json = (
+            self.data["path"]
+            / "annotations"
+            / ("instances_val2017.json" if self.is_coco else f"lvis_v1_{self.args.split}.json")
+        )  # annotations
+        return self.coco_evaluate(stats, pred_json, anno_json)
+
+    def coco_evaluate(
+        self,
+        stats: dict[str, Any],
+        pred_json: str,
+        anno_json: str,
+        iou_types: str | list[str] = "bbox",
+        suffix: str | list[str] = "Box",
+    ) -> dict[str, Any]:
+        """
+        Evaluate COCO/LVIS metrics using faster-coco-eval library.
+
+        Performs evaluation using the faster-coco-eval library to compute mAP metrics
+        for object detection. Updates the provided stats dictionary with computed metrics
+        including mAP50, mAP50-95, and LVIS-specific metrics if applicable.
+
+        Args:
+            stats (dict[str, Any]): Dictionary to store computed metrics and statistics.
+            pred_json (str | Path]): Path to JSON file containing predictions in COCO format.
+            anno_json (str | Path]): Path to JSON file containing ground truth annotations in COCO format.
+            iou_types (str | list[str]]): IoU type(s) for evaluation. Can be single string or list of strings.
+                Common values include "bbox", "segm", "keypoints". Defaults to "bbox".
+            suffix (str | list[str]]): Suffix to append to metric names in stats dictionary. Should correspond
+                to iou_types if multiple types provided. Defaults to "Box".
+
+        Returns:
+            (dict[str, Any]): Updated stats dictionary containing the computed COCO/LVIS evaluation metrics.
+        """
+        if self.args.save_json and (self.is_coco or self.is_lvis) and len(self.jdict):
+            LOGGER.info(f"\nEvaluating faster-coco-eval mAP using {pred_json} and {anno_json}...")
+            try:
+                for x in pred_json, anno_json:
+                    assert x.is_file(), f"{x} file not found"
+                iou_types = [iou_types] if isinstance(iou_types, str) else iou_types
+                suffix = [suffix] if isinstance(suffix, str) else suffix
+                check_requirements("faster-coco-eval>=1.6.7")
+                from faster_coco_eval import COCO, COCOeval_faster
+
+                anno = COCO(anno_json)
+                pred = anno.loadRes(pred_json)
+                for i, iou_type in enumerate(iou_types):
+                    val = COCOeval_faster(
+                        anno, pred, iouType=iou_type, lvis_style=self.is_lvis, print_function=LOGGER.info
+                    )
+                    val.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files]  # images to eval
+                    val.evaluate()
+                    val.accumulate()
+                    val.summarize()
+
+                    # update mAP50-95 and mAP50
+                    stats[f"metrics/mAP50({suffix[i][0]})"] = val.stats_as_dict["AP_50"]
+                    stats[f"metrics/mAP50-95({suffix[i][0]})"] = val.stats_as_dict["AP_all"]
+
+                    if self.is_lvis:
+                        stats[f"metrics/APr({suffix[i][0]})"] = val.stats_as_dict["APr"]
+                        stats[f"metrics/APc({suffix[i][0]})"] = val.stats_as_dict["APc"]
+                        stats[f"metrics/APf({suffix[i][0]})"] = val.stats_as_dict["APf"]
+
+                if self.is_lvis:
+                    stats["fitness"] = stats["metrics/mAP50-95(B)"]  # always use box mAP50-95 for fitness
+            except Exception as e:
+                LOGGER.warning(f"faster-coco-eval unable to run: {e}")
+        return stats

ultralytics/models/yolo/model.py

@@ -0,0 +1,447 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import torch
+
+from ultralytics.data.build import load_inference_source
+from ultralytics.engine.model import Model
+from ultralytics.models import yolo
+from ultralytics.nn.tasks import (
+    ClassificationModel,
+    DetectionModel,
+    OBBModel,
+    PoseModel,
+    SegmentationModel,
+    WorldModel,
+    YOLOEModel,
+    YOLOESegModel,
+)
+from ultralytics.utils import ROOT, YAML
+
+
+class YOLO(Model):
+    """
+    YOLO (You Only Look Once) object detection model.
+
+    This class provides a unified interface for YOLO models, automatically switching to specialized model types
+    (YOLOWorld or YOLOE) based on the model filename. It supports various computer vision tasks including object
+    detection, segmentation, classification, pose estimation, and oriented bounding box detection.
+
+    Attributes:
+        model: The loaded YOLO model instance.
+        task: The task type (detect, segment, classify, pose, obb).
+        overrides: Configuration overrides for the model.
+
+    Methods:
+        __init__: Initialize a YOLO model with automatic type detection.
+        task_map: Map tasks to their corresponding model, trainer, validator, and predictor classes.
+
+    Examples:
+        Load a pretrained YOLOv11n detection model
+        >>> model = YOLO("yolo11n.pt")
+
+        Load a pretrained YOLO11n segmentation model
+        >>> model = YOLO("yolo11n-seg.pt")
+
+        Initialize from a YAML configuration
+        >>> model = YOLO("yolo11n.yaml")
+    """
+
+    def __init__(self, model: str | Path = "yolo11n.pt", task: str | None = None, verbose: bool = False):
+        """
+        Initialize a YOLO model.
+
+        This constructor initializes a YOLO model, automatically switching to specialized model types
+        (YOLOWorld or YOLOE) based on the model filename.
+
+        Args:
+            model (str | Path): Model name or path to model file, i.e. 'yolo11n.pt', 'yolo11n.yaml'.
+            task (str, optional): YOLO task specification, i.e. 'detect', 'segment', 'classify', 'pose', 'obb'.
+                Defaults to auto-detection based on model.
+            verbose (bool): Display model info on load.
+
+        Examples:
+            >>> from ultralytics import YOLO
+            >>> model = YOLO("yolo11n.pt")  # load a pretrained YOLOv11n detection model
+            >>> model = YOLO("yolo11n-seg.pt")  # load a pretrained YOLO11n segmentation model
+        """
+        path = Path(model if isinstance(model, (str, Path)) else "")
+        if "-world" in path.stem and path.suffix in {".pt", ".yaml", ".yml"}:  # if YOLOWorld PyTorch model
+            new_instance = YOLOWorld(path, verbose=verbose)
+            self.__class__ = type(new_instance)
+            self.__dict__ = new_instance.__dict__
+        elif "yoloe" in path.stem and path.suffix in {".pt", ".yaml", ".yml"}:  # if YOLOE PyTorch model
+            new_instance = YOLOE(path, task=task, verbose=verbose)
+            self.__class__ = type(new_instance)
+            self.__dict__ = new_instance.__dict__
+        else:
+            # Continue with default YOLO initialization
+            super().__init__(model=model, task=task, verbose=verbose)
+            if hasattr(self.model, "model") and "RTDETR" in self.model.model[-1]._get_name():  # if RTDETR head
+                from ultralytics import RTDETR
+
+                new_instance = RTDETR(self)
+                self.__class__ = type(new_instance)
+                self.__dict__ = new_instance.__dict__
+
+    @property
+    def task_map(self) -> dict[str, dict[str, Any]]:
+        """Map head to model, trainer, validator, and predictor classes."""
+        return {
+            "classify": {
+                "model": ClassificationModel,
+                "trainer": yolo.classify.ClassificationTrainer,
+                "validator": yolo.classify.ClassificationValidator,
+                "predictor": yolo.classify.ClassificationPredictor,
+            },
+            "detect": {
+                "model": DetectionModel,
+                "trainer": yolo.detect.DetectionTrainer,
+                "validator": yolo.detect.DetectionValidator,
+                "predictor": yolo.detect.DetectionPredictor,
+            },
+            "segment": {
+                "model": SegmentationModel,
+                "trainer": yolo.segment.SegmentationTrainer,
+                "validator": yolo.segment.SegmentationValidator,
+                "predictor": yolo.segment.SegmentationPredictor,
+            },
+            "pose": {
+                "model": PoseModel,
+                "trainer": yolo.pose.PoseTrainer,
+                "validator": yolo.pose.PoseValidator,
+                "predictor": yolo.pose.PosePredictor,
+            },
+            "obb": {
+                "model": OBBModel,
+                "trainer": yolo.obb.OBBTrainer,
+                "validator": yolo.obb.OBBValidator,
+                "predictor": yolo.obb.OBBPredictor,
+            },
+        }
+
+
+class YOLOWorld(Model):
+    """
+    YOLO-World object detection model.
+
+    YOLO-World is an open-vocabulary object detection model that can detect objects based on text descriptions
+    without requiring training on specific classes. It extends the YOLO architecture to support real-time
+    open-vocabulary detection.
+
+    Attributes:
+        model: The loaded YOLO-World model instance.
+        task: Always set to 'detect' for object detection.
+        overrides: Configuration overrides for the model.
+
+    Methods:
+        __init__: Initialize YOLOv8-World model with a pre-trained model file.
+        task_map: Map tasks to their corresponding model, trainer, validator, and predictor classes.
+        set_classes: Set the model's class names for detection.
+
+    Examples:
+        Load a YOLOv8-World model
+        >>> model = YOLOWorld("yolov8s-world.pt")
+
+        Set custom classes for detection
+        >>> model.set_classes(["person", "car", "bicycle"])
+    """
+
+    def __init__(self, model: str | Path = "yolov8s-world.pt", verbose: bool = False) -> None:
+        """
+        Initialize YOLOv8-World model with a pre-trained model file.
+
+        Loads a YOLOv8-World model for object detection. If no custom class names are provided, it assigns default
+        COCO class names.
+
+        Args:
+            model (str | Path): Path to the pre-trained model file. Supports *.pt and *.yaml formats.
+            verbose (bool): If True, prints additional information during initialization.
+        """
+        super().__init__(model=model, task="detect", verbose=verbose)
+
+        # Assign default COCO class names when there are no custom names
+        if not hasattr(self.model, "names"):
+            self.model.names = YAML.load(ROOT / "cfg/datasets/coco8.yaml").get("names")
+
+    @property
+    def task_map(self) -> dict[str, dict[str, Any]]:
+        """Map head to model, validator, and predictor classes."""
+        return {
+            "detect": {
+                "model": WorldModel,
+                "validator": yolo.detect.DetectionValidator,
+                "predictor": yolo.detect.DetectionPredictor,
+                "trainer": yolo.world.WorldTrainer,
+            }
+        }
+
+    def set_classes(self, classes: list[str]) -> None:
+        """
+        Set the model's class names for detection.
+
+        Args:
+            classes (list[str]): A list of categories i.e. ["person"].
+        """
+        self.model.set_classes(classes)
+        # Remove background if it's given
+        background = " "
+        if background in classes:
+            classes.remove(background)
+        self.model.names = classes
+
+        # Reset method class names
+        if self.predictor:
+            self.predictor.model.names = classes
+
+
+class YOLOE(Model):
+    """
+    YOLOE object detection and segmentation model.
+
+    YOLOE is an enhanced YOLO model that supports both object detection and instance segmentation tasks with
+    improved performance and additional features like visual and text positional embeddings.
+
+    Attributes:
+        model: The loaded YOLOE model instance.
+        task: The task type (detect or segment).
+        overrides: Configuration overrides for the model.
+
+    Methods:
+        __init__: Initialize YOLOE model with a pre-trained model file.
+        task_map: Map tasks to their corresponding model, trainer, validator, and predictor classes.
+        get_text_pe: Get text positional embeddings for the given texts.
+        get_visual_pe: Get visual positional embeddings for the given image and visual features.
+        set_vocab: Set vocabulary and class names for the YOLOE model.
+        get_vocab: Get vocabulary for the given class names.
+        set_classes: Set the model's class names and embeddings for detection.
+        val: Validate the model using text or visual prompts.
+        predict: Run prediction on images, videos, directories, streams, etc.
+
+    Examples:
+        Load a YOLOE detection model
+        >>> model = YOLOE("yoloe-11s-seg.pt")
+
+        Set vocabulary and class names
+        >>> model.set_vocab(["person", "car", "dog"], ["person", "car", "dog"])
+
+        Predict with visual prompts
+        >>> prompts = {"bboxes": [[10, 20, 100, 200]], "cls": ["person"]}
+        >>> results = model.predict("image.jpg", visual_prompts=prompts)
+    """
+
+    def __init__(self, model: str | Path = "yoloe-11s-seg.pt", task: str | None = None, verbose: bool = False) -> None:
+        """
+        Initialize YOLOE model with a pre-trained model file.
+
+        Args:
+            model (str | Path): Path to the pre-trained model file. Supports *.pt and *.yaml formats.
+            task (str, optional): Task type for the model. Auto-detected if None.
+            verbose (bool): If True, prints additional information during initialization.
+        """
+        super().__init__(model=model, task=task, verbose=verbose)
+
+    @property
+    def task_map(self) -> dict[str, dict[str, Any]]:
+        """Map head to model, validator, and predictor classes."""
+        return {
+            "detect": {
+                "model": YOLOEModel,
+                "validator": yolo.yoloe.YOLOEDetectValidator,
+                "predictor": yolo.detect.DetectionPredictor,
+                "trainer": yolo.yoloe.YOLOETrainer,
+            },
+            "segment": {
+                "model": YOLOESegModel,
+                "validator": yolo.yoloe.YOLOESegValidator,
+                "predictor": yolo.segment.SegmentationPredictor,
+                "trainer": yolo.yoloe.YOLOESegTrainer,
+            },
+        }
+
+    def get_text_pe(self, texts):
+        """Get text positional embeddings for the given texts."""
+        assert isinstance(self.model, YOLOEModel)
+        return self.model.get_text_pe(texts)
+
+    def get_visual_pe(self, img, visual):
+        """
+        Get visual positional embeddings for the given image and visual features.
+
+        This method extracts positional embeddings from visual features based on the input image. It requires
+        that the model is an instance of YOLOEModel.
+
+        Args:
+            img (torch.Tensor): Input image tensor.
+            visual (torch.Tensor): Visual features extracted from the image.
+
+        Returns:
+            (torch.Tensor): Visual positional embeddings.
+
+        Examples:
+            >>> model = YOLOE("yoloe-11s-seg.pt")
+            >>> img = torch.rand(1, 3, 640, 640)
+            >>> visual_features = torch.rand(1, 1, 80, 80)
+            >>> pe = model.get_visual_pe(img, visual_features)
+        """
+        assert isinstance(self.model, YOLOEModel)
+        return self.model.get_visual_pe(img, visual)
+
+    def set_vocab(self, vocab: list[str], names: list[str]) -> None:
+        """
+        Set vocabulary and class names for the YOLOE model.
+
+        This method configures the vocabulary and class names used by the model for text processing and
+        classification tasks. The model must be an instance of YOLOEModel.
+
+        Args:
+            vocab (list[str]): Vocabulary list containing tokens or words used by the model for text processing.
+            names (list[str]): List of class names that the model can detect or classify.
+
+        Raises:
+            AssertionError: If the model is not an instance of YOLOEModel.
+
+        Examples:
+            >>> model = YOLOE("yoloe-11s-seg.pt")
+            >>> model.set_vocab(["person", "car", "dog"], ["person", "car", "dog"])
+        """
+        assert isinstance(self.model, YOLOEModel)
+        self.model.set_vocab(vocab, names=names)
+
+    def get_vocab(self, names):
+        """Get vocabulary for the given class names."""
+        assert isinstance(self.model, YOLOEModel)
+        return self.model.get_vocab(names)
+
+    def set_classes(self, classes: list[str], embeddings: torch.Tensor | None = None) -> None:
+        """
+        Set the model's class names and embeddings for detection.
+
+        Args:
+            classes (list[str]): A list of categories i.e. ["person"].
+            embeddings (torch.Tensor): Embeddings corresponding to the classes.
+        """
+        assert isinstance(self.model, YOLOEModel)
+        if embeddings is None:
+            embeddings = self.get_text_pe(classes)  # generate text embeddings if not provided
+        self.model.set_classes(classes, embeddings)
+        # Verify no background class is present
+        assert " " not in classes
+        self.model.names = classes
+
+        # Reset method class names
+        if self.predictor:
+            self.predictor.model.names = classes
+
+    def val(
+        self,
+        validator=None,
+        load_vp: bool = False,
+        refer_data: str | None = None,
+        **kwargs,
+    ):
+        """
+        Validate the model using text or visual prompts.
+
+        Args:
+            validator (callable, optional): A callable validator function. If None, a default validator is loaded.
+            load_vp (bool): Whether to load visual prompts. If False, text prompts are used.
+            refer_data (str, optional): Path to the reference data for visual prompts.
+            **kwargs (Any): Additional keyword arguments to override default settings.
+
+        Returns:
+            (dict): Validation statistics containing metrics computed during validation.
+        """
+        custom = {"rect": not load_vp}  # method defaults
+        args = {**self.overrides, **custom, **kwargs, "mode": "val"}  # highest priority args on the right
+
+        validator = (validator or self._smart_load("validator"))(args=args, _callbacks=self.callbacks)
+        validator(model=self.model, load_vp=load_vp, refer_data=refer_data)
+        self.metrics = validator.metrics
+        return validator.metrics
+
+    def predict(
+        self,
+        source=None,
+        stream: bool = False,
+        visual_prompts: dict[str, list] = {},
+        refer_image=None,
+        predictor=yolo.yoloe.YOLOEVPDetectPredictor,
+        **kwargs,
+    ):
+        """
+        Run prediction on images, videos, directories, streams, etc.
+
+        Args:
+            source (str | int | PIL.Image | np.ndarray, optional): Source for prediction. Accepts image paths,
+                directory paths, URL/YouTube streams, PIL images, numpy arrays, or webcam indices.
+            stream (bool): Whether to stream the prediction results. If True, results are yielded as a
+                generator as they are computed.
+            visual_prompts (dict[str, list]): Dictionary containing visual prompts for the model. Must include
+                'bboxes' and 'cls' keys when non-empty.
+            refer_image (str | PIL.Image | np.ndarray, optional): Reference image for visual prompts.
+            predictor (callable, optional): Custom predictor function. If None, a predictor is automatically
+                loaded based on the task.
+            **kwargs (Any): Additional keyword arguments passed to the predictor.
+
+        Returns:
+            (list | generator): List of Results objects or generator of Results objects if stream=True.
+
+        Examples:
+            >>> model = YOLOE("yoloe-11s-seg.pt")
+            >>> results = model.predict("path/to/image.jpg")
+            >>> # With visual prompts
+            >>> prompts = {"bboxes": [[10, 20, 100, 200]], "cls": ["person"]}
+            >>> results = model.predict("path/to/image.jpg", visual_prompts=prompts)
+        """
+        if len(visual_prompts):
+            assert "bboxes" in visual_prompts and "cls" in visual_prompts, (
+                f"Expected 'bboxes' and 'cls' in visual prompts, but got {visual_prompts.keys()}"
+            )
+            assert len(visual_prompts["bboxes"]) == len(visual_prompts["cls"]), (
+                f"Expected equal number of bounding boxes and classes, but got {len(visual_prompts['bboxes'])} and "
+                f"{len(visual_prompts['cls'])} respectively"
+            )
+            if type(self.predictor) is not predictor:
+                self.predictor = predictor(
+                    overrides={
+                        "task": self.model.task,
+                        "mode": "predict",
+                        "save": False,
+                        "verbose": refer_image is None,
+                        "batch": 1,
+                        "device": kwargs.get("device", None),
+                        "half": kwargs.get("half", False),
+                        "imgsz": kwargs.get("imgsz", self.overrides["imgsz"]),
+                    },
+                    _callbacks=self.callbacks,
+                )
+
+            num_cls = (
+                max(len(set(c)) for c in visual_prompts["cls"])
+                if isinstance(source, list) and refer_image is None  # means multiple images
+                else len(set(visual_prompts["cls"]))
+            )
+            self.model.model[-1].nc = num_cls
+            self.model.names = [f"object{i}" for i in range(num_cls)]
+            self.predictor.set_prompts(visual_prompts.copy())
+            self.predictor.setup_model(model=self.model)
+
+            if refer_image is None and source is not None:
+                dataset = load_inference_source(source)
+                if dataset.mode in {"video", "stream"}:
+                    # NOTE: set the first frame as refer image for videos/streams inference
+                    refer_image = next(iter(dataset))[1][0]
+            if refer_image is not None:
+                vpe = self.predictor.get_vpe(refer_image)
+                self.model.set_classes(self.model.names, vpe)
+                self.task = "segment" if isinstance(self.predictor, yolo.segment.SegmentationPredictor) else "detect"
+                self.predictor = None  # reset predictor
+        elif isinstance(self.predictor, yolo.yoloe.YOLOEVPDetectPredictor):
+            self.predictor = None  # reset predictor if no visual prompts
+
+        return super().predict(source, stream, **kwargs)

ultralytics/models/yolo/obb/__init__.py

@@ -0,0 +1,7 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from .predict import OBBPredictor
+from .train import OBBTrainer
+from .val import OBBValidator
+
+__all__ = "OBBPredictor", "OBBTrainer", "OBBValidator"

ultralytics/models/yolo/obb/predict.py

@@ -0,0 +1,65 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import torch
+
+from ultralytics.engine.results import Results
+from ultralytics.models.yolo.detect.predict import DetectionPredictor
+from ultralytics.utils import DEFAULT_CFG, ops
+
+
+class OBBPredictor(DetectionPredictor):
+    """
+    A class extending the DetectionPredictor class for prediction based on an Oriented Bounding Box (OBB) model.
+
+    This predictor handles oriented bounding box detection tasks, processing images and returning results with rotated
+    bounding boxes.
+
+    Attributes:
+        args (namespace): Configuration arguments for the predictor.
+        model (torch.nn.Module): The loaded YOLO OBB model.
+
+    Examples:
+        >>> from ultralytics.utils import ASSETS
+        >>> from ultralytics.models.yolo.obb import OBBPredictor
+        >>> args = dict(model="yolo11n-obb.pt", source=ASSETS)
+        >>> predictor = OBBPredictor(overrides=args)
+        >>> predictor.predict_cli()
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """
+        Initialize OBBPredictor with optional model and data configuration overrides.
+
+        Args:
+            cfg (dict, optional): Default configuration for the predictor.
+            overrides (dict, optional): Configuration overrides that take precedence over the default config.
+            _callbacks (list, optional): List of callback functions to be invoked during prediction.
+
+        Examples:
+            >>> from ultralytics.utils import ASSETS
+            >>> from ultralytics.models.yolo.obb import OBBPredictor
+            >>> args = dict(model="yolo11n-obb.pt", source=ASSETS)
+            >>> predictor = OBBPredictor(overrides=args)
+        """
+        super().__init__(cfg, overrides, _callbacks)
+        self.args.task = "obb"
+
+    def construct_result(self, pred, img, orig_img, img_path):
+        """
+        Construct the result object from the prediction.
+
+        Args:
+            pred (torch.Tensor): The predicted bounding boxes, scores, and rotation angles with shape (N, 7) where
+                the last dimension contains [x, y, w, h, confidence, class_id, angle].
+            img (torch.Tensor): The image after preprocessing with shape (B, C, H, W).
+            orig_img (np.ndarray): The original image before preprocessing.
+            img_path (str): The path to the original image.
+
+        Returns:
+            (Results): The result object containing the original image, image path, class names, and oriented bounding
+                boxes.
+        """
+        rboxes = ops.regularize_rboxes(torch.cat([pred[:, :4], pred[:, -1:]], dim=-1))
+        rboxes[:, :4] = ops.scale_boxes(img.shape[2:], rboxes[:, :4], orig_img.shape, xywh=True)
+        obb = torch.cat([rboxes, pred[:, 4:6]], dim=-1)
+        return Results(orig_img, path=img_path, names=self.model.names, obb=obb)

ultralytics/models/yolo/obb/train.py

@@ -0,0 +1,82 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from copy import copy
+from pathlib import Path
+from typing import Any
+
+from ultralytics.models import yolo
+from ultralytics.nn.tasks import OBBModel
+from ultralytics.utils import DEFAULT_CFG, RANK
+
+
+class OBBTrainer(yolo.detect.DetectionTrainer):
+    """
+    A class extending the DetectionTrainer class for training based on an Oriented Bounding Box (OBB) model.
+
+    This trainer specializes in training YOLO models that detect oriented bounding boxes, which are useful for
+    detecting objects at arbitrary angles rather than just axis-aligned rectangles.
+
+    Attributes:
+        loss_names (tuple): Names of the loss components used during training including box_loss, cls_loss,
+            and dfl_loss.
+
+    Methods:
+        get_model: Return OBBModel initialized with specified config and weights.
+        get_validator: Return an instance of OBBValidator for validation of YOLO model.
+
+    Examples:
+        >>> from ultralytics.models.yolo.obb import OBBTrainer
+        >>> args = dict(model="yolo11n-obb.pt", data="dota8.yaml", epochs=3)
+        >>> trainer = OBBTrainer(overrides=args)
+        >>> trainer.train()
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides: dict | None = None, _callbacks: list[Any] | None = None):
+        """
+        Initialize an OBBTrainer object for training Oriented Bounding Box (OBB) models.
+
+        Args:
+            cfg (dict, optional): Configuration dictionary for the trainer. Contains training parameters and
+                model configuration.
+            overrides (dict, optional): Dictionary of parameter overrides for the configuration. Any values here
+                will take precedence over those in cfg.
+            _callbacks (list[Any], optional): List of callback functions to be invoked during training.
+        """
+        if overrides is None:
+            overrides = {}
+        overrides["task"] = "obb"
+        super().__init__(cfg, overrides, _callbacks)
+
+    def get_model(
+        self, cfg: str | dict | None = None, weights: str | Path | None = None, verbose: bool = True
+    ) -> OBBModel:
+        """
+        Return OBBModel initialized with specified config and weights.
+
+        Args:
+            cfg (str | dict, optional): Model configuration. Can be a path to a YAML config file, a dictionary
+                containing configuration parameters, or None to use default configuration.
+            weights (str | Path, optional): Path to pretrained weights file. If None, random initialization is used.
+            verbose (bool): Whether to display model information during initialization.
+
+        Returns:
+            (OBBModel): Initialized OBBModel with the specified configuration and weights.
+
+        Examples:
+            >>> trainer = OBBTrainer()
+            >>> model = trainer.get_model(cfg="yolo11n-obb.yaml", weights="yolo11n-obb.pt")
+        """
+        model = OBBModel(cfg, nc=self.data["nc"], ch=self.data["channels"], verbose=verbose and RANK == -1)
+        if weights:
+            model.load(weights)
+
+        return model
+
+    def get_validator(self):
+        """Return an instance of OBBValidator for validation of YOLO model."""
+        self.loss_names = "box_loss", "cls_loss", "dfl_loss"
+        return yolo.obb.OBBValidator(
+            self.test_loader, save_dir=self.save_dir, args=copy(self.args), _callbacks=self.callbacks
+        )

ultralytics/models/yolo/obb/val.py

@@ -0,0 +1,299 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+
+from ultralytics.models.yolo.detect import DetectionValidator
+from ultralytics.utils import LOGGER, ops
+from ultralytics.utils.metrics import OBBMetrics, batch_probiou
+from ultralytics.utils.nms import TorchNMS
+
+
+class OBBValidator(DetectionValidator):
+    """
+    A class extending the DetectionValidator class for validation based on an Oriented Bounding Box (OBB) model.
+
+    This validator specializes in evaluating models that predict rotated bounding boxes, commonly used for aerial and
+    satellite imagery where objects can appear at various orientations.
+
+    Attributes:
+        args (dict): Configuration arguments for the validator.
+        metrics (OBBMetrics): Metrics object for evaluating OBB model performance.
+        is_dota (bool): Flag indicating whether the validation dataset is in DOTA format.
+
+    Methods:
+        init_metrics: Initialize evaluation metrics for YOLO.
+        _process_batch: Process batch of detections and ground truth boxes to compute IoU matrix.
+        _prepare_batch: Prepare batch data for OBB validation.
+        _prepare_pred: Prepare predictions with scaled and padded bounding boxes.
+        plot_predictions: Plot predicted bounding boxes on input images.
+        pred_to_json: Serialize YOLO predictions to COCO json format.
+        save_one_txt: Save YOLO detections to a txt file in normalized coordinates.
+        eval_json: Evaluate YOLO output in JSON format and return performance statistics.
+
+    Examples:
+        >>> from ultralytics.models.yolo.obb import OBBValidator
+        >>> args = dict(model="yolo11n-obb.pt", data="dota8.yaml")
+        >>> validator = OBBValidator(args=args)
+        >>> validator(model=args["model"])
+    """
+
+    def __init__(self, dataloader=None, save_dir=None, args=None, _callbacks=None) -> None:
+        """
+        Initialize OBBValidator and set task to 'obb', metrics to OBBMetrics.
+
+        This constructor initializes an OBBValidator instance for validating Oriented Bounding Box (OBB) models.
+        It extends the DetectionValidator class and configures it specifically for the OBB task.
+
+        Args:
+            dataloader (torch.utils.data.DataLoader, optional): Dataloader to be used for validation.
+            save_dir (str | Path, optional): Directory to save results.
+            args (dict | SimpleNamespace, optional): Arguments containing validation parameters.
+            _callbacks (list, optional): List of callback functions to be called during validation.
+        """
+        super().__init__(dataloader, save_dir, args, _callbacks)
+        self.args.task = "obb"
+        self.metrics = OBBMetrics()
+
+    def init_metrics(self, model: torch.nn.Module) -> None:
+        """
+        Initialize evaluation metrics for YOLO obb validation.
+
+        Args:
+            model (torch.nn.Module): Model to validate.
+        """
+        super().init_metrics(model)
+        val = self.data.get(self.args.split, "")  # validation path
+        self.is_dota = isinstance(val, str) and "DOTA" in val  # check if dataset is DOTA format
+        self.confusion_matrix.task = "obb"  # set confusion matrix task to 'obb'
+
+    def _process_batch(self, preds: dict[str, torch.Tensor], batch: dict[str, torch.Tensor]) -> dict[str, np.ndarray]:
+        """
+        Compute the correct prediction matrix for a batch of detections and ground truth bounding boxes.
+
+        Args:
+            preds (dict[str, torch.Tensor]): Prediction dictionary containing 'cls' and 'bboxes' keys with detected
+                class labels and bounding boxes.
+            batch (dict[str, torch.Tensor]): Batch dictionary containing 'cls' and 'bboxes' keys with ground truth
+                class labels and bounding boxes.
+
+        Returns:
+            (dict[str, np.ndarray]): Dictionary containing 'tp' key with the correct prediction matrix as a numpy
+                array with shape (N, 10), which includes 10 IoU levels for each detection, indicating the accuracy
+                of predictions compared to the ground truth.
+
+        Examples:
+            >>> detections = torch.rand(100, 7)  # 100 sample detections
+            >>> gt_bboxes = torch.rand(50, 5)  # 50 sample ground truth boxes
+            >>> gt_cls = torch.randint(0, 5, (50,))  # 50 ground truth class labels
+            >>> correct_matrix = validator._process_batch(detections, gt_bboxes, gt_cls)
+        """
+        if batch["cls"].shape[0] == 0 or preds["cls"].shape[0] == 0:
+            return {"tp": np.zeros((preds["cls"].shape[0], self.niou), dtype=bool)}
+        iou = batch_probiou(batch["bboxes"], preds["bboxes"])
+        return {"tp": self.match_predictions(preds["cls"], batch["cls"], iou).cpu().numpy()}
+
+    def postprocess(self, preds: torch.Tensor) -> list[dict[str, torch.Tensor]]:
+        """
+        Args:
+            preds (torch.Tensor): Raw predictions from the model.
+
+        Returns:
+            (list[dict[str, torch.Tensor]]): Processed predictions with angle information concatenated to bboxes.
+        """
+        preds = super().postprocess(preds)
+        for pred in preds:
+            pred["bboxes"] = torch.cat([pred["bboxes"], pred.pop("extra")], dim=-1)  # concatenate angle
+        return preds
+
+    def _prepare_batch(self, si: int, batch: dict[str, Any]) -> dict[str, Any]:
+        """
+        Prepare batch data for OBB validation with proper scaling and formatting.
+
+        Args:
+            si (int): Batch index to process.
+            batch (dict[str, Any]): Dictionary containing batch data with keys:
+                - batch_idx: Tensor of batch indices
+                - cls: Tensor of class labels
+                - bboxes: Tensor of bounding boxes
+                - ori_shape: Original image shapes
+                - img: Batch of images
+                - ratio_pad: Ratio and padding information
+
+        Returns:
+            (dict[str, Any]): Prepared batch data with scaled bounding boxes and metadata.
+        """
+        idx = batch["batch_idx"] == si
+        cls = batch["cls"][idx].squeeze(-1)
+        bbox = batch["bboxes"][idx]
+        ori_shape = batch["ori_shape"][si]
+        imgsz = batch["img"].shape[2:]
+        ratio_pad = batch["ratio_pad"][si]
+        if cls.shape[0]:
+            bbox[..., :4].mul_(torch.tensor(imgsz, device=self.device)[[1, 0, 1, 0]])  # target boxes
+        return {
+            "cls": cls,
+            "bboxes": bbox,
+            "ori_shape": ori_shape,
+            "imgsz": imgsz,
+            "ratio_pad": ratio_pad,
+            "im_file": batch["im_file"][si],
+        }
+
+    def plot_predictions(self, batch: dict[str, Any], preds: list[torch.Tensor], ni: int) -> None:
+        """
+        Plot predicted bounding boxes on input images and save the result.
+
+        Args:
+            batch (dict[str, Any]): Batch data containing images, file paths, and other metadata.
+            preds (list[torch.Tensor]): List of prediction tensors for each image in the batch.
+            ni (int): Batch index used for naming the output file.
+
+        Examples:
+            >>> validator = OBBValidator()
+            >>> batch = {"img": images, "im_file": paths}
+            >>> preds = [torch.rand(10, 7)]  # Example predictions for one image
+            >>> validator.plot_predictions(batch, preds, 0)
+        """
+        for p in preds:
+            # TODO: fix this duplicated `xywh2xyxy`
+            p["bboxes"][:, :4] = ops.xywh2xyxy(p["bboxes"][:, :4])  # convert to xyxy format for plotting
+        super().plot_predictions(batch, preds, ni)  # plot bboxes
+
+    def pred_to_json(self, predn: dict[str, torch.Tensor], pbatch: dict[str, Any]) -> None:
+        """
+        Convert YOLO predictions to COCO JSON format with rotated bounding box information.
+
+        Args:
+            predn (dict[str, torch.Tensor]): Prediction dictionary containing 'bboxes', 'conf', and 'cls' keys
+                with bounding box coordinates, confidence scores, and class predictions.
+            pbatch (dict[str, Any]): Batch dictionary containing 'imgsz', 'ori_shape', 'ratio_pad', and 'im_file'.
+
+        Notes:
+            This method processes rotated bounding box predictions and converts them to both rbox format
+            (x, y, w, h, angle) and polygon format (x1, y1, x2, y2, x3, y3, x4, y4) before adding them
+            to the JSON dictionary.
+        """
+        path = Path(pbatch["im_file"])
+        stem = path.stem
+        image_id = int(stem) if stem.isnumeric() else stem
+        rbox = predn["bboxes"]
+        poly = ops.xywhr2xyxyxyxy(rbox).view(-1, 8)
+        for r, b, s, c in zip(rbox.tolist(), poly.tolist(), predn["conf"].tolist(), predn["cls"].tolist()):
+            self.jdict.append(
+                {
+                    "image_id": image_id,
+                    "file_name": path.name,
+                    "category_id": self.class_map[int(c)],
+                    "score": round(s, 5),
+                    "rbox": [round(x, 3) for x in r],
+                    "poly": [round(x, 3) for x in b],
+                }
+            )
+
+    def save_one_txt(self, predn: dict[str, torch.Tensor], save_conf: bool, shape: tuple[int, int], file: Path) -> None:
+        """
+        Save YOLO OBB detections to a text file in normalized coordinates.
+
+        Args:
+            predn (torch.Tensor): Predicted detections with shape (N, 7) containing bounding boxes, confidence scores,
+                class predictions, and angles in format (x, y, w, h, conf, cls, angle).
+            save_conf (bool): Whether to save confidence scores in the text file.
+            shape (tuple[int, int]): Original image shape in format (height, width).
+            file (Path): Output file path to save detections.
+
+        Examples:
+            >>> validator = OBBValidator()
+            >>> predn = torch.tensor([[100, 100, 50, 30, 0.9, 0, 45]])  # One detection: x,y,w,h,conf,cls,angle
+            >>> validator.save_one_txt(predn, True, (640, 480), "detection.txt")
+        """
+        import numpy as np
+
+        from ultralytics.engine.results import Results
+
+        Results(
+            np.zeros((shape[0], shape[1]), dtype=np.uint8),
+            path=None,
+            names=self.names,
+            obb=torch.cat([predn["bboxes"], predn["conf"].unsqueeze(-1), predn["cls"].unsqueeze(-1)], dim=1),
+        ).save_txt(file, save_conf=save_conf)
+
+    def scale_preds(self, predn: dict[str, torch.Tensor], pbatch: dict[str, Any]) -> dict[str, torch.Tensor]:
+        """Scales predictions to the original image size."""
+        return {
+            **predn,
+            "bboxes": ops.scale_boxes(
+                pbatch["imgsz"], predn["bboxes"].clone(), pbatch["ori_shape"], ratio_pad=pbatch["ratio_pad"], xywh=True
+            ),
+        }
+
+    def eval_json(self, stats: dict[str, Any]) -> dict[str, Any]:
+        """
+        Evaluate YOLO output in JSON format and save predictions in DOTA format.
+
+        Args:
+            stats (dict[str, Any]): Performance statistics dictionary.
+
+        Returns:
+            (dict[str, Any]): Updated performance statistics.
+        """
+        if self.args.save_json and self.is_dota and len(self.jdict):
+            import json
+            import re
+            from collections import defaultdict
+
+            pred_json = self.save_dir / "predictions.json"  # predictions
+            pred_txt = self.save_dir / "predictions_txt"  # predictions
+            pred_txt.mkdir(parents=True, exist_ok=True)
+            data = json.load(open(pred_json))
+            # Save split results
+            LOGGER.info(f"Saving predictions with DOTA format to {pred_txt}...")
+            for d in data:
+                image_id = d["image_id"]
+                score = d["score"]
+                classname = self.names[d["category_id"] - 1].replace(" ", "-")
+                p = d["poly"]
+
+                with open(f"{pred_txt / f'Task1_{classname}'}.txt", "a", encoding="utf-8") as f:
+                    f.writelines(f"{image_id} {score} {p[0]} {p[1]} {p[2]} {p[3]} {p[4]} {p[5]} {p[6]} {p[7]}\n")
+            # Save merged results, this could result slightly lower map than using official merging script,
+            # because of the probiou calculation.
+            pred_merged_txt = self.save_dir / "predictions_merged_txt"  # predictions
+            pred_merged_txt.mkdir(parents=True, exist_ok=True)
+            merged_results = defaultdict(list)
+            LOGGER.info(f"Saving merged predictions with DOTA format to {pred_merged_txt}...")
+            for d in data:
+                image_id = d["image_id"].split("__", 1)[0]
+                pattern = re.compile(r"\d+___\d+")
+                x, y = (int(c) for c in re.findall(pattern, d["image_id"])[0].split("___"))
+                bbox, score, cls = d["rbox"], d["score"], d["category_id"] - 1
+                bbox[0] += x
+                bbox[1] += y
+                bbox.extend([score, cls])
+                merged_results[image_id].append(bbox)
+            for image_id, bbox in merged_results.items():
+                bbox = torch.tensor(bbox)
+                max_wh = torch.max(bbox[:, :2]).item() * 2
+                c = bbox[:, 6:7] * max_wh  # classes
+                scores = bbox[:, 5]  # scores
+                b = bbox[:, :5].clone()
+                b[:, :2] += c
+                # 0.3 could get results close to the ones from official merging script, even slightly better.
+                i = TorchNMS.fast_nms(b, scores, 0.3, iou_func=batch_probiou)
+                bbox = bbox[i]
+
+                b = ops.xywhr2xyxyxyxy(bbox[:, :5]).view(-1, 8)
+                for x in torch.cat([b, bbox[:, 5:7]], dim=-1).tolist():
+                    classname = self.names[int(x[-1])].replace(" ", "-")
+                    p = [round(i, 3) for i in x[:-2]]  # poly
+                    score = round(x[-2], 3)
+
+                    with open(f"{pred_merged_txt / f'Task1_{classname}'}.txt", "a", encoding="utf-8") as f:
+                        f.writelines(f"{image_id} {score} {p[0]} {p[1]} {p[2]} {p[3]} {p[4]} {p[5]} {p[6]} {p[7]}\n")
+
+        return stats

ultralytics/models/yolo/pose/__init__.py

@@ -0,0 +1,7 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from .predict import PosePredictor
+from .train import PoseTrainer
+from .val import PoseValidator
+
+__all__ = "PoseTrainer", "PoseValidator", "PosePredictor"

ultralytics/models/yolo/pose/predict.py

@@ -0,0 +1,80 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from ultralytics.models.yolo.detect.predict import DetectionPredictor
+from ultralytics.utils import DEFAULT_CFG, LOGGER, ops
+
+
+class PosePredictor(DetectionPredictor):
+    """
+    A class extending the DetectionPredictor class for prediction based on a pose model.
+
+    This class specializes in pose estimation, handling keypoints detection alongside standard object detection
+    capabilities inherited from DetectionPredictor.
+
+    Attributes:
+        args (namespace): Configuration arguments for the predictor.
+        model (torch.nn.Module): The loaded YOLO pose model with keypoint detection capabilities.
+
+    Methods:
+        construct_result: Construct the result object from the prediction, including keypoints.
+
+    Examples:
+        >>> from ultralytics.utils import ASSETS
+        >>> from ultralytics.models.yolo.pose import PosePredictor
+        >>> args = dict(model="yolo11n-pose.pt", source=ASSETS)
+        >>> predictor = PosePredictor(overrides=args)
+        >>> predictor.predict_cli()
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """
+        Initialize PosePredictor for pose estimation tasks.
+
+        Sets up a PosePredictor instance, configuring it for pose detection tasks and handling device-specific
+        warnings for Apple MPS.
+
+        Args:
+            cfg (Any): Configuration for the predictor.
+            overrides (dict, optional): Configuration overrides that take precedence over cfg.
+            _callbacks (list, optional): List of callback functions to be invoked during prediction.
+
+        Examples:
+            >>> from ultralytics.utils import ASSETS
+            >>> from ultralytics.models.yolo.pose import PosePredictor
+            >>> args = dict(model="yolo11n-pose.pt", source=ASSETS)
+            >>> predictor = PosePredictor(overrides=args)
+            >>> predictor.predict_cli()
+        """
+        super().__init__(cfg, overrides, _callbacks)
+        self.args.task = "pose"
+        if isinstance(self.args.device, str) and self.args.device.lower() == "mps":
+            LOGGER.warning(
+                "Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. "
+                "See https://github.com/ultralytics/ultralytics/issues/4031."
+            )
+
+    def construct_result(self, pred, img, orig_img, img_path):
+        """
+        Construct the result object from the prediction, including keypoints.
+
+        Extends the parent class implementation by extracting keypoint data from predictions and adding them to the
+        result object.
+
+        Args:
+            pred (torch.Tensor): The predicted bounding boxes, scores, and keypoints with shape (N, 6+K*D) where N is
+                the number of detections, K is the number of keypoints, and D is the keypoint dimension.
+            img (torch.Tensor): The processed input image tensor with shape (B, C, H, W).
+            orig_img (np.ndarray): The original unprocessed image as a numpy array.
+            img_path (str): The path to the original image file.
+
+        Returns:
+            (Results): The result object containing the original image, image path, class names, bounding boxes, and
+                keypoints.
+        """
+        result = super().construct_result(pred, img, orig_img, img_path)
+        # Extract keypoints from prediction and reshape according to model's keypoint shape
+        pred_kpts = pred[:, 6:].view(pred.shape[0], *self.model.kpt_shape)
+        # Scale keypoints coordinates to match the original image dimensions
+        pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, orig_img.shape)
+        result.update(keypoints=pred_kpts)
+        return result

ultralytics/models/yolo/pose/train.py

@@ -0,0 +1,115 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from copy import copy
+from pathlib import Path
+from typing import Any
+
+from ultralytics.models import yolo
+from ultralytics.nn.tasks import PoseModel
+from ultralytics.utils import DEFAULT_CFG, LOGGER
+
+
+class PoseTrainer(yolo.detect.DetectionTrainer):
+    """
+    A class extending the DetectionTrainer class for training YOLO pose estimation models.
+
+    This trainer specializes in handling pose estimation tasks, managing model training, validation, and visualization
+    of pose keypoints alongside bounding boxes.
+
+    Attributes:
+        args (dict): Configuration arguments for training.
+        model (PoseModel): The pose estimation model being trained.
+        data (dict): Dataset configuration including keypoint shape information.
+        loss_names (tuple): Names of the loss components used in training.
+
+    Methods:
+        get_model: Retrieve a pose estimation model with specified configuration.
+        set_model_attributes: Set keypoints shape attribute on the model.
+        get_validator: Create a validator instance for model evaluation.
+        plot_training_samples: Visualize training samples with keypoints.
+        get_dataset: Retrieve the dataset and ensure it contains required kpt_shape key.
+
+    Examples:
+        >>> from ultralytics.models.yolo.pose import PoseTrainer
+        >>> args = dict(model="yolo11n-pose.pt", data="coco8-pose.yaml", epochs=3)
+        >>> trainer = PoseTrainer(overrides=args)
+        >>> trainer.train()
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides: dict[str, Any] | None = None, _callbacks=None):
+        """
+        Initialize a PoseTrainer object for training YOLO pose estimation models.
+
+        Args:
+            cfg (dict, optional): Default configuration dictionary containing training parameters.
+            overrides (dict, optional): Dictionary of parameter overrides for the default configuration.
+            _callbacks (list, optional): List of callback functions to be executed during training.
+
+        Notes:
+            This trainer will automatically set the task to 'pose' regardless of what is provided in overrides.
+            A warning is issued when using Apple MPS device due to known bugs with pose models.
+        """
+        if overrides is None:
+            overrides = {}
+        overrides["task"] = "pose"
+        super().__init__(cfg, overrides, _callbacks)
+
+        if isinstance(self.args.device, str) and self.args.device.lower() == "mps":
+            LOGGER.warning(
+                "Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. "
+                "See https://github.com/ultralytics/ultralytics/issues/4031."
+            )
+
+    def get_model(
+        self,
+        cfg: str | Path | dict[str, Any] | None = None,
+        weights: str | Path | None = None,
+        verbose: bool = True,
+    ) -> PoseModel:
+        """
+        Get pose estimation model with specified configuration and weights.
+
+        Args:
+            cfg (str | Path | dict, optional): Model configuration file path or dictionary.
+            weights (str | Path, optional): Path to the model weights file.
+            verbose (bool): Whether to display model information.
+
+        Returns:
+            (PoseModel): Initialized pose estimation model.
+        """
+        model = PoseModel(
+            cfg, nc=self.data["nc"], ch=self.data["channels"], data_kpt_shape=self.data["kpt_shape"], verbose=verbose
+        )
+        if weights:
+            model.load(weights)
+
+        return model
+
+    def set_model_attributes(self):
+        """Set keypoints shape attribute of PoseModel."""
+        super().set_model_attributes()
+        self.model.kpt_shape = self.data["kpt_shape"]
+
+    def get_validator(self):
+        """Return an instance of the PoseValidator class for validation."""
+        self.loss_names = "box_loss", "pose_loss", "kobj_loss", "cls_loss", "dfl_loss"
+        return yolo.pose.PoseValidator(
+            self.test_loader, save_dir=self.save_dir, args=copy(self.args), _callbacks=self.callbacks
+        )
+
+    def get_dataset(self) -> dict[str, Any]:
+        """
+        Retrieve the dataset and ensure it contains the required `kpt_shape` key.
+
+        Returns:
+            (dict): A dictionary containing the training/validation/test dataset and category names.
+
+        Raises:
+            KeyError: If the `kpt_shape` key is not present in the dataset.
+        """
+        data = super().get_dataset()
+        if "kpt_shape" not in data:
+            raise KeyError(f"No `kpt_shape` in the {self.args.data}. See https://docs.ultralytics.com/datasets/pose/")
+        return data

ultralytics/models/yolo/pose/val.py

@@ -0,0 +1,267 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+
+from ultralytics.models.yolo.detect import DetectionValidator
+from ultralytics.utils import LOGGER, ops
+from ultralytics.utils.metrics import OKS_SIGMA, PoseMetrics, kpt_iou
+
+
+class PoseValidator(DetectionValidator):
+    """
+    A class extending the DetectionValidator class for validation based on a pose model.
+
+    This validator is specifically designed for pose estimation tasks, handling keypoints and implementing
+    specialized metrics for pose evaluation.
+
+    Attributes:
+        sigma (np.ndarray): Sigma values for OKS calculation, either OKS_SIGMA or ones divided by number of keypoints.
+        kpt_shape (list[int]): Shape of the keypoints, typically [17, 3] for COCO format.
+        args (dict): Arguments for the validator including task set to "pose".
+        metrics (PoseMetrics): Metrics object for pose evaluation.
+
+    Methods:
+        preprocess: Preprocess batch by converting keypoints data to float and moving it to the device.
+        get_desc: Return description of evaluation metrics in string format.
+        init_metrics: Initialize pose estimation metrics for YOLO model.
+        _prepare_batch: Prepare a batch for processing by converting keypoints to float and scaling to original
+            dimensions.
+        _prepare_pred: Prepare and scale keypoints in predictions for pose processing.
+        _process_batch: Return correct prediction matrix by computing Intersection over Union (IoU) between
+            detections and ground truth.
+        plot_val_samples: Plot and save validation set samples with ground truth bounding boxes and keypoints.
+        plot_predictions: Plot and save model predictions with bounding boxes and keypoints.
+        save_one_txt: Save YOLO pose detections to a text file in normalized coordinates.
+        pred_to_json: Convert YOLO predictions to COCO JSON format.
+        eval_json: Evaluate object detection model using COCO JSON format.
+
+    Examples:
+        >>> from ultralytics.models.yolo.pose import PoseValidator
+        >>> args = dict(model="yolo11n-pose.pt", data="coco8-pose.yaml")
+        >>> validator = PoseValidator(args=args)
+        >>> validator()
+    """
+
+    def __init__(self, dataloader=None, save_dir=None, args=None, _callbacks=None) -> None:
+        """
+        Initialize a PoseValidator object for pose estimation validation.
+
+        This validator is specifically designed for pose estimation tasks, handling keypoints and implementing
+        specialized metrics for pose evaluation.
+
+        Args:
+            dataloader (torch.utils.data.DataLoader, optional): Dataloader to be used for validation.
+            save_dir (Path | str, optional): Directory to save results.
+            args (dict, optional): Arguments for the validator including task set to "pose".
+            _callbacks (list, optional): List of callback functions to be executed during validation.
+
+        Examples:
+            >>> from ultralytics.models.yolo.pose import PoseValidator
+            >>> args = dict(model="yolo11n-pose.pt", data="coco8-pose.yaml")
+            >>> validator = PoseValidator(args=args)
+            >>> validator()
+
+        Notes:
+            This class extends DetectionValidator with pose-specific functionality. It initializes with sigma values
+            for OKS calculation and sets up PoseMetrics for evaluation. A warning is displayed when using Apple MPS
+            due to a known bug with pose models.
+        """
+        super().__init__(dataloader, save_dir, args, _callbacks)
+        self.sigma = None
+        self.kpt_shape = None
+        self.args.task = "pose"
+        self.metrics = PoseMetrics()
+        if isinstance(self.args.device, str) and self.args.device.lower() == "mps":
+            LOGGER.warning(
+                "Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. "
+                "See https://github.com/ultralytics/ultralytics/issues/4031."
+            )
+
+    def preprocess(self, batch: dict[str, Any]) -> dict[str, Any]:
+        """Preprocess batch by converting keypoints data to float and moving it to the device."""
+        batch = super().preprocess(batch)
+        batch["keypoints"] = batch["keypoints"].float()
+        return batch
+
+    def get_desc(self) -> str:
+        """Return description of evaluation metrics in string format."""
+        return ("%22s" + "%11s" * 10) % (
+            "Class",
+            "Images",
+            "Instances",
+            "Box(P",
+            "R",
+            "mAP50",
+            "mAP50-95)",
+            "Pose(P",
+            "R",
+            "mAP50",
+            "mAP50-95)",
+        )
+
+    def init_metrics(self, model: torch.nn.Module) -> None:
+        """
+        Initialize evaluation metrics for YOLO pose validation.
+
+        Args:
+            model (torch.nn.Module): Model to validate.
+        """
+        super().init_metrics(model)
+        self.kpt_shape = self.data["kpt_shape"]
+        is_pose = self.kpt_shape == [17, 3]
+        nkpt = self.kpt_shape[0]
+        self.sigma = OKS_SIGMA if is_pose else np.ones(nkpt) / nkpt
+
+    def postprocess(self, preds: torch.Tensor) -> dict[str, torch.Tensor]:
+        """
+        Postprocess YOLO predictions to extract and reshape keypoints for pose estimation.
+
+        This method extends the parent class postprocessing by extracting keypoints from the 'extra'
+        field of predictions and reshaping them according to the keypoint shape configuration.
+        The keypoints are reshaped from a flattened format to the proper dimensional structure
+        (typically [N, 17, 3] for COCO pose format).
+
+        Args:
+            preds (torch.Tensor): Raw prediction tensor from the YOLO pose model containing
+                bounding boxes, confidence scores, class predictions, and keypoint data.
+
+        Returns:
+            (dict[torch.Tensor]): Dict of processed prediction dictionaries, each containing:
+                - 'bboxes': Bounding box coordinates
+                - 'conf': Confidence scores
+                - 'cls': Class predictions
+                - 'keypoints': Reshaped keypoint coordinates with shape (-1, *self.kpt_shape)
+
+        Note:
+            If no keypoints are present in a prediction (empty keypoints), that prediction
+            is skipped and continues to the next one. The keypoints are extracted from the
+            'extra' field which contains additional task-specific data beyond basic detection.
+        """
+        preds = super().postprocess(preds)
+        for pred in preds:
+            pred["keypoints"] = pred.pop("extra").view(-1, *self.kpt_shape)  # remove extra if exists
+        return preds
+
+    def _prepare_batch(self, si: int, batch: dict[str, Any]) -> dict[str, Any]:
+        """
+        Prepare a batch for processing by converting keypoints to float and scaling to original dimensions.
+
+        Args:
+            si (int): Batch index.
+            batch (dict[str, Any]): Dictionary containing batch data with keys like 'keypoints', 'batch_idx', etc.
+
+        Returns:
+            (dict[str, Any]): Prepared batch with keypoints scaled to original image dimensions.
+
+        Notes:
+            This method extends the parent class's _prepare_batch method by adding keypoint processing.
+            Keypoints are scaled from normalized coordinates to original image dimensions.
+        """
+        pbatch = super()._prepare_batch(si, batch)
+        kpts = batch["keypoints"][batch["batch_idx"] == si]
+        h, w = pbatch["imgsz"]
+        kpts = kpts.clone()
+        kpts[..., 0] *= w
+        kpts[..., 1] *= h
+        pbatch["keypoints"] = kpts
+        return pbatch
+
+    def _process_batch(self, preds: dict[str, torch.Tensor], batch: dict[str, Any]) -> dict[str, np.ndarray]:
+        """
+        Return correct prediction matrix by computing Intersection over Union (IoU) between detections and ground truth.
+
+        Args:
+            preds (dict[str, torch.Tensor]): Dictionary containing prediction data with keys 'cls' for class predictions
+                and 'keypoints' for keypoint predictions.
+            batch (dict[str, Any]): Dictionary containing ground truth data with keys 'cls' for class labels,
+                'bboxes' for bounding boxes, and 'keypoints' for keypoint annotations.
+
+        Returns:
+            (dict[str, np.ndarray]): Dictionary containing the correct prediction matrix including 'tp_p' for pose
+                true positives across 10 IoU levels.
+
+        Notes:
+            `0.53` scale factor used in area computation is referenced from
+            https://github.com/jin-s13/xtcocoapi/blob/master/xtcocotools/cocoeval.py#L384.
+        """
+        tp = super()._process_batch(preds, batch)
+        gt_cls = batch["cls"]
+        if gt_cls.shape[0] == 0 or preds["cls"].shape[0] == 0:
+            tp_p = np.zeros((preds["cls"].shape[0], self.niou), dtype=bool)
+        else:
+            # `0.53` is from https://github.com/jin-s13/xtcocoapi/blob/master/xtcocotools/cocoeval.py#L384
+            area = ops.xyxy2xywh(batch["bboxes"])[:, 2:].prod(1) * 0.53
+            iou = kpt_iou(batch["keypoints"], preds["keypoints"], sigma=self.sigma, area=area)
+            tp_p = self.match_predictions(preds["cls"], gt_cls, iou).cpu().numpy()
+        tp.update({"tp_p": tp_p})  # update tp with kpts IoU
+        return tp
+
+    def save_one_txt(self, predn: dict[str, torch.Tensor], save_conf: bool, shape: tuple[int, int], file: Path) -> None:
+        """
+        Save YOLO pose detections to a text file in normalized coordinates.
+
+        Args:
+            predn (dict[str, torch.Tensor]): Dictionary containing predictions with keys 'bboxes', 'conf', 'cls' and 'keypoints.
+            save_conf (bool): Whether to save confidence scores.
+            shape (tuple[int, int]): Shape of the original image (height, width).
+            file (Path): Output file path to save detections.
+
+        Notes:
+            The output format is: class_id x_center y_center width height confidence keypoints where keypoints are
+            normalized (x, y, visibility) values for each point.
+        """
+        from ultralytics.engine.results import Results
+
+        Results(
+            np.zeros((shape[0], shape[1]), dtype=np.uint8),
+            path=None,
+            names=self.names,
+            boxes=torch.cat([predn["bboxes"], predn["conf"].unsqueeze(-1), predn["cls"].unsqueeze(-1)], dim=1),
+            keypoints=predn["keypoints"],
+        ).save_txt(file, save_conf=save_conf)
+
+    def pred_to_json(self, predn: dict[str, torch.Tensor], pbatch: dict[str, Any]) -> None:
+        """
+        Convert YOLO predictions to COCO JSON format.
+
+        This method takes prediction tensors and a filename, converts the bounding boxes from YOLO format
+        to COCO format, and appends the results to the internal JSON dictionary (self.jdict).
+
+        Args:
+            predn (dict[str, torch.Tensor]): Prediction dictionary containing 'bboxes', 'conf', 'cls',
+                and 'keypoints' tensors.
+            pbatch (dict[str, Any]): Batch dictionary containing 'imgsz', 'ori_shape', 'ratio_pad', and 'im_file'.
+
+        Notes:
+            The method extracts the image ID from the filename stem (either as an integer if numeric, or as a string),
+            converts bounding boxes from xyxy to xywh format, and adjusts coordinates from center to top-left corner
+            before saving to the JSON dictionary.
+        """
+        super().pred_to_json(predn, pbatch)
+        kpts = predn["kpts"]
+        for i, k in enumerate(kpts.flatten(1, 2).tolist()):
+            self.jdict[-len(kpts) + i]["keypoints"] = k  # keypoints
+
+    def scale_preds(self, predn: dict[str, torch.Tensor], pbatch: dict[str, Any]) -> dict[str, torch.Tensor]:
+        """Scales predictions to the original image size."""
+        return {
+            **super().scale_preds(predn, pbatch),
+            "kpts": ops.scale_coords(
+                pbatch["imgsz"],
+                predn["keypoints"].clone(),
+                pbatch["ori_shape"],
+                ratio_pad=pbatch["ratio_pad"],
+            ),
+        }
+
+    def eval_json(self, stats: dict[str, Any]) -> dict[str, Any]:
+        """Evaluate object detection model using COCO JSON format."""
+        anno_json = self.data["path"] / "annotations/person_keypoints_val2017.json"  # annotations
+        pred_json = self.save_dir / "predictions.json"  # predictions
+        return super().coco_evaluate(stats, pred_json, anno_json, ["bbox", "keypoints"], suffix=["Box", "Pose"])

ultralytics/models/yolo/segment/__init__.py

@@ -0,0 +1,7 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from .predict import SegmentationPredictor
+from .train import SegmentationTrainer
+from .val import SegmentationValidator
+
+__all__ = "SegmentationPredictor", "SegmentationTrainer", "SegmentationValidator"

ultralytics/models/yolo/segment/predict.py

@@ -0,0 +1,113 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from ultralytics.engine.results import Results
+from ultralytics.models.yolo.detect.predict import DetectionPredictor
+from ultralytics.utils import DEFAULT_CFG, ops
+
+
+class SegmentationPredictor(DetectionPredictor):
+    """
+    A class extending the DetectionPredictor class for prediction based on a segmentation model.
+
+    This class specializes in processing segmentation model outputs, handling both bounding boxes and masks in the
+    prediction results.
+
+    Attributes:
+        args (dict): Configuration arguments for the predictor.
+        model (torch.nn.Module): The loaded YOLO segmentation model.
+        batch (list): Current batch of images being processed.
+
+    Methods:
+        postprocess: Apply non-max suppression and process segmentation detections.
+        construct_results: Construct a list of result objects from predictions.
+        construct_result: Construct a single result object from a prediction.
+
+    Examples:
+        >>> from ultralytics.utils import ASSETS
+        >>> from ultralytics.models.yolo.segment import SegmentationPredictor
+        >>> args = dict(model="yolo11n-seg.pt", source=ASSETS)
+        >>> predictor = SegmentationPredictor(overrides=args)
+        >>> predictor.predict_cli()
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """
+        Initialize the SegmentationPredictor with configuration, overrides, and callbacks.
+
+        This class specializes in processing segmentation model outputs, handling both bounding boxes and masks in the
+        prediction results.
+
+        Args:
+            cfg (dict): Configuration for the predictor.
+            overrides (dict, optional): Configuration overrides that take precedence over cfg.
+            _callbacks (list, optional): List of callback functions to be invoked during prediction.
+        """
+        super().__init__(cfg, overrides, _callbacks)
+        self.args.task = "segment"
+
+    def postprocess(self, preds, img, orig_imgs):
+        """
+        Apply non-max suppression and process segmentation detections for each image in the input batch.
+
+        Args:
+            preds (tuple): Model predictions, containing bounding boxes, scores, classes, and mask coefficients.
+            img (torch.Tensor): Input image tensor in model format, with shape (B, C, H, W).
+            orig_imgs (list | torch.Tensor | np.ndarray): Original image or batch of images.
+
+        Returns:
+            (list): List of Results objects containing the segmentation predictions for each image in the batch.
+                Each Results object includes both bounding boxes and segmentation masks.
+
+        Examples:
+            >>> predictor = SegmentationPredictor(overrides=dict(model="yolo11n-seg.pt"))
+            >>> results = predictor.postprocess(preds, img, orig_img)
+        """
+        # Extract protos - tuple if PyTorch model or array if exported
+        protos = preds[1][-1] if isinstance(preds[1], tuple) else preds[1]
+        return super().postprocess(preds[0], img, orig_imgs, protos=protos)
+
+    def construct_results(self, preds, img, orig_imgs, protos):
+        """
+        Construct a list of result objects from the predictions.
+
+        Args:
+            preds (list[torch.Tensor]): List of predicted bounding boxes, scores, and masks.
+            img (torch.Tensor): The image after preprocessing.
+            orig_imgs (list[np.ndarray]): List of original images before preprocessing.
+            protos (list[torch.Tensor]): List of prototype masks.
+
+        Returns:
+            (list[Results]): List of result objects containing the original images, image paths, class names,
+                bounding boxes, and masks.
+        """
+        return [
+            self.construct_result(pred, img, orig_img, img_path, proto)
+            for pred, orig_img, img_path, proto in zip(preds, orig_imgs, self.batch[0], protos)
+        ]
+
+    def construct_result(self, pred, img, orig_img, img_path, proto):
+        """
+        Construct a single result object from the prediction.
+
+        Args:
+            pred (torch.Tensor): The predicted bounding boxes, scores, and masks.
+            img (torch.Tensor): The image after preprocessing.
+            orig_img (np.ndarray): The original image before preprocessing.
+            img_path (str): The path to the original image.
+            proto (torch.Tensor): The prototype masks.
+
+        Returns:
+            (Results): Result object containing the original image, image path, class names, bounding boxes, and masks.
+        """
+        if pred.shape[0] == 0:  # save empty boxes
+            masks = None
+        elif self.args.retina_masks:
+            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
+            masks = ops.process_mask_native(proto, pred[:, 6:], pred[:, :4], orig_img.shape[:2])  # HWC
+        else:
+            masks = ops.process_mask(proto, pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True)  # HWC
+            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
+        if masks is not None:
+            keep = masks.sum((-2, -1)) > 0  # only keep predictions with masks
+            pred, masks = pred[keep], masks[keep]
+        return Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks)

ultralytics/models/yolo/segment/train.py

@@ -0,0 +1,72 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from copy import copy
+from pathlib import Path
+
+from ultralytics.models import yolo
+from ultralytics.nn.tasks import SegmentationModel
+from ultralytics.utils import DEFAULT_CFG, RANK
+
+
+class SegmentationTrainer(yolo.detect.DetectionTrainer):
+    """
+    A class extending the DetectionTrainer class for training based on a segmentation model.
+
+    This trainer specializes in handling segmentation tasks, extending the detection trainer with segmentation-specific
+    functionality including model initialization, validation, and visualization.
+
+    Attributes:
+        loss_names (tuple[str]): Names of the loss components used during training.
+
+    Examples:
+        >>> from ultralytics.models.yolo.segment import SegmentationTrainer
+        >>> args = dict(model="yolo11n-seg.pt", data="coco8-seg.yaml", epochs=3)
+        >>> trainer = SegmentationTrainer(overrides=args)
+        >>> trainer.train()
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides: dict | None = None, _callbacks=None):
+        """
+        Initialize a SegmentationTrainer object.
+
+        Args:
+            cfg (dict): Configuration dictionary with default training settings.
+            overrides (dict, optional): Dictionary of parameter overrides for the default configuration.
+            _callbacks (list, optional): List of callback functions to be executed during training.
+        """
+        if overrides is None:
+            overrides = {}
+        overrides["task"] = "segment"
+        super().__init__(cfg, overrides, _callbacks)
+
+    def get_model(self, cfg: dict | str | None = None, weights: str | Path | None = None, verbose: bool = True):
+        """
+        Initialize and return a SegmentationModel with specified configuration and weights.
+
+        Args:
+            cfg (dict | str, optional): Model configuration. Can be a dictionary, a path to a YAML file, or None.
+            weights (str | Path, optional): Path to pretrained weights file.
+            verbose (bool): Whether to display model information during initialization.
+
+        Returns:
+            (SegmentationModel): Initialized segmentation model with loaded weights if specified.
+
+        Examples:
+            >>> trainer = SegmentationTrainer()
+            >>> model = trainer.get_model(cfg="yolo11n-seg.yaml")
+            >>> model = trainer.get_model(weights="yolo11n-seg.pt", verbose=False)
+        """
+        model = SegmentationModel(cfg, nc=self.data["nc"], ch=self.data["channels"], verbose=verbose and RANK == -1)
+        if weights:
+            model.load(weights)
+
+        return model
+
+    def get_validator(self):
+        """Return an instance of SegmentationValidator for validation of YOLO model."""
+        self.loss_names = "box_loss", "seg_loss", "cls_loss", "dfl_loss"
+        return yolo.segment.SegmentationValidator(
+            self.test_loader, save_dir=self.save_dir, args=copy(self.args), _callbacks=self.callbacks
+        )

ultralytics/models/yolo/segment/val.py

@@ -0,0 +1,259 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from multiprocessing.pool import ThreadPool
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from ultralytics.models.yolo.detect import DetectionValidator
+from ultralytics.utils import LOGGER, NUM_THREADS, ops
+from ultralytics.utils.checks import check_requirements
+from ultralytics.utils.metrics import SegmentMetrics, mask_iou
+
+
+class SegmentationValidator(DetectionValidator):
+    """
+    A class extending the DetectionValidator class for validation based on a segmentation model.
+
+    This validator handles the evaluation of segmentation models, processing both bounding box and mask predictions
+    to compute metrics such as mAP for both detection and segmentation tasks.
+
+    Attributes:
+        plot_masks (list): List to store masks for plotting.
+        process (callable): Function to process masks based on save_json and save_txt flags.
+        args (namespace): Arguments for the validator.
+        metrics (SegmentMetrics): Metrics calculator for segmentation tasks.
+        stats (dict): Dictionary to store statistics during validation.
+
+    Examples:
+        >>> from ultralytics.models.yolo.segment import SegmentationValidator
+        >>> args = dict(model="yolo11n-seg.pt", data="coco8-seg.yaml")
+        >>> validator = SegmentationValidator(args=args)
+        >>> validator()
+    """
+
+    def __init__(self, dataloader=None, save_dir=None, args=None, _callbacks=None) -> None:
+        """
+        Initialize SegmentationValidator and set task to 'segment', metrics to SegmentMetrics.
+
+        Args:
+            dataloader (torch.utils.data.DataLoader, optional): Dataloader to use for validation.
+            save_dir (Path, optional): Directory to save results.
+            args (namespace, optional): Arguments for the validator.
+            _callbacks (list, optional): List of callback functions.
+        """
+        super().__init__(dataloader, save_dir, args, _callbacks)
+        self.process = None
+        self.args.task = "segment"
+        self.metrics = SegmentMetrics()
+
+    def preprocess(self, batch: dict[str, Any]) -> dict[str, Any]:
+        """
+        Preprocess batch of images for YOLO segmentation validation.
+
+        Args:
+            batch (dict[str, Any]): Batch containing images and annotations.
+
+        Returns:
+            (dict[str, Any]): Preprocessed batch.
+        """
+        batch = super().preprocess(batch)
+        batch["masks"] = batch["masks"].float()
+        return batch
+
+    def init_metrics(self, model: torch.nn.Module) -> None:
+        """
+        Initialize metrics and select mask processing function based on save_json flag.
+
+        Args:
+            model (torch.nn.Module): Model to validate.
+        """
+        super().init_metrics(model)
+        if self.args.save_json:
+            check_requirements("faster-coco-eval>=1.6.7")
+        # More accurate vs faster
+        self.process = ops.process_mask_native if self.args.save_json or self.args.save_txt else ops.process_mask
+
+    def get_desc(self) -> str:
+        """Return a formatted description of evaluation metrics."""
+        return ("%22s" + "%11s" * 10) % (
+            "Class",
+            "Images",
+            "Instances",
+            "Box(P",
+            "R",
+            "mAP50",
+            "mAP50-95)",
+            "Mask(P",
+            "R",
+            "mAP50",
+            "mAP50-95)",
+        )
+
+    def postprocess(self, preds: list[torch.Tensor]) -> list[dict[str, torch.Tensor]]:
+        """
+        Post-process YOLO predictions and return output detections with proto.
+
+        Args:
+            preds (list[torch.Tensor]): Raw predictions from the model.
+
+        Returns:
+            list[dict[str, torch.Tensor]]: Processed detection predictions with masks.
+        """
+        proto = preds[1][-1] if len(preds[1]) == 3 else preds[1]  # second output is len 3 if pt, but only 1 if exported
+        preds = super().postprocess(preds[0])
+        imgsz = [4 * x for x in proto.shape[2:]]  # get image size from proto
+        for i, pred in enumerate(preds):
+            coefficient = pred.pop("extra")
+            pred["masks"] = (
+                self.process(proto[i], coefficient, pred["bboxes"], shape=imgsz)
+                if coefficient.shape[0]
+                else torch.zeros(
+                    (0, *(imgsz if self.process is ops.process_mask_native else proto.shape[2:])),
+                    dtype=torch.uint8,
+                    device=pred["bboxes"].device,
+                )
+            )
+        return preds
+
+    def _prepare_batch(self, si: int, batch: dict[str, Any]) -> dict[str, Any]:
+        """
+        Prepare a batch for training or inference by processing images and targets.
+
+        Args:
+            si (int): Batch index.
+            batch (dict[str, Any]): Batch data containing images and annotations.
+
+        Returns:
+            (dict[str, Any]): Prepared batch with processed annotations.
+        """
+        prepared_batch = super()._prepare_batch(si, batch)
+        nl = prepared_batch["cls"].shape[0]
+        if self.args.overlap_mask:
+            masks = batch["masks"][si]
+            index = torch.arange(1, nl + 1, device=masks.device).view(nl, 1, 1)
+            masks = (masks == index).float()
+        else:
+            masks = batch["masks"][batch["batch_idx"] == si]
+        if nl:
+            mask_size = [s if self.process is ops.process_mask_native else s // 4 for s in prepared_batch["imgsz"]]
+            if masks.shape[1:] != mask_size:
+                masks = F.interpolate(masks[None], mask_size, mode="bilinear", align_corners=False)[0]
+                masks = masks.gt_(0.5)
+        prepared_batch["masks"] = masks
+        return prepared_batch
+
+    def _process_batch(self, preds: dict[str, torch.Tensor], batch: dict[str, Any]) -> dict[str, np.ndarray]:
+        """
+        Compute correct prediction matrix for a batch based on bounding boxes and optional masks.
+
+        Args:
+            preds (dict[str, torch.Tensor]): Dictionary containing predictions with keys like 'cls' and 'masks'.
+            batch (dict[str, Any]): Dictionary containing batch data with keys like 'cls' and 'masks'.
+
+        Returns:
+            (dict[str, np.ndarray]): A dictionary containing correct prediction matrices including 'tp_m' for mask IoU.
+
+        Notes:
+            - If `masks` is True, the function computes IoU between predicted and ground truth masks.
+            - If `overlap` is True and `masks` is True, overlapping masks are taken into account when computing IoU.
+
+        Examples:
+            >>> preds = {"cls": torch.tensor([1, 0]), "masks": torch.rand(2, 640, 640), "bboxes": torch.rand(2, 4)}
+            >>> batch = {"cls": torch.tensor([1, 0]), "masks": torch.rand(2, 640, 640), "bboxes": torch.rand(2, 4)}
+            >>> correct_preds = validator._process_batch(preds, batch)
+        """
+        tp = super()._process_batch(preds, batch)
+        gt_cls = batch["cls"]
+        if gt_cls.shape[0] == 0 or preds["cls"].shape[0] == 0:
+            tp_m = np.zeros((preds["cls"].shape[0], self.niou), dtype=bool)
+        else:
+            iou = mask_iou(batch["masks"].flatten(1), preds["masks"].flatten(1))
+            tp_m = self.match_predictions(preds["cls"], gt_cls, iou).cpu().numpy()
+        tp.update({"tp_m": tp_m})  # update tp with mask IoU
+        return tp
+
+    def plot_predictions(self, batch: dict[str, Any], preds: list[dict[str, torch.Tensor]], ni: int) -> None:
+        """
+        Plot batch predictions with masks and bounding boxes.
+
+        Args:
+            batch (dict[str, Any]): Batch containing images and annotations.
+            preds (list[dict[str, torch.Tensor]]): List of predictions from the model.
+            ni (int): Batch index.
+        """
+        for p in preds:
+            masks = p["masks"]
+            if masks.shape[0] > self.args.max_det:
+                LOGGER.warning(f"Limiting validation plots to 'max_det={self.args.max_det}' items.")
+            p["masks"] = torch.as_tensor(masks[: self.args.max_det], dtype=torch.uint8).cpu()
+        super().plot_predictions(batch, preds, ni, max_det=self.args.max_det)  # plot bboxes
+
+    def save_one_txt(self, predn: torch.Tensor, save_conf: bool, shape: tuple[int, int], file: Path) -> None:
+        """
+        Save YOLO detections to a txt file in normalized coordinates in a specific format.
+
+        Args:
+            predn (torch.Tensor): Predictions in the format (x1, y1, x2, y2, conf, class).
+            save_conf (bool): Whether to save confidence scores.
+            shape (tuple[int, int]): Shape of the original image.
+            file (Path): File path to save the detections.
+        """
+        from ultralytics.engine.results import Results
+
+        Results(
+            np.zeros((shape[0], shape[1]), dtype=np.uint8),
+            path=None,
+            names=self.names,
+            boxes=torch.cat([predn["bboxes"], predn["conf"].unsqueeze(-1), predn["cls"].unsqueeze(-1)], dim=1),
+            masks=torch.as_tensor(predn["masks"], dtype=torch.uint8),
+        ).save_txt(file, save_conf=save_conf)
+
+    def pred_to_json(self, predn: dict[str, torch.Tensor], pbatch: dict[str, Any]) -> None:
+        """
+        Save one JSON result for COCO evaluation.
+
+        Args:
+            predn (dict[str, torch.Tensor]): Predictions containing bboxes, masks, confidence scores, and classes.
+            pbatch (dict[str, Any]): Batch dictionary containing 'imgsz', 'ori_shape', 'ratio_pad', and 'im_file'.
+        """
+        from faster_coco_eval.core.mask import encode  # noqa
+
+        def single_encode(x):
+            """Encode predicted masks as RLE and append results to jdict."""
+            rle = encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0]
+            rle["counts"] = rle["counts"].decode("utf-8")
+            return rle
+
+        pred_masks = np.transpose(predn["masks"], (2, 0, 1))
+        with ThreadPool(NUM_THREADS) as pool:
+            rles = pool.map(single_encode, pred_masks)
+        super().pred_to_json(predn, pbatch)
+        for i, r in enumerate(rles):
+            self.jdict[-len(rles) + i]["segmentation"] = r  # segmentation
+
+    def scale_preds(self, predn: dict[str, torch.Tensor], pbatch: dict[str, Any]) -> dict[str, torch.Tensor]:
+        """Scales predictions to the original image size."""
+        return {
+            **super().scale_preds(predn, pbatch),
+            "masks": ops.scale_image(
+                torch.as_tensor(predn["masks"], dtype=torch.uint8).permute(1, 2, 0).contiguous().cpu().numpy(),
+                pbatch["ori_shape"],
+                ratio_pad=pbatch["ratio_pad"],
+            ),
+        }
+
+    def eval_json(self, stats: dict[str, Any]) -> dict[str, Any]:
+        """Return COCO-style instance segmentation evaluation metrics."""
+        pred_json = self.save_dir / "predictions.json"  # predictions
+        anno_json = (
+            self.data["path"]
+            / "annotations"
+            / ("instances_val2017.json" if self.is_coco else f"lvis_v1_{self.args.split}.json")
+        )  # annotations
+        return super().coco_evaluate(stats, pred_json, anno_json, ["bbox", "segm"], suffix=["Box", "Mask"])

ultralytics/models/yolo/world/__init__.py

@@ -0,0 +1,5 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from .train import WorldTrainer
+
+__all__ = ["WorldTrainer"]

ultralytics/models/yolo/world/train.py

@@ -0,0 +1,179 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+import itertools
+from pathlib import Path
+from typing import Any
+
+import torch
+
+from ultralytics.data import build_yolo_dataset
+from ultralytics.models.yolo.detect import DetectionTrainer
+from ultralytics.nn.tasks import WorldModel
+from ultralytics.utils import DEFAULT_CFG, LOGGER, RANK
+from ultralytics.utils.torch_utils import unwrap_model
+
+
+def on_pretrain_routine_end(trainer) -> None:
+    """Set up model classes and text encoder at the end of the pretrain routine."""
+    if RANK in {-1, 0}:
+        # Set class names for evaluation
+        names = [name.split("/", 1)[0] for name in list(trainer.test_loader.dataset.data["names"].values())]
+        unwrap_model(trainer.ema.ema).set_classes(names, cache_clip_model=False)
+
+
+class WorldTrainer(DetectionTrainer):
+    """
+    A trainer class for fine-tuning YOLO World models on close-set datasets.
+
+    This trainer extends the DetectionTrainer to support training YOLO World models, which combine visual and textual
+    features for improved object detection and understanding. It handles text embedding generation and caching to
+    accelerate training with multi-modal data.
+
+    Attributes:
+        text_embeddings (dict[str, torch.Tensor] | None): Cached text embeddings for category names to accelerate
+            training.
+        model (WorldModel): The YOLO World model being trained.
+        data (dict[str, Any]): Dataset configuration containing class information.
+        args (Any): Training arguments and configuration.
+
+    Methods:
+        get_model: Return WorldModel initialized with specified config and weights.
+        build_dataset: Build YOLO Dataset for training or validation.
+        set_text_embeddings: Set text embeddings for datasets to accelerate training.
+        generate_text_embeddings: Generate text embeddings for a list of text samples.
+        preprocess_batch: Preprocess a batch of images and text for YOLOWorld training.
+
+    Examples:
+        Initialize and train a YOLO World model
+        >>> from ultralytics.models.yolo.world import WorldTrainer
+        >>> args = dict(model="yolov8s-world.pt", data="coco8.yaml", epochs=3)
+        >>> trainer = WorldTrainer(overrides=args)
+        >>> trainer.train()
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides: dict[str, Any] | None = None, _callbacks=None):
+        """
+        Initialize a WorldTrainer object with given arguments.
+
+        Args:
+            cfg (dict[str, Any]): Configuration for the trainer.
+            overrides (dict[str, Any], optional): Configuration overrides.
+            _callbacks (list[Any], optional): List of callback functions.
+        """
+        if overrides is None:
+            overrides = {}
+        assert not overrides.get("compile"), f"Training with 'model={overrides['model']}' requires 'compile=False'"
+        super().__init__(cfg, overrides, _callbacks)
+        self.text_embeddings = None
+
+    def get_model(self, cfg=None, weights: str | None = None, verbose: bool = True) -> WorldModel:
+        """
+        Return WorldModel initialized with specified config and weights.
+
+        Args:
+            cfg (dict[str, Any] | str, optional): Model configuration.
+            weights (str, optional): Path to pretrained weights.
+            verbose (bool): Whether to display model info.
+
+        Returns:
+            (WorldModel): Initialized WorldModel.
+        """
+        # NOTE: This `nc` here is the max number of different text samples in one image, rather than the actual `nc`.
+        # NOTE: Following the official config, nc hard-coded to 80 for now.
+        model = WorldModel(
+            cfg["yaml_file"] if isinstance(cfg, dict) else cfg,
+            ch=self.data["channels"],
+            nc=min(self.data["nc"], 80),
+            verbose=verbose and RANK == -1,
+        )
+        if weights:
+            model.load(weights)
+        self.add_callback("on_pretrain_routine_end", on_pretrain_routine_end)
+
+        return model
+
+    def build_dataset(self, img_path: str, mode: str = "train", batch: int | None = None):
+        """
+        Build YOLO Dataset for training or validation.
+
+        Args:
+            img_path (str): Path to the folder containing images.
+            mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
+            batch (int, optional): Size of batches, this is for `rect`.
+
+        Returns:
+            (Any): YOLO dataset configured for training or validation.
+        """
+        gs = max(int(unwrap_model(self.model).stride.max() if self.model else 0), 32)
+        dataset = build_yolo_dataset(
+            self.args, img_path, batch, self.data, mode=mode, rect=mode == "val", stride=gs, multi_modal=mode == "train"
+        )
+        if mode == "train":
+            self.set_text_embeddings([dataset], batch)  # cache text embeddings to accelerate training
+        return dataset
+
+    def set_text_embeddings(self, datasets: list[Any], batch: int | None) -> None:
+        """
+        Set text embeddings for datasets to accelerate training by caching category names.
+
+        This method collects unique category names from all datasets, then generates and caches text embeddings
+        for these categories to improve training efficiency.
+
+        Args:
+            datasets (list[Any]): List of datasets from which to extract category names.
+            batch (int | None): Batch size used for processing.
+
+        Notes:
+            This method collects category names from datasets that have the 'category_names' attribute,
+            then uses the first dataset's image path to determine where to cache the generated text embeddings.
+        """
+        text_embeddings = {}
+        for dataset in datasets:
+            if not hasattr(dataset, "category_names"):
+                continue
+            text_embeddings.update(
+                self.generate_text_embeddings(
+                    list(dataset.category_names), batch, cache_dir=Path(dataset.img_path).parent
+                )
+            )
+        self.text_embeddings = text_embeddings
+
+    def generate_text_embeddings(self, texts: list[str], batch: int, cache_dir: Path) -> dict[str, torch.Tensor]:
+        """
+        Generate text embeddings for a list of text samples.
+
+        Args:
+            texts (list[str]): List of text samples to encode.
+            batch (int): Batch size for processing.
+            cache_dir (Path): Directory to save/load cached embeddings.
+
+        Returns:
+            (dict[str, torch.Tensor]): Dictionary mapping text samples to their embeddings.
+        """
+        model = "clip:ViT-B/32"
+        cache_path = cache_dir / f"text_embeddings_{model.replace(':', '_').replace('/', '_')}.pt"
+        if cache_path.exists():
+            LOGGER.info(f"Reading existed cache from '{cache_path}'")
+            txt_map = torch.load(cache_path, map_location=self.device)
+            if sorted(txt_map.keys()) == sorted(texts):
+                return txt_map
+        LOGGER.info(f"Caching text embeddings to '{cache_path}'")
+        assert self.model is not None
+        txt_feats = unwrap_model(self.model).get_text_pe(texts, batch, cache_clip_model=False)
+        txt_map = dict(zip(texts, txt_feats.squeeze(0)))
+        torch.save(txt_map, cache_path)
+        return txt_map
+
+    def preprocess_batch(self, batch: dict[str, Any]) -> dict[str, Any]:
+        """Preprocess a batch of images and text for YOLOWorld training."""
+        batch = DetectionTrainer.preprocess_batch(self, batch)
+
+        # Add text features
+        texts = list(itertools.chain(*batch["texts"]))
+        txt_feats = torch.stack([self.text_embeddings[text] for text in texts]).to(
+            self.device, non_blocking=self.device.type == "cuda"
+        )
+        batch["txt_feats"] = txt_feats.reshape(len(batch["texts"]), -1, txt_feats.shape[-1])
+        return batch