in create_dataloader dataset = LoadImagesAndLabels( File "F:\WorkSpace\GitSpace\yolov5\utils\dataloaders.py", line 504, in __init__ assert nf > 0 or not augment, f'{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}' AssertionError: train: No labels found ...
For hours now I'm dealing with "WARNING⚠️user config directory is not writeable, defaulting to '\tmp\Ultralytics'." error. I belive it prevents yolov5 training on custom dataset. During training process this error pops up constantly and during each epoch "_loss" values are nan I'm ...
{HELP_URL}") # Check cache 3根据imgs路径找到labels的路径self.label_files self.label_files = img2label_paths(self.im_files) # labels # 4、cache label 下次运行这个脚本的时候直接从cache中取label而不是去文件中取label 速度更快 cache_path = (p if p.is_file() else Path(self.label_files...
3.46s/it] val: WARNING Cache directory C:\Users\~~~\yolov5-master\data\valid is not writeable: [WinError 183] : 'C:\\Users\~~~\yolov5-master\\data\\valid\\labels.cache.npy' -> 'C:\\Users\~~~\yolov5-master\\data\\valid\\labels.cache' Class Images Instances P R mAP50 ...
= 0.3 # 将当前cache version存入最终字典dist try: torch.save(x, path) # save cache to path logging.info(f{ prefix}New cache created: { path}) except Exception as e: logging.info(f{ prefix}WARNING: Cache directory { path.parent} is not writeable: { e}) # path not writeable return ...
python val.py --img 640 --weight yolov5s.pt 1. 没有指定的结果 include_class = [] # filter labels to include only these classes (optional) 1. 指定类别的结果 # 指定第13,14类 include_class = [13,14] # filter labels to include only these classes (optional)...
{HELP_URL}') # Check cache # 根据imgs路径找到labels的路径self.label_files self.label_files = img2label_paths(self.img_files) # labels # cache label 下次运行这个脚本的时候直接从cache中取label而不是去文件中取label 速度更快 cache_path = (p if p.is_file() else Path(self.label_files[...
save_dir=Path(increment_path(Path(opt.project)/opt.name,exist_ok=opt.exist_ok))# incrementrun(save_dir/'labels'ifsave_txtelsesave_dir).mkdir(parents=True,exist_ok=True)# make dir # Initializeset_logging()device=select_device(opt.device)half=device.type!='cpu'# half precision only suppor...
Add detect.py --hide-conf --hide-labels --line-thickness options by @Ashafix inhttps://github.com/ultralytics/yolov5/pull/2658 Default optimize_for_mobile() on TorchScript models by @glenn-jocher inhttps://github.com/ultralytics/yolov5/pull/2908 ...
(save_dir/'labels'ifsave_txtelsesave_dir).mkdir(parents=True,exist_ok=True)# make dir # Initialize set_logging() device=select_device(opt.device) half=device.type!='cpu'# half precision only supported on CUDA # Load model model=attempt_load(weights,map_location=device)# load FP32 model...