慢慢记录吧
yolov7的数据集格式和yolov5是一样的，基本上直接将yolov5的数据集拿过来用即可
文件层级：
├—data │ ├—train │ │ ├—images │ │ │ ├—000000000001.jpg │ │ │ ├—000000000002.jpg │ │ ├—labels │ │ │ ├—000000000001.txt │ │ │ ├—000000000002.txt │ ├—valid │ │ ├—images │ │ ├—labels
区别就在yaml文件上，
yolov5的文件格式：
yolov7的文件格式：
区别就是没了path，主要是有些数据比较大，不想移来移去，所以直接修改v7的代码
主要修改的是yolov7\utils\general.py
将以下代码加入check_dataset函数即可：
FILE = Path(__file__).resolve()
ROOT = FILE.parents[1]
path = Path(dict.get('path') or '')
if not path.is_absolute():
path = (ROOT / path).resolve()
for k in 'train', 'val', 'test':
if dict.get(k): # prepend path
dict[k] = str(path / dict[k]) if isinstance(dict[k], str) else [str(path / x) for x in dict[k]]def check_dataset(dict):
FILE = Path(__file__).resolve()
ROOT = FILE.parents[1]
path = Path(dict.get('path') or '')
if not path.is_absolute():
path = (ROOT / path).resolve()
for k in 'train', 'val', 'test':
if dict.get(k): # prepend path
dict[k] = str(path / dict[k]) if isinstance(dict[k], str) else [str(path / x) for x in dict[k]]
# Download dataset if not found locally
val, s = dict.get('val'), dict.get('download')
if val and len(val):
val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path
if not all(x.exists() for x in val):
print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()])
if s and len(s): # download script
print('Downloading %s ...' % s)
if s.startswith('http') and s.endswith('.zip'): # URL
f = Path(s).name # filename
torch.hub.download_url_to_file(s, f)
r = os.system('unzip -q %s -d ../ && rm %s' % (f, f)) # unzip
else: # bash script
r = os.system(s)
print('Dataset autodownload %s\n' % ('success' if r == 0 else 'failure')) # analyze return value
else:
raise Exception('Dataset not found.')这样就只需要在配置文件中改数据集路径即可。
还有点需要注意就是用yolov5训练后的cache文件，在训练yolov7时要删除，不然会报_pickle.UnpicklingError: STACK_GLOBAL requires str