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ultralytics/cfg/datasets/VisDrone.yaml

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
+# Documentation: https://docs.ultralytics.com/datasets/detect/visdrone/
+# Example usage: yolo train data=VisDrone.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── VisDrone ← downloads here (2.3 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: VisDrone # dataset root dir
+train: images/train # train images (relative to 'path') 6471 images
+val: images/val # val images (relative to 'path') 548 images
+test: images/test # test-dev images (optional) 1610 images
+
+# Classes
+names:
+  0: pedestrian
+  1: people
+  2: bicycle
+  3: car
+  4: van
+  5: truck
+  6: tricycle
+  7: awning-tricycle
+  8: bus
+  9: motor
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import os
+  from pathlib import Path
+  import shutil
+
+  from ultralytics.utils.downloads import download
+  from ultralytics.utils import TQDM
+
+
+  def visdrone2yolo(dir, split, source_name=None):
+      """Convert VisDrone annotations to YOLO format with images/{split} and labels/{split} structure."""
+      from PIL import Image
+
+      source_dir = dir / (source_name or f"VisDrone2019-DET-{split}")
+      images_dir = dir / "images" / split
+      labels_dir = dir / "labels" / split
+      labels_dir.mkdir(parents=True, exist_ok=True)
+
+      # Move images to new structure
+      if (source_images_dir := source_dir / "images").exists():
+          images_dir.mkdir(parents=True, exist_ok=True)
+          for img in source_images_dir.glob("*.jpg"):
+              img.rename(images_dir / img.name)
+
+      for f in TQDM((source_dir / "annotations").glob("*.txt"), desc=f"Converting {split}"):
+          img_size = Image.open(images_dir / f.with_suffix(".jpg").name).size
+          dw, dh = 1.0 / img_size[0], 1.0 / img_size[1]
+          lines = []
+
+          with open(f, encoding="utf-8") as file:
+              for row in [x.split(",") for x in file.read().strip().splitlines()]:
+                  if row[4] != "0":  # Skip ignored regions
+                      x, y, w, h = map(int, row[:4])
+                      cls = int(row[5]) - 1
+                      # Convert to YOLO format
+                      x_center, y_center = (x + w / 2) * dw, (y + h / 2) * dh
+                      w_norm, h_norm = w * dw, h * dh
+                      lines.append(f"{cls} {x_center:.6f} {y_center:.6f} {w_norm:.6f} {h_norm:.6f}\n")
+
+          (labels_dir / f.name).write_text("".join(lines), encoding="utf-8")
+
+
+  # Download (ignores test-challenge split)
+  dir = Path(yaml["path"])  # dataset root dir
+  urls = [
+      "https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-train.zip",
+      "https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-val.zip",
+      "https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-dev.zip",
+      # "https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-challenge.zip",
+  ]
+  download(urls, dir=dir, threads=4)
+
+  # Convert
+  splits = {"VisDrone2019-DET-train": "train", "VisDrone2019-DET-val": "val", "VisDrone2019-DET-test-dev": "test"}
+  for folder, split in splits.items():
+      visdrone2yolo(dir, split, folder)  # convert VisDrone annotations to YOLO labels
+      shutil.rmtree(dir / folder)  # cleanup original directory