Using 2 dataloader workers Logging results to runs/detect/train Starting training for 10 epochs... Closing dataloader mosaic albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=...
print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU')) 1. 2. 3. 4. 5. 6. 7. 8. 9. Setup complete. Using torch 1.7.0+cu101 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7,...
cache=False, device=0, workers=8, project=None, name=None, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10,
optimizer: SGD(lr=0.01, momentum=0.9) with parameter groups 143 weight(decay=0.0), 206 weight(decay=0.0005), 226 bias(decay=0.0) Image sizes 640 train, 640 val Using 0 dataloader workers Logging results to runs\detect\train8 Starting training for 300 epochs... Epoch GPU_mem giou_loss cls...
(如果进行数据增强)self.max_buffer_length =min((self.ni, self.batch_size *8,1000))ifself.augmentelse0# 缓存图像(缓存选项包括 True, False, None, "ram", "disk")self.ims, self.im_hw0, self.im_hw = [None] * self.ni, [None] * self.ni, [None] * self.ni# 生成每个图像文件对应...
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 66 weight(decay=0.0), 77 weight(decay=0.0005), 76 bias(decay=0.0) Image sizes 640 train, 640 val Using 0 dataloader workers Logging results to runs\segment\train3 Starting training for 20 epochs... Epoch GPU_mem box_loss ...
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'] = batch['img'].to(self.device, non_...
Check DataLoader Arguments: Since you've already experimented with different numbers of workers, it might help to set workers to 0, which will run the dataloading on the main process and may provide more verbose errors. Keep in mind that this could slow down the loading process. Resource Limi...
dataloader(dataset, batch_size, self.args.workers, rank=rank)# 如果不是训练模式,将推理转换添加到模型中ifmode !="train":ifis_parallel(self.model): self.model.module.transforms = loader.dataset.torch_transformselse: self.model.transforms = loader.dataset.torch_transforms# 返回 DataLoader 对象...
s\cfg\datasets\VOC2012.yaml,epochs=10,patience=50,batch=16,imgsz=640,save=True,save_period=-1,cache=False,device=None,workers=8,project=None,name=None,exist_ok=False,pretrained=model\yolov8n.pt,optimizer=auto,verbose=True,seed=0,deterministic=True,single_cls=False,rect=False,cos_lr=False...