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- # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
- # Train command: python train.py --data Argoverse_HD.yaml
- # Default dataset location is next to YOLOv5:
- # /parent
- # /datasets/Argoverse
- # /yolov5
-
-
- # 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: ../datasets/Argoverse # dataset root dir
- train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
- val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
- test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
-
- # Classes
- nc: 8 # number of classes
- names: [ 'person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign' ] # class names
-
-
- # Download script/URL (optional) ---------------------------------------------------------------------------------------
- download: |
- import json
-
- from tqdm import tqdm
- from utils.general import download, Path
-
-
- def argoverse2yolo(set):
- labels = {}
- a = json.load(open(set, "rb"))
- for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
- img_id = annot['image_id']
- img_name = a['images'][img_id]['name']
- img_label_name = img_name[:-3] + "txt"
-
- cls = annot['category_id'] # instance class id
- x_center, y_center, width, height = annot['bbox']
- x_center = (x_center + width / 2) / 1920.0 # offset and scale
- y_center = (y_center + height / 2) / 1200.0 # offset and scale
- width /= 1920.0 # scale
- height /= 1200.0 # scale
-
- img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
- if not img_dir.exists():
- img_dir.mkdir(parents=True, exist_ok=True)
-
- k = str(img_dir / img_label_name)
- if k not in labels:
- labels[k] = []
- labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
-
- for k in labels:
- with open(k, "w") as f:
- f.writelines(labels[k])
-
-
- # Download
- dir = Path('../datasets/Argoverse') # dataset root dir
- urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
- download(urls, dir=dir, delete=False)
-
- # Convert
- annotations_dir = 'Argoverse-HD/annotations/'
- (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images'
- for d in "train.json", "val.json":
- argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels
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