78 lines
3.2 KiB
YAML
78 lines
3.2 KiB
YAML
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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# Argoverse-HD dataset (ring-front-center camera) https://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
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# Documentation: https://docs.ultralytics.com/datasets/detect/argoverse/
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# Example usage: yolo train data=Argoverse.yaml
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# parent
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# ├── ultralytics
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# └── datasets
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# └── Argoverse ← downloads here (31.5 GB)
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# 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, ..]
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path: Argoverse # dataset root dir
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train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
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val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
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test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
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# Classes
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names:
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0: person
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1: bicycle
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2: car
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3: motorcycle
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4: bus
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5: truck
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6: traffic_light
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7: stop_sign
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# Download script/URL (optional) ---------------------------------------------------------------------------------------
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download: |
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import json
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from pathlib import Path
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from tqdm import tqdm
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from ultralytics.utils.downloads import download
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def argoverse2yolo(set):
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"""Convert Argoverse dataset annotations to YOLO format for object detection tasks."""
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labels = {}
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a = json.load(open(set, "rb"))
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for annot in tqdm(a["annotations"], desc=f"Converting {set} to YOLOv5 format..."):
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img_id = annot["image_id"]
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img_name = a["images"][img_id]["name"]
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img_label_name = f"{img_name[:-3]}txt"
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cls = annot["category_id"] # instance class id
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x_center, y_center, width, height = annot["bbox"]
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x_center = (x_center + width / 2) / 1920.0 # offset and scale
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y_center = (y_center + height / 2) / 1200.0 # offset and scale
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width /= 1920.0 # scale
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height /= 1200.0 # scale
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img_dir = set.parents[2] / "Argoverse-1.1" / "labels" / a["seq_dirs"][a["images"][annot["image_id"]]["sid"]]
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if not img_dir.exists():
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img_dir.mkdir(parents=True, exist_ok=True)
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k = str(img_dir / img_label_name)
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if k not in labels:
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labels[k] = []
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labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
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for k in labels:
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with open(k, "w", encoding="utf-8") as f:
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f.writelines(labels[k])
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# Download 'https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip' (deprecated S3 link)
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dir = Path(yaml["path"]) # dataset root dir
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urls = ["https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link"]
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print("\n\nWARNING: Argoverse dataset MUST be downloaded manually, autodownload will NOT work.")
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print(f"WARNING: Manually download Argoverse dataset '{urls[0]}' to '{dir}' and re-run your command.\n\n")
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# download(urls, dir=dir)
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# Convert
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annotations_dir = "Argoverse-HD/annotations/"
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(dir / "Argoverse-1.1" / "tracking").rename(dir / "Argoverse-1.1" / "images") # rename 'tracking' to 'images'
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for d in "train.json", "val.json":
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argoverse2yolo(dir / annotations_dir / d) # convert Argoverse annotations to YOLO labels
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