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| # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license | |
| # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford | |
| # Example usage: python train.py --data VOC.yaml | |
| # parent | |
| # ├── yolov5 | |
| # └── datasets | |
| # └── VOC ← downloads here (2.8 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/VOC | |
| train: # train images (relative to 'path') 16551 images | |
| - images/train2012 | |
| - images/train2007 | |
| - images/val2012 | |
| - images/val2007 | |
| val: # val images (relative to 'path') 4952 images | |
| - images/test2007 | |
| test: # test images (optional) | |
| - images/test2007 | |
| # Classes | |
| names: | |
| 0: aeroplane | |
| 1: bicycle | |
| 2: bird | |
| 3: boat | |
| 4: bottle | |
| 5: bus | |
| 6: car | |
| 7: cat | |
| 8: chair | |
| 9: cow | |
| 10: diningtable | |
| 11: dog | |
| 12: horse | |
| 13: motorbike | |
| 14: person | |
| 15: pottedplant | |
| 16: sheep | |
| 17: sofa | |
| 18: train | |
| 19: tvmonitor | |
| # Download script/URL (optional) --------------------------------------------------------------------------------------- | |
| download: | | |
| import xml.etree.ElementTree as ET | |
| from tqdm import tqdm | |
| from utils.general import download, Path | |
| def convert_label(path, lb_path, year, image_id): | |
| def convert_box(size, box): | |
| dw, dh = 1. / size[0], 1. / size[1] | |
| x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2] | |
| return x * dw, y * dh, w * dw, h * dh | |
| in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml') | |
| out_file = open(lb_path, 'w') | |
| tree = ET.parse(in_file) | |
| root = tree.getroot() | |
| size = root.find('size') | |
| w = int(size.find('width').text) | |
| h = int(size.find('height').text) | |
| names = list(yaml['names'].values()) # names list | |
| for obj in root.iter('object'): | |
| cls = obj.find('name').text | |
| if cls in names and int(obj.find('difficult').text) != 1: | |
| xmlbox = obj.find('bndbox') | |
| bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')]) | |
| cls_id = names.index(cls) # class id | |
| out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n') | |
| # Download | |
| dir = Path(yaml['path']) # dataset root dir | |
| url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' | |
| urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images | |
| f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images | |
| f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images | |
| download(urls, dir=dir / 'images', delete=False, curl=True, threads=3) | |
| # Convert | |
| path = dir / 'images/VOCdevkit' | |
| for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'): | |
| imgs_path = dir / 'images' / f'{image_set}{year}' | |
| lbs_path = dir / 'labels' / f'{image_set}{year}' | |
| imgs_path.mkdir(exist_ok=True, parents=True) | |
| lbs_path.mkdir(exist_ok=True, parents=True) | |
| with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f: | |
| image_ids = f.read().strip().split() | |
| for id in tqdm(image_ids, desc=f'{image_set}{year}'): | |
| f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path | |
| lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path | |
| f.rename(imgs_path / f.name) # move image | |
| convert_label(path, lb_path, year, id) # convert labels to YOLO format | |