测试训练集二十几张图片，在m1 mac上的运行时间一共8.084 小时，共152 epochs。对于这个计算速度还是比较让人吃惊的，这个效率也太低了。对于需要处理图像的训练这个速度也无法让人接受。
152 epochs completed in 8.084 hours. Optimizer stripped from runs/train/exp3/weights/last.pt, 14.4MB Optimizer stripped from runs/train/exp3/weights/best.pt, 14.4MB wandb: Waiting for W&B process to finish, PID 63332 wandb: Program ended successfully. wandb: Find user logs for this run at: /Users/zhongming/PycharmProjects/yolov5/wandb/offline-run-20210913_191626-18h6dxo0/logs/debug.log wandb: Find internal logs for this run at: /Users/zhongming/PycharmProjects/yolov5/wandb/offline-run-20210913_191626-18h6dxo0/logs/debug-
下面是Win 10下的效果：
96 epochs completed in 0.014 hours.这个速度对比就明显了
(E:\anaconda_dirs\venvs\yolov5-gpu) F:\Pycharm_Projects\yolov5>python train_ads.py train: weights=yolov5s.pt, cfg=, data=data/ads.yaml, hyp=data/hyps/hyp.scratch.yaml, epochs=300, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=True, adam=False, sync_bn=False, workers=4, project=runs/train, entity=None, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias=latest, local_rank=-1, freeze=0, patience=30 github: skipping check (offline), for updates see https://github.com/ultralytics/yolov5 YOLOv5 v5.0-405-gfad57c2 torch 1.9.0 CUDA:0 (NVIDIA GeForce RTX 3080, 10240.0MB) hyperparameters: lr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0 TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/ wandb: (1) Create a W&B account wandb: (2) Use an existing W&B account wandb: (3) Don't visualize my results [34m[1mwandb[0m: Enter your choice: 2 wandb: You chose 'Use an existing W&B account' wandb: You can find your API key in your browser here: https://wandb.ai/authorize [34m[1mwandb[0m: Paste an API key from your profile and hit enter: wandb: Appending key for api.wandb.ai to your netrc file: C:\Users\obaby/.netrc wandb: Tracking run with wandb version 0.12.1 wandb: Syncing run volcanic-shape-1 wandb: View project at https://wandb.ai/obaby/YOLOv5 wandb: View run at https://wandb.ai/obaby/YOLOv5/runs/3fb47yv2 wandb: Run data is saved locally in F:\Pycharm_Projects\yolov5\wandb\run-20210915_203301-3fb47yv2 wandb: Run `wandb offline` to turn off syncing. Overriding model.yaml nc=80 with nc=1 from n params module arguments 0 -1 1 3520 models.common.Focus [3, 32, 3] 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] 2 -1 1 18816 models.common.C3 [64, 64, 1] 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] 4 -1 3 156928 models.common.C3 [128, 128, 3] 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] 6 -1 3 625152 models.common.C3 [256, 256, 3] 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] 8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]] 9 -1 1 1182720 models.common.C3 [512, 512, 1, False] 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 models.common.Concat [1] 13 -1 1 361984 models.common.C3 [512, 256, 1, False] 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 16 [-1, 4] 1 0 models.common.Concat [1] 17 -1 1 90880 models.common.C3 [256, 128, 1, False] 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] 19 [-1, 14] 1 0 models.common.Concat [1] 20 -1 1 296448 models.common.C3 [256, 256, 1, False] 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] 22 [-1, 10] 1 0 models.common.Concat [1] 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] 24 [17, 20, 23] 1 16182 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]] Model Summary: 283 layers, 7063542 parameters, 7063542 gradients, 16.4 GFLOPs Transferred 356/362 items from yolov5s.pt Scaled weight_decay = 0.0005 optimizer: SGD with parameter groups 59 weight, 62 weight (no decay), 62 bias train: Scanning 'data\train.cache' images and labels... 16 found, 0 missing, 0 empty, 0 corrupted: 100%|█| 16/16 [00:00 val: Scanning 'data\val.cache' images and labels... 2 found, 0 missing, 0 empty, 0 corrupted: 100%|█| 2/2 [00:00<?, ?it Plotting labels... autoanchor: Analyzing anchors... anchors/target = 4.44, Best Possible Recall (BPR) = 1.0000 Image sizes 640 train, 640 val Using 4 dataloader workers Logging results to runs\train\exp19 Starting training for 300 epochs... Epoch gpu_mem box obj cls labels img_size 0/299 3.4G 0.1378 0.0201 0 28 640: 100%|████████████| 1/1 [00:04<00:00, 4.35s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|█| 1/1 [00:00<00:00, 28.65 all 2 0 0 0 0 0 Epoch gpu_mem box obj cls labels img_size 94/299 3.63G 0.06651 0.02039 0 27 640: 100%|████████████| 1/1 [00:00<00:00, 7.90it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|█| 1/1 [00:00<00:00, 35.77 all 2 2 0.137 0.5 0.0874 0.0514 Epoch gpu_mem box obj cls labels img_size 95/299 3.63G 0.06247 0.01776 0 22 640: 100%|████████████| 1/1 [00:00<00:00, 7.90it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|█| 1/1 [00:00<00:00, 37.14 all 2 2 0.171 0.427 0.104 0.0729 EarlyStopping patience 30 exceeded, stopping training. 96 epochs completed in 0.014 hours. Optimizer stripped from runs\train\exp19\weights\last.pt, 14.4MB Optimizer stripped from runs\train\exp19\weights\best.pt, 14.4MB wandb: Waiting for W&B process to finish, PID 13352 wandb: Program ended successfully. wandb: wandb: Find user logs for this run at: F:\Pycharm_Projects\yolov5\wandb\run-20210915_203301-3fb47yv2\logs\debug.log wandb: Find internal logs for this run at: F:\Pycharm_Projects\yolov5\wandb\run-20210915_203301-3fb47yv2\logs\debug-internal.log wandb: Run summary: wandb: train/box_loss 0.06247 wandb: train/obj_loss 0.01776 wandb: train/cls_loss 0.0 wandb: metrics/precision 0.17077 wandb: metrics/recall 0.42693 wandb: metrics/mAP_0.5 0.10413 wandb: metrics/mAP_0.5:0.95 0.07289 wandb: val/box_loss 0.07609 wandb: val/obj_loss 0.02425 wandb: val/cls_loss 0.0 wandb: x/lr0 0.00078 wandb: x/lr1 0.00078 wandb: x/lr2 0.09128 wandb: _runtime 60 wandb: _timestamp 1631709241 wandb: _step 96 wandb: Run history: wandb: train/box_loss █████▇▇▇▆▇▆▆▆▅▆▅▄▅▄▄▃▄▄▃▃▂▃▃▃▂▂▃▂▁▂▁▂▁▁▁ wandb: train/obj_loss ▅▅▅▄▃▃▅▃▄▄▃▂▃▁▄▃▅▃▅▃▅▂▄▆▅▅▄▄█▆▄▆▄▅▅▃▇█▅▅ wandb: train/cls_loss ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ wandb: metrics/precision ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃▁▁▁▁█▁▆▁▂▂▁▁▁▁▃▅▄▃▃ wandb: metrics/recall ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█▁█████▇██████████████ wandb: metrics/mAP_0.5 ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃▁▁▁▁█▁▆▁▂▂▁▁▂▂▃▄▃▃▃ wandb: metrics/mAP_0.5:0.95 ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▁▁▁▃▁█▁▁▁▁▁▁▂▃▅▄▅▅ wandb: val/box_loss ███████▇▇▇▇▇▆▇▆▆▆▅▅▅▄▄▅▄▃▃▄▃▃▃▃▄▃▂▃▂▁▁▁▁ wandb: val/obj_loss ▅▄▄▄▄▃▃▃▃▃▂▂▁▁▁▁▂▂▂▂▂▂▁▃▄▄▃▄▄▃▄▄▅▇▅▇▇▇██ wandb: val/cls_loss ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ wandb: x/lr0 ▁▁▁▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇▇█████ wandb: x/lr1 ▁▁▁▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇▇█████ wandb: x/lr2 ███▇▇▇▇▇▇▆▆▆▆▆▆▅▅▅▅▅▄▄▄▄▄▄▃▃▃▃▃▂▂▂▂▂▂▁▁▁ wandb: _runtime ▁▁▁▂▂▂▂▂▂▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇▇██ wandb: _timestamp ▁▁▁▂▂▂▂▂▂▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇▇██ wandb: _step ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███ wandb: wandb: Synced 6 W&B file(s), 18 media file(s), 1 artifact file(s) and 0 other file(s) wandb: wandb: Synced volcanic-shape-1: https://wandb.ai/obaby/YOLOv5/runs/3fb47yv2 Results saved to runs\train\exp19
