LOGGER.warning(f'{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}') x['hash'] = get_hash(self.label_files + self.im_files) x['results'] = nf, nm, ne, nc, len(self.im_files) x['msgs'] = msgs # warnings x['version'] = self.cache_version # cache version t...
train: WARNING⚠️No labels found in /content/EMSL/Train/Images.cache. Seehttps://docs.ultralytics.com/yolov5/tutorials/train_custom_data train: New cache created: /content/EMSL/Train/Images.cache Traceback (most recent call last): ...
WARNING: Dataset not found, nonexistent paths: ['/content/gdrive/MyDrive/Object/yolov7/content/gdrive/MyDrive/Object/yolov7/data/val/img'] Traceback (most recent call last): File "train.py", line 616, in train(hyp, opt, device, tb_writer) ...
batch_idx=torch.arange(len(batch["img"])), cls=batch["cls"].view(-1),# warning: use .view(), not .squeeze() for Classify modelsfname=self.save_dir /f"val_batch{ni}_labels.jpg", names=self.names, on_plot=self.on_plot, )# 绘制输入图像上的预测边界框并保存结果defplot_predictions...
LOGGER.warning(f"WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels") # Print results per class if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): for i, c in enumerate(ap_class): LOGGER.info(pf % (names[c], seen...
warning("WARNING ⚠️ Upgrade to torch>=2.0.0 for deterministic training.") else: # 关闭确定性算法,允许非确定性行为 torch.use_deterministic_algorithms(False) torch.backends.cudnn.deterministic = False class ModelEMA: """ Updated Exponential Moving Average (EMA) from https://github.com/r...
(dataset, 'rect', False) and shuffle: LOGGER.warning("WARNING ⚠️ 'rect=True' is incompatible with DataLoader shuffle, setting shuffle=False") shuffle = False workers = 0 return build_dataloader(dataset, batch_size, workers, shuffle, rank) def preprocess_batch(self, batch): batch['img...
print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.') # 筛选出 label 大于 2 个像素的框拿来聚类, [...]内的相当于一个筛选器, 为True的留下 wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels ...
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...
targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1,0,1,0]])# 将目标数据拆分为类别标签(gt_labels)和边界框(gt_bboxes)gt_labels, gt_bboxes = targets.split((1,4),2)# cls, xyxy# 生成用于过滤的掩码(mask_gt),判断边界框是否有效mask_gt = gt_bbox...