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- # YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
- # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC
- # Example usage: python train.py --data VOC.yaml
- # parent
- # ├── yolov5
- # └── datasets
- # └── VOC ← downloads here
-
-
- # 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
- nc: 20 # number of classes
- names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
- 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] # class names
-
-
- # 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)
-
- for obj in root.iter('object'):
- cls = obj.find('name').text
- if cls in yaml['names'] and not 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 = yaml['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 = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
- url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
- url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
- download(urls, dir=dir / 'images', delete=False)
-
- # Convert
- path = dir / f'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)
-
- image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').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
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