- # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
- # VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
- # Example usage: python train.py --data VisDrone.yaml
- # parent
- # ├── yolov5
- # └── 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: ../datasets/VisDrone # dataset root dir
- train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
- val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
- test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
-
- # Classes
- nc: 10 # number of classes
- names: ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor']
-
-
- # Download script/URL (optional) ---------------------------------------------------------------------------------------
- download: |
- from utils.general import download, os, Path
-
- def visdrone2yolo(dir):
- from PIL import Image
- from tqdm import tqdm
-
- def convert_box(size, box):
- # Convert VisDrone box to YOLO xywh box
- dw = 1. / size[0]
- dh = 1. / size[1]
- return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
-
- (dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory
- pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
- for f in pbar:
- img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
- lines = []
- with open(f, 'r') as file: # read annotation.txt
- for row in [x.split(',') for x in file.read().strip().splitlines()]:
- if row[4] == '0': # VisDrone 'ignored regions' class 0
- continue
- cls = int(row[5]) - 1
- box = convert_box(img_size, tuple(map(int, row[:4])))
- lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
- with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
- fl.writelines(lines) # write label.txt
-
-
- # Download
- dir = Path(yaml['path']) # dataset root dir
- urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
- 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
- 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
- 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
- download(urls, dir=dir, curl=True, threads=4)
-
- # Convert
- for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
- visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels
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