diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..a04c59b Binary files /dev/null and b/.DS_Store differ diff --git a/Presentation.pdf b/Presentation.pdf new file mode 100644 index 0000000..64a8192 Binary files /dev/null and b/Presentation.pdf differ diff --git a/report.ipynb b/report.ipynb new file mode 100644 index 0000000..aa167c0 --- /dev/null +++ b/report.ipynb @@ -0,0 +1,24715 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "L4" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lPyZo-rMcUZ5", + "outputId": "46a63def-1e83-41e2-80cf-2909c1ddf994" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip -qq install ultralytics" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YC0R60jBePnc", + "outputId": "10d5a8ee-9574-487d-c526-2300f3b97609" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m25.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m103.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K 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Project/report/results/tuning/14c\"\n", + "\n", + "\n", + "search_space = {\n", + " \"lr0\": (1e-5, 1e-1),\n", + " \"momentum\":(0.6, 0.98),\n", + " \"weight_decay\":(0.0, 0.001),\n", + " \"hsv_h\": (0.0, 0.1),\n", + " \"hsv_s\": (0.0, 0.9),\n", + " \"hsv_v\": (0.0, 0.9),\n", + " \"scale\": (0.0, 0.9),\n", + " \"box\": (0.02, 0.2)\n", + "\n", + " }\n", + "\n", + "model.tune(\n", + " data= data_path + \"/data.yaml\",\n", + " epochs=20,\n", + " iterations=50,\n", + " optimizer=\"AdamW\",\n", + " project=result_path,\n", + " space=search_space,\n", + " plots=True,\n", + " save=True,\n", + " val=False,\n", + " device=\"cuda\"\n", + ")" + ], + "metadata": { + "id": "T474WqE8cX_G" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Training over Best Found Hyper-parameters\n", + "from ultralytics import YOLO\n", + "import torch\n", + "torch.cuda.empty_cache()\n", + "\n", + "model = YOLO(\"yolov8l-seg.pt\")\n", + "data_path = \"/content/drive/MyDrive/Repair Project/report/data/14c\"\n", + "result_path = \"/content/drive/MyDrive/Repair Project/report/results/training/14c\"\n", + "\n", + "model.train(\n", + " data= data_path + \"/data.yaml\",\n", + " project=result_path,\n", + " imgsz=800,\n", + " epochs=250,\n", + " batch=16,\n", + " name=\"train_250_800_16\",\n", + " device=\"cuda\",\n", + " augment=True,\n", + " cache=False,\n", + " save=True,\n", + " save_period=100,\n", + " lr0 = 0.01,\n", + " momentum= 0.937,\n", + " weight_decay= 0.0005,\n", + " hsv_h= 0.015,\n", + " hsv_s= 0.7,\n", + " hsv_v= 0.4,\n", + " scale= 0.5,\n", + " box= 7.5,\n", + " verbose=True,\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "skhX5jncdL62", + "outputId": "dc2bc732-4bbe-4fab-fbbb-f7ff54bf3a2d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Ultralytics 8.3.174 🚀 Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (NVIDIA L4, 22693MiB)\n", + "\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, augment=True, auto_augment=randaugment, batch=16, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/content/drive/MyDrive/Repair Project/report/data/14c/data.yaml, degrees=0.0, deterministic=True, device=0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=250, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=800, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.01, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8l-seg.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=train_250_800_16, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=/content/drive/MyDrive/Repair Project/report/results/training/14c, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=/content/drive/MyDrive/Repair Project/report/results/training/14c/train_250_800_16, save_frames=False, save_json=False, save_period=100, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=segment, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n", + "Overriding model.yaml nc=80 with nc=14\n", + "\n", + " from n params module arguments \n", + " 0 -1 1 1856 ultralytics.nn.modules.conv.Conv [3, 64, 3, 2] \n", + " 1 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n", + " 2 -1 3 279808 ultralytics.nn.modules.block.C2f [128, 128, 3, True] \n", + " 3 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n", + " 4 -1 6 2101248 ultralytics.nn.modules.block.C2f [256, 256, 6, True] \n", + " 5 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2] \n", + " 6 -1 6 8396800 ultralytics.nn.modules.block.C2f [512, 512, 6, True] \n", + " 7 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] \n", + " 8 -1 3 4461568 ultralytics.nn.modules.block.C2f [512, 512, 3, True] \n", + " 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5] \n", + " 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 12 -1 3 4723712 ultralytics.nn.modules.block.C2f [1024, 512, 3] \n", + " 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 15 -1 3 1247744 ultralytics.nn.modules.block.C2f [768, 256, 3] \n", + " 16 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n", + " 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 18 -1 3 4592640 ultralytics.nn.modules.block.C2f [768, 512, 3] \n", + " 19 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] \n", + " 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 21 -1 3 4723712 ultralytics.nn.modules.block.C2f [1024, 512, 3] \n", + " 22 [15, 18, 21] 1 7899802 ultralytics.nn.modules.head.Segment [14, 32, 256, [256, 512, 512]]\n", + "YOLOv8l-seg summary: 231 layers, 45,946,842 parameters, 45,946,826 gradients, 220.9 GFLOPs\n", + "\n", + "Transferred 651/657 items from pretrained weights\n", + "Freezing layer 'model.22.dfl.conv.weight'\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.3±0.1 ms, read: 170.3±190.3 MB/s, size: 319.8 KB)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/drive/MyDrive/Repair Project/report/data/14c/train/labels.cache... 324 images, 3 backgrounds, 0 corrupt: 100%|██████████| 324/324 [00:00\n", + "curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)', 'Precision-Recall(M)', 'F1-Confidence(M)', 'Precision-Confidence(M)', 'Recall-Confidence(M)']\n", + "curves_results: [[array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,\n", + " 0.024024, 0.025025, 0.026026, 0.027027, 0.028028, 0.029029, 0.03003, 0.031031, 0.032032, 0.033033, 0.034034, 0.035035, 0.036036, 0.037037, 0.038038, 0.039039, 0.04004, 0.041041, 0.042042, 0.043043, 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0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,\n", + " 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n", + " 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n", + " 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 1, 1, 1, ..., 0, 0, 0],\n", + " [ 1, 1, 1, ..., 0, 0, 0],\n", + " [ 0.5, 0.5, 0.5, ..., 0, 0, 0],\n", + " ...,\n", + " [ 1, 1, 1, ..., 0, 0, 0],\n", + " [ 1, 1, 1, ..., 0, 0, 0],\n", + " [ 0.72222, 0.72222, 0.72222, ..., 0, 0, 0]]), 'Confidence', 'Recall']]\n", + "fitness: np.float64(1.4177096550340642)\n", + "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)', 'metrics/precision(M)', 'metrics/recall(M)', 'metrics/mAP50(M)', 'metrics/mAP50-95(M)']\n", + "maps: array([ 1.387, 1.387, 1.8905, 1.387, 1.6527, 0.85029, 1.1817, 1.0447, 1.2523, 1.5299, 1.373, 1.795, 1.6164, 1.0704])\n", + "names: {0: 'background', 1: 'fragment_surface', 2: 'blue_bird', 3: 'yellow_bird', 4: 'red_griffon', 5: 'red_flower', 6: 'blue_flower', 7: 'red_circle', 8: 'red_spiral', 9: 'curved_green_stripe', 10: 'thin_red_stripe', 11: 'thick_red_stripe', 12: 'thin_floral_stripe', 13: 'thick_floral_stripe'}\n", + "nt_per_class: array([ 0, 0, 2, 0, 2, 2, 9, 5, 45, 14, 22, 4, 7, 18])\n", + "nt_per_image: array([ 0, 0, 2, 0, 1, 1, 6, 3, 8, 14, 17, 4, 4, 5])\n", + "results_dict: {'metrics/precision(B)': np.float64(0.8681240793422045), 'metrics/recall(B)': np.float64(0.7989992946832962), 'metrics/mAP50(B)': np.float64(0.8415805630276157), 'metrics/mAP50-95(B)': np.float64(0.7201500531686704), 'metrics/precision(M)': np.float64(0.8755151396128515), 'metrics/recall(M)': np.float64(0.8046980249208364), 'metrics/mAP50(M)': np.float64(0.8527533296773526), 'metrics/mAP50-95(M)': np.float64(0.6668235754575155), 'fitness': np.float64(1.4177096550340642)}\n", + "save_dir: PosixPath('/content/drive/MyDrive/Repair Project/report/results/training/14c/train_250_800_16')\n", + "seg: ultralytics.utils.metrics.Metric object\n", + "speed: {'preprocess': 0.25680455000838265, 'inference': 17.657204575016294, 'loss': 0.0004052499889439787, 'postprocess': 1.5282967000075587}\n", + "stats: {'tp': [], 'conf': [], 'pred_cls': [], 'target_cls': [], 'target_img': [], 'tp_m': []}\n", + "task: 'segment'" + ] + }, + "metadata": {}, + "execution_count": 1 + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "qJ2QEpNAJXgD" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def evaluate_yolo_box_seg(model_path, data_yaml):\n", + "\n", + " model = YOLO(model_path)\n", + " results = model.val(data=data_yaml, split='test', verbose=False)\n", + "\n", + " metrics = {}\n", + "\n", + " # Bounding box metrics\n", + " if results.box is not None:\n", + " metrics[\"box_precision\"] = results.box.p.mean().item()\n", + " metrics[\"box_recall\"] = results.box.r.mean().item()\n", + " metrics[\"box_map50\"] = results.box.map50.item()\n", + "\n", + " # Segmentation metrics\n", + " if results.seg is not None:\n", + " metrics[\"seg_precision\"] = results.seg.p.mean().item()\n", + " metrics[\"seg_recall\"] = results.seg.r.mean().item()\n", + " metrics[\"seg_map50\"] = results.seg.map50.item()\n", + "\n", + " return metrics" + ], + "metadata": { + "id": "1M_xzeBqdxj1" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "evaluate_yolo_box_seg(\n", + "\"/content/drive/MyDrive/Repair Project/report/results/training/14c/train_250_800_16/weights/best.pt\",\n", + "data_path+\"/data.yaml\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UmEXegghccsi", + "outputId": "743e7bfc-2b78-4f64-f444-9d32f569f9f3" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Ultralytics 8.3.174 🚀 Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (NVIDIA L4, 22693MiB)\n", + "YOLOv8l-seg summary (fused): 125 layers, 45,922,682 parameters, 0 gradients, 220.2 GFLOPs\n", + "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.4±0.1 ms, read: 0.2±0.1 MB/s, size: 227.1 KB)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/Repair Project/report/data/14c/test/labels... 39 images, 1 backgrounds, 0 corrupt: 100%|██████████| 39/39 [00:11<00:00, 3.48it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/drive/MyDrive/Repair Project/report/data/14c/test/labels.cache\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:02<00:00, 1.11it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + " all 39 84 0.787 0.866 0.844 0.699 0.896 0.811 0.903 0.711\n", + "Speed: 6.4ms preprocess, 30.8ms inference, 0.0ms loss, 2.4ms postprocess per image\n", + "Results saved to \u001b[1mruns/segment/val2\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'box_precision': 0.7865616846032591,\n", + " 'box_recall': 0.8658896650404496,\n", + " 'box_map50': 0.8438613385936378,\n", + " 'seg_precision': 0.8961427609220894,\n", + " 'seg_recall': 0.811297847642913,\n", + " 'seg_map50': 0.9025302776148787}" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import cv2\n", + "\n", + "def get_yolo_masks(weights_path, image_path, conf_threshold=0.5):\n", + " model = YOLO(weights_path)\n", + " results = model(image_path, conf=conf_threshold, verbose=False)\n", + " masks = []\n", + " for result in results:\n", + " if result.masks is None:\n", + " continue\n", + " for m in result.masks.data.cpu().numpy():\n", + " binary_mask = (m > 0.5).astype(np.uint8) * 255\n", + " masks.append(binary_mask)\n", + " return masks\n", + "\n", + "def plot_masks(masks):\n", + "\n", + " if not masks:\n", + " print(\"No masks found.\")\n", + " return\n", + "\n", + " height, width = masks[0].shape\n", + " combined_mask = np.zeros((height, width), dtype=np.uint8)\n", + "\n", + " for mask in masks:\n", + " combined_mask = np.logical_or(combined_mask, mask > 0).astype(np.uint8)\n", + "\n", + " mask_rgb = np.zeros((height, width, 3), dtype=np.uint8)\n", + "\n", + " purple = [100, 0, 100]\n", + " yellow = [200, 200, 0]\n", + "\n", + " mask_rgb[:, :] = purple\n", + " mask_rgb[combined_mask == 1] = yellow\n", + "\n", + " plt.figure(figsize=(10, 10))\n", + " plt.imshow(mask_rgb)\n", + " plt.axis('off')\n", + " plt.show()" + ], + "metadata": { + "id": "lTk_D00UJZNT" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "masks = get_yolo_masks(\n", + " weights_path=\"/content/drive/MyDrive/Repair Project/report/results/training/14c/train_250_800_16/weights/best.pt\",\n", + " image_path=\"/content/drive/MyDrive/Repair Project/report/data/14c/test/images/RPf_00481.png\"\n", + " )\n", + "\n", + "plot_masks(masks)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 807 + }, + "id": "NV6M8tO7JldU", + "outputId": "7f1eb355-8be8-467e-b15b-811a1986c998" + }, + "execution_count": 42, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Training over Best Found Hyper-parameters\n", + "from ultralytics import YOLO\n", + "import torch\n", + "torch.cuda.empty_cache()\n", + "\n", + "model = YOLO(\"yolov8l-seg.pt\")\n", + "data_path = \"/content/drive/MyDrive/Repair Project/report/data/7c\"\n", + "result_path = \"/content/drive/MyDrive/Repair Project/report/results/training/7c\"\n", + "\n", + "model.train(\n", + " data= data_path + \"/data.yaml\",\n", + " project=result_path,\n", + " imgsz=800,\n", + " epochs=250,\n", + " batch=16,\n", + " name=\"train_250_800_16\",\n", + " device=\"cuda\",\n", + " augment=True,\n", + " cache=False,\n", + " save=True,\n", + " save_period=100,\n", + " lr0 = 0.01,\n", + " momentum= 0.937,\n", + " weight_decay= 0.0005,\n", + " hsv_h= 0.015,\n", + " hsv_s= 0.7,\n", + " hsv_v= 0.4,\n", + " scale= 0.5,\n", + " box= 7.5,\n", + " verbose=True,\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iTDovu8Fc_Hw", + "outputId": "f98ce4d1-e850-408c-a15a-36ab3c89fe56" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Ultralytics 8.3.174 🚀 Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (NVIDIA L4, 22693MiB)\n", + "\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, augment=True, auto_augment=randaugment, batch=16, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/content/drive/MyDrive/Repair Project/report/data/7c/data.yaml, degrees=0.0, deterministic=True, device=0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=250, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=800, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.01, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8l-seg.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=train_250_800_162, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=/content/drive/MyDrive/Repair Project/report/results/training/7c, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=/content/drive/MyDrive/Repair Project/report/results/training/7c/train_250_800_162, save_frames=False, save_json=False, save_period=100, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=segment, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n", + "Overriding model.yaml nc=80 with nc=7\n", + "\n", + " from n params module arguments \n", + " 0 -1 1 1856 ultralytics.nn.modules.conv.Conv [3, 64, 3, 2] \n", + " 1 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n", + " 2 -1 3 279808 ultralytics.nn.modules.block.C2f [128, 128, 3, True] \n", + " 3 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n", + " 4 -1 6 2101248 ultralytics.nn.modules.block.C2f [256, 256, 6, True] \n", + " 5 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2] \n", + " 6 -1 6 8396800 ultralytics.nn.modules.block.C2f [512, 512, 6, True] \n", + " 7 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] \n", + " 8 -1 3 4461568 ultralytics.nn.modules.block.C2f [512, 512, 3, True] \n", + " 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5] \n", + " 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 12 -1 3 4723712 ultralytics.nn.modules.block.C2f [1024, 512, 3] \n", + " 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 15 -1 3 1247744 ultralytics.nn.modules.block.C2f [768, 256, 3] \n", + " 16 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n", + " 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 18 -1 3 4592640 ultralytics.nn.modules.block.C2f [768, 512, 3] \n", + " 19 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] \n", + " 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 21 -1 3 4723712 ultralytics.nn.modules.block.C2f [1024, 512, 3] \n", + " 22 [15, 18, 21] 1 7894405 ultralytics.nn.modules.head.Segment [7, 32, 256, [256, 512, 512]] \n", + "YOLOv8l-seg summary: 231 layers, 45,941,445 parameters, 45,941,429 gradients, 220.8 GFLOPs\n", + "\n", + "Transferred 651/657 items from pretrained weights\n", + "Freezing layer 'model.22.dfl.conv.weight'\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.8±0.8 ms, read: 0.3±0.2 MB/s, size: 319.8 KB)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/drive/MyDrive/Repair Project/report/data/7c/train/labels... 324 images, 65 backgrounds, 0 corrupt: 100%|██████████| 324/324 [00:48<00:00, 6.62it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/drive/MyDrive/Repair Project/report/data/7c/train/labels.cache\n", + "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n", + "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.8±0.3 ms, read: 0.1±0.1 MB/s, size: 109.8 KB)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/Repair Project/report/data/7c/val/labels... 40 images, 7 backgrounds, 0 corrupt: 100%|██████████| 40/40 [00:08<00:00, 4.60it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/drive/MyDrive/Repair Project/report/data/7c/val/labels.cache\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Plotting labels to /content/drive/MyDrive/Repair Project/report/results/training/7c/train_250_800_162/labels.jpg... \n", + "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n", + "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.000909, momentum=0.9) with parameter groups 106 weight(decay=0.0), 117 weight(decay=0.0005), 116 bias(decay=0.0)\n", + "Image sizes 800 train, 800 val\n", + "Using 8 dataloader workers\n", + "Logging results to \u001b[1m/content/drive/MyDrive/Repair Project/report/results/training/7c/train_250_800_162\u001b[0m\n", + "Starting training for 250 epochs...\n", + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + " 1/250 15.6G 1.113 2.045 3.547 1.371 10 800: 100%|██████████| 21/21 [00:19<00:00, 1.06it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 2/2 [00:01<00:00, 1.89it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ 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0.93493, 0.93594,\n", + " 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n", + " 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n", + " 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 1, 1, 1, ..., 0, 0, 0],\n", + " [ 0.88889, 0.88889, 0.82155, ..., 0, 0, 0],\n", + " [ 0.88889, 0.88889, 0.88889, ..., 0, 0, 0],\n", + " [ 1, 1, 1, ..., 0, 0, 0],\n", + " [ 0.95455, 0.95455, 0.95455, ..., 0, 0, 0],\n", + " [ 1, 1, 1, ..., 0, 0, 0]]), 'Confidence', 'Recall']]\n", + "fitness: np.float64(1.5019670143351331)\n", + "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)', 'metrics/precision(M)', 'metrics/recall(M)', 'metrics/mAP50(M)', 'metrics/mAP50-95(M)']\n", + "maps: array([ 1.8421, 1.4669, 1.0974, 1.1926, 1.6045, 1.4103, 1.6544])\n", + "names: {0: 'blue_bird', 1: 'yellow_bird', 2: 'blue_flower', 3: 'red_spiral', 4: 'curved_green_stripe', 5: 'thin_red_stripe', 6: 'thick_red_stripe'}\n", + "nt_per_class: array([ 2, 0, 9, 45, 14, 22, 4])\n", + "nt_per_image: array([ 2, 0, 6, 8, 14, 17, 4])\n", + "results_dict: {'metrics/precision(B)': np.float64(0.9026551037449391), 'metrics/recall(B)': np.float64(0.8255537573125343), 'metrics/mAP50(B)': np.float64(0.9052384575341944), 'metrics/mAP50-95(B)': np.float64(0.7509562105939432), 'metrics/precision(M)': np.float64(0.9111520300493701), 'metrics/recall(M)': np.float64(0.8328031546853883), 'metrics/mAP50(M)': np.float64(0.912544969638021), 'metrics/mAP50-95(M)': np.float64(0.715920091203736), 'fitness': np.float64(1.5019670143351331)}\n", + "save_dir: PosixPath('/content/drive/MyDrive/Repair Project/report/results/training/7c/train_250_800_162')\n", + "seg: ultralytics.utils.metrics.Metric object\n", + "speed: {'preprocess': 0.2639171999817336, 'inference': 17.619013800003813, 'loss': 0.0004487249952944694, 'postprocess': 1.4502619500035507}\n", + "stats: {'tp': [], 'conf': [], 'pred_cls': [], 'target_cls': [], 'target_img': [], 'tp_m': []}\n", + "task: 'segment'" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "code", + "source": [ + "evaluate_yolo_box_seg(\n", + "\"/content/drive/MyDrive/Repair Project/report/results/training/7c/train_250_800_16/weights/best.pt\",\n", + "data_path+\"/data.yaml\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iir9eVWsdyX_", + "outputId": "41ae1ac9-a01f-4846-c9f9-74cb842eb620" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Ultralytics 8.3.174 🚀 Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (NVIDIA L4, 22693MiB)\n", + "YOLOv8l-seg summary (fused): 125 layers, 45,917,285 parameters, 0 gradients, 220.2 GFLOPs\n", + "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.4±0.1 ms, read: 0.3±0.3 MB/s, size: 351.0 KB)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/Repair Project/report/data/7c/test/labels... 39 images, 10 backgrounds, 0 corrupt: 100%|██████████| 39/39 [00:09<00:00, 4.26it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/drive/MyDrive/Repair Project/report/data/7c/test/labels.cache\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:02<00:00, 1.30it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + " all 39 62 0.824 0.858 0.92 0.795 0.824 0.858 0.92 0.792\n", + "Speed: 5.3ms preprocess, 27.2ms inference, 0.0ms loss, 2.2ms postprocess per image\n", + "Results saved to \u001b[1mruns/segment/val3\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'box_precision': 0.8241512399625393,\n", + " 'box_recall': 0.857753181853182,\n", + " 'box_map50': 0.9203596167916207,\n", + " 'seg_precision': 0.8241512399625393,\n", + " 'seg_recall': 0.857753181853182,\n", + " 'seg_map50': 0.9203596167916207}" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from ultralytics import YOLO\n", + "import matplotlib.pyplot as plt\n", + "\n", + "masks = get_yolo_masks(\n", + " weights_path=\"/content/drive/MyDrive/Repair Project/report/results/training/7c/train_250_800_16/weights/best.pt\",\n", + " image_path=\"/content/drive/MyDrive/Repair Project/report/data/7c/test/images/RPf_00481.png\"\n", + " )\n", + "\n", + "plot_masks(masks)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 807 + }, + "id": "72NIq34lgOJb", + "outputId": "c0daa091-6e40-4b90-e0c8-c7ac65047f9f" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Training over Best Found Hyper-parameters\n", + "from ultralytics import YOLO\n", + "import torch\n", + "torch.cuda.empty_cache()\n", + "\n", + "model = YOLO(\"yolov8l-seg.pt\")\n", + "data_path = \"/content/drive/MyDrive/Repair Project/report/data/3c\"\n", + "result_path = \"/content/drive/MyDrive/Repair Project/report/results/training/3c\"\n", + "\n", + "model.train(\n", + " data= data_path + \"/data.yaml\",\n", + " project=result_path,\n", + " imgsz=800,\n", + " epochs=250,\n", + " batch=16,\n", + " name=\"train_250_800_16\",\n", + " device=\"cuda\",\n", + " augment=True,\n", + " cache=False,\n", + " save=True,\n", + " save_period=100,\n", + " lr0 = 0.01,\n", + " momentum= 0.937,\n", + " weight_decay= 0.0005,\n", + " hsv_h= 0.015,\n", + " hsv_s= 0.7,\n", + " hsv_v= 0.4,\n", + " scale= 0.5,\n", + " box= 7.5,\n", + " verbose=True,\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OtUF2Nze0A8v", + "outputId": "53276004-3ed9-4f6d-f191-cb323286d58a" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Ultralytics 8.3.174 🚀 Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (NVIDIA L4, 22693MiB)\n", + "\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, augment=True, auto_augment=randaugment, batch=16, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/content/drive/MyDrive/Repair Project/report/data/3c/data.yaml, degrees=0.0, deterministic=True, device=0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=250, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=800, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.01, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8l-seg.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=train_250_800_16, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=/content/drive/MyDrive/Repair Project/report/results/training/3c, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=/content/drive/MyDrive/Repair Project/report/results/training/3c/train_250_800_16, save_frames=False, save_json=False, save_period=100, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=segment, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n", + "Overriding model.yaml nc=80 with nc=3\n", + "\n", + " from n params module arguments \n", + " 0 -1 1 1856 ultralytics.nn.modules.conv.Conv [3, 64, 3, 2] \n", + " 1 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n", + " 2 -1 3 279808 ultralytics.nn.modules.block.C2f [128, 128, 3, True] \n", + " 3 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n", + " 4 -1 6 2101248 ultralytics.nn.modules.block.C2f [256, 256, 6, True] \n", + " 5 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2] \n", + " 6 -1 6 8396800 ultralytics.nn.modules.block.C2f [512, 512, 6, True] \n", + " 7 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] \n", + " 8 -1 3 4461568 ultralytics.nn.modules.block.C2f [512, 512, 3, True] \n", + " 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5] \n", + " 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 12 -1 3 4723712 ultralytics.nn.modules.block.C2f [1024, 512, 3] \n", + " 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 15 -1 3 1247744 ultralytics.nn.modules.block.C2f [768, 256, 3] \n", + " 16 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n", + " 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 18 -1 3 4592640 ultralytics.nn.modules.block.C2f [768, 512, 3] \n", + " 19 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] \n", + " 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 21 -1 3 4723712 ultralytics.nn.modules.block.C2f [1024, 512, 3] \n", + " 22 [15, 18, 21] 1 7891321 ultralytics.nn.modules.head.Segment [3, 32, 256, [256, 512, 512]] \n", + "YOLOv8l-seg summary: 231 layers, 45,938,361 parameters, 45,938,345 gradients, 220.8 GFLOPs\n", + "\n", + "Transferred 651/657 items from pretrained weights\n", + "Freezing layer 'model.22.dfl.conv.weight'\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.6±0.4 ms, read: 0.2±0.3 MB/s, size: 319.8 KB)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/drive/MyDrive/Repair Project/report/data/3c/train/labels... 324 images, 116 backgrounds, 0 corrupt: 100%|██████████| 324/324 [00:37<00:00, 8.59it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/drive/MyDrive/Repair Project/report/data/3c/train/labels.cache\n", + "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.8±0.1 ms, read: 0.1±0.1 MB/s, size: 109.8 KB)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/Repair Project/report/data/3c/val/labels... 40 images, 12 backgrounds, 0 corrupt: 100%|██████████| 40/40 [00:09<00:00, 4.02it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/drive/MyDrive/Repair Project/report/data/3c/val/labels.cache\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Plotting labels to /content/drive/MyDrive/Repair Project/report/results/training/3c/train_250_800_16/labels.jpg... \n", + "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n", + "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001429, momentum=0.9) with parameter groups 106 weight(decay=0.0), 117 weight(decay=0.0005), 116 bias(decay=0.0)\n", + "Image sizes 800 train, 800 val\n", + "Using 8 dataloader workers\n", + "Logging results to \u001b[1m/content/drive/MyDrive/Repair Project/report/results/training/3c/train_250_800_16\u001b[0m\n", + "Starting training for 250 epochs...\n", + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + " 1/250 17.3G 1.174 1.982 3.774 1.355 6 800: 100%|██████████| 21/21 [00:19<00:00, 1.06it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) 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0.7988, 0.7998, 0.8008, 0.8018, 0.8028, 0.8038, 0.8048, 0.80581, 0.80681, 0.80781, 0.80881, 0.80981, 0.81081, 0.81181, 0.81281, 0.81381, 0.81481, 0.81582,\n", + " 0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,\n", + " 0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,\n", + " 0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,\n", + " 0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,\n", + " 0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,\n", + " 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n", + " 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n", + " 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.95556, 0.95556, 0.95556, ..., 0, 0, 0],\n", + " [ 1, 1, 1, ..., 0, 0, 0],\n", + " [ 0.95455, 0.95455, 0.95455, ..., 0, 0, 0]]), 'Confidence', 'Recall']]\n", + "fitness: np.float64(1.402305849447365)\n", + "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)', 'metrics/precision(M)', 'metrics/recall(M)', 'metrics/mAP50(M)', 'metrics/mAP50-95(M)']\n", + "maps: array([ 1.2353, 1.6092, 1.2376])\n", + "names: {0: 'red_spiral', 1: 'curved_green_stripe', 2: 'thin_red_stripe'}\n", + "nt_per_class: array([45, 14, 22])\n", + "nt_per_image: array([ 8, 14, 17])\n", + "results_dict: {'metrics/precision(B)': np.float64(0.826940719694793), 'metrics/recall(B)': np.float64(0.8473028606709162), 'metrics/mAP50(B)': np.float64(0.8713851175641324), 'metrics/mAP50-95(B)': np.float64(0.680615555801979), 'metrics/precision(M)': np.float64(0.8555622972700969), 'metrics/recall(M)': np.float64(0.8774787797357243), 'metrics/mAP50(M)': np.float64(0.9053461901368953), 'metrics/mAP50-95(M)': np.float64(0.6800874649505345), 'fitness': np.float64(1.402305849447365)}\n", + "save_dir: PosixPath('/content/drive/MyDrive/Repair Project/report/results/training/3c/train_250_800_16')\n", + "seg: ultralytics.utils.metrics.Metric object\n", + "speed: {'preprocess': 0.26829759999600356, 'inference': 17.56487192496934, 'loss': 0.00040727500163484365, 'postprocess': 1.4331795749058074}\n", + "stats: {'tp': [], 'conf': [], 'pred_cls': [], 'target_cls': [], 'target_img': [], 'tp_m': []}\n", + "task: 'segment'" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "source": [ + "evaluate_yolo_box_seg(\n", + "\"/content/drive/MyDrive/Repair Project/report/results/training/3c/train_250_800_16/weights/best.pt\",\n", + "data_path+\"/data.yaml\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "D7ZvrLSP0FTf", + "outputId": "66a3d9ca-e4a5-4c3e-b35f-5a2e96f35761" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Ultralytics 8.3.174 🚀 Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (NVIDIA L4, 22693MiB)\n", + "YOLOv8l-seg summary (fused): 125 layers, 45,914,201 parameters, 0 gradients, 220.1 GFLOPs\n", + "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.4±0.1 ms, read: 0.3±0.3 MB/s, size: 351.0 KB)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/Repair Project/report/data/3c/test/labels... 39 images, 18 backgrounds, 0 corrupt: 100%|██████████| 39/39 [00:09<00:00, 3.92it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/drive/MyDrive/Repair Project/report/data/3c/test/labels.cache\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:02<00:00, 1.20it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + " all 39 48 0.811 0.895 0.898 0.716 0.811 0.895 0.898 0.692\n", + "Speed: 5.7ms preprocess, 27.4ms inference, 0.0ms loss, 3.4ms postprocess per image\n", + "Results saved to \u001b[1mruns/segment/val4\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'box_precision': 0.8112426209553462,\n", + " 'box_recall': 0.8952811615422949,\n", + " 'box_map50': 0.8977972515998317,\n", + " 'seg_precision': 0.8112426209553462,\n", + " 'seg_recall': 0.8952811615422949,\n", + " 'seg_map50': 0.8977972515998317}" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from ultralytics import YOLO\n", + "import matplotlib.pyplot as plt\n", + "\n", + "masks = get_yolo_masks(\n", + " weights_path=\"/content/drive/MyDrive/Repair Project/report/results/training/3c/train_250_800_16/weights/best.pt\",\n", + " image_path=\"/content/drive/MyDrive/Repair Project/report/data/3c/test/images/RPf_00481.png\"\n", + " )\n", + "\n", + "plot_masks(masks)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 807 + }, + "id": "tFYPGJggxC9j", + "outputId": "d24649a1-d801-4c59-9073-38790c2ee158" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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