Running train.py will not generate p_ Curve, r_ The four curves

[DA-liu-a]

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Running train.py will not generate p Curve, r The four curves

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👋 Hello @DA-liu-a, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

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Check out our YOLOv8 Docs for details and get started with:

pip install ultralytics

[DA-liu-a]

[DA-liu-a]

The version is correct, but it will not generate a curve graph

[glenn-jocher]

@DA-liu-a hello! I'm sorry to hear that you're having trouble generating the p-curve and r-curve. Can you please provide more details on the issue you are facing? It would be very helpful if you can share your code snippet, or a screenshot of the error logs you received.

Also, make sure that you have properly selected the parameters --save-ckp and --save-dir while running the train.py script, which are required for generating the curves.

I hope this helps! Let me know if you have any further questions or concerns.

[DA-liu-a]

The code is the latest, and the same dataset can be trained with a curve graph in the old version of YOLOv5, but running in the new YOLOv5 master will not generate a curve graph。 This is new： D:\anaconda\envs\yolo\python.exe D:\yolov5-master\train.py github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5 train: weights=weights\yolov5s.pt, cfg=models/yolov5m_face.yaml, data=data\face.yaml, hyp=data\hyps\hyp.scratch.yaml, epochs=5, batch_size=4, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest YOLOv5 2023-5-14 Python-3.8.16 torch-1.7.1+cu110 CUDA:0 (GeForce GTX 1650, 4096MiB)

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=0.0, mixup=0.0 ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 in ClearML Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 runs in Comet TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/

             from  n    params  module                                  arguments                     

0 -1 1 5280 models.common.Focus [3, 48, 3]
1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
2 -1 2 65280 models.common.C3 [96, 96, 2]
3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
4 -1 6 629760 models.common.C3 [192, 192, 6]
5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
6 -1 6 2512896 models.common.C3 [384, 384, 6]
7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
8 -1 1 1476864 models.common.SPP [768, 768, [5, 9, 13]]
9 -1 2 4134912 models.common.C3 [768, 768, 2, False]
10 -1 1 295680 models.common.Conv [768, 384, 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 2 1182720 models.common.C3 [768, 384, 2, False]
14 -1 1 74112 models.common.Conv [384, 192, 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 2 296448 models.common.C3 [384, 192, 2, False]
18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 2 1035264 models.common.C3 [384, 384, 2, False]
21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 2 4134912 models.common.C3 [768, 768, 2, False]
24 [17, 20, 23] 1 24246 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]] YOLOv5m_face summary: 309 layers, 21056406 parameters, 21056406 gradients

Transferred 59/505 items from weights\yolov5s.pt AMP: checks passed optimizer: SGD(lr=0.01) with parameter groups 83 weight(decay=0.0), 86 weight(decay=0.0005), 86 bias train: Scanning D:\yolov5-master\VOCdevkit\labels\train.cache... 59 images, 0 backgrounds, 0 corrupt: 100%|██████████| 59/59 [00:00<?, ?it/s] val: Scanning D:\yolov5-master\VOCdevkit\labels\val.cache... 12 images, 0 backgrounds, 0 corrupt: 100%|██████████| 12/12 [00:00<?, ?it/s]

AutoAnchor: 5.13 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset Plotting labels to runs\train\exp22\labels.jpg... Image sizes 640 train, 640 val Using 4 dataloader workers Logging results to runs\train\exp22 Starting training for 5 epochs...

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    0/4      1.79G        nan        nan          0          6        640: 100%|██████████| 15/15 [00:28<00:00,  1.87s/it]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 2/2 [00:01<00:00,  1.37it/s]
               all         12         20          0          0          0          0

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    1/4       2.1G        nan        nan          0          7        640: 100%|██████████| 15/15 [00:25<00:00,  1.68s/it]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 2/2 [00:01<00:00,  1.38it/s]
               all         12         20          0          0          0          0

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    2/4       2.1G        nan        nan          0          4        640: 100%|██████████| 15/15 [00:25<00:00,  1.67s/it]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 2/2 [00:01<00:00,  1.37it/s]
               all         12         20          0          0          0          0

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    3/4       2.1G        nan        nan          0          6        640: 100%|██████████| 15/15 [00:25<00:00,  1.67s/it]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 2/2 [00:01<00:00,  1.38it/s]
               all         12         20          0          0          0          0

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    4/4       2.1G        nan        nan          0          4        640: 100%|██████████| 15/15 [00:25<00:00,  1.67s/it]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 2/2 [00:01<00:00,  1.39it/s]
               all         12         20          0          0          0          0

5 epochs completed in 0.039 hours. Optimizer stripped from runs\train\exp22\weights\last.pt, 42.5MB Optimizer stripped from runs\train\exp22\weights\best.pt, 42.5MB

Validating runs\train\exp22\weights\best.pt... Fusing layers... YOLOv5m_face summary: 226 layers, 21037638 parameters, 0 gradients Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 2/2 [00:01<00:00, 1.34it/s] all 12 20 0 0 0 0 Results saved to runs\train\exp22

Process finished with exit code 0 `

This is old： `D:\anaconda\envs\yolo\python.exe D:\faceyuanma\FaceDetect5\train.py github: skipping check (not a git repository) YOLOv5 2021-4-12 torch 1.7.1+cu110 CUDA:0 (GeForce GTX 1650, 4096.0MB)

Namespace(adam=False, artifact_alias='latest', batch_size=4, bbox_interval=-1, bucket='', cache_images=False, cfg='models/yolov5s.yaml', data='data/face.yaml', device='', entity=None, epochs=5, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], label_smoothing=0.0, linear_lr=False, local_rank=-1, multi_scale=False, name='exp', noautoanchor=False, nosave=False, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs\train\exp9', save_period=-1, single_cls=False, sync_bn=False, total_batch_size=4, upload_dataset=False, weights='weights/yolov5s.pt', workers=8, world_size=1) tensorboard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ 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=0.0, mixup=0.0 wandb: Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended) Overriding model.yaml nc=10 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 1 156928 models.common.C3 [128, 128, 3]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 1 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, 16.5 GFLOPS

Transferred 354/362 items from weights/yolov5s.pt Scaled weight_decay = 0.0005 Optimizer groups: 62 .bias, 62 conv.weight, 59 other train: Scanning 'D:\faceyuanma\FaceDetect5\VOCdevkit\labels\train.cache' images and labels... 59 found, 0 missing, 0 empty, 0 corrupted: 100%|██████████| 59/59 [00:00<?, ?it/s] val: Scanning 'D:\faceyuanma\FaceDetect5\VOCdevkit\labels\val.cache' images and labels... 12 found, 0 missing, 0 empty, 0 corrupted: 100%|██████████| 12/12 [00:00<?, ?it/s] Plotting labels...

autoanchor: Analyzing anchors... anchors/target = 5.23, Best Possible Recall (BPR) = 1.0000 Image sizes 640 train, 640 test Using 0 dataloader workers Logging results to runs\train\exp9 Starting training for 5 epochs...

 Epoch   gpu_mem       box       obj       cls     total    labels  img_size
   0/4    0.795G    0.1057   0.03017         0    0.1358         7       640: 100%|██████████| 15/15 [00:10<00:00,  1.46it/s]

D:\anaconda\envs\yolo\lib\site-packages\torch\optim\lr_scheduler.py:131: UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate warnings.warn("Detected call of lr_scheduler.step() before optimizer.step(). " Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 2/2 [00:02<00:00, 1.43s/it] all 12 20 0.00273 0.25 0.00109 0.00017

 Epoch   gpu_mem       box       obj       cls     total    labels  img_size
   1/4    0.814G     0.103   0.02903         0    0.1321         7       640: 100%|██████████| 15/15 [00:04<00:00,  3.40it/s]
           Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 2/2 [00:00<00:00,  3.04it/s]
             all          12          20      0.0024         0.4    0.000978    0.000159

 Epoch   gpu_mem       box       obj       cls     total    labels  img_size
   2/4    0.828G    0.1064   0.02965         0    0.1361         7       640: 100%|██████████| 15/15 [00:04<00:00,  3.44it/s]
           Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 2/2 [00:00<00:00,  3.16it/s]
             all          12          20     0.00285        0.05    0.000883    0.000112

 Epoch   gpu_mem       box       obj       cls     total    labels  img_size
   3/4    0.828G    0.1014   0.03021         0    0.1316         7       640: 100%|██████████| 15/15 [00:04<00:00,  3.52it/s]
           Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 2/2 [00:00<00:00,  3.12it/s]
             all          12          20     0.00182         0.2    0.000471    5.62e-05

 Epoch   gpu_mem       box       obj       cls     total    labels  img_size
   4/4     0.83G     0.103   0.02905         0     0.132         7       640: 100%|██████████| 15/15 [00:04<00:00,  3.51it/s]
           Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100%|██████████| 2/2 [00:00<00:00,  2.06it/s]
             all          12          20      0.0023        0.05    0.000593     6.8e-05

5 epochs completed in 0.010 hours.

Optimizer stripped from runs\train\exp9\weights\last.pt, 14.4MB Optimizer stripped from runs\train\exp9\weights\best.pt, 14.4MB

Process finished with exit code 0 `

[DA-liu-a]

`D:\anaconda\envs\yolo\python.exe D:\yolov5-master\train.py train: weights=weights\yolov5s.pt, cfg=models/yolov5s.yaml, data=data\face.yaml, hyp=data\hyps\hyp.scratch.yaml, epochs=5, batch_size=4, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5 YOLOv5 2023-5-14 Python-3.8.16 torch-1.7.1+cu110 CUDA:0 (GeForce GTX 1650, 4096MiB)

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=0.0, mixup=0.0 ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 in ClearML Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 runs in Comet TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/

             from  n    params  module                                  arguments                     

0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
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 2 115712 models.common.C3 [128, 128, 2]
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 1182720 models.common.C3 [512, 512, 1]
9 -1 1 656896 models.common.SPPF [512, 512, 5]
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]] YOLOv5s summary: 214 layers, 7022326 parameters, 7022326 gradients, 15.9 GFLOPs

Transferred 308/349 items from weights\yolov5s.pt AMP: checks passed optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias train: Scanning D:\yolov5-master\VOCdevkit\labels\train.cache... 59 images, 0 backgrounds, 0 corrupt: 100%|██████████| 59/59 [00:00<?, ?it/s] val: Scanning D:\yolov5-master\VOCdevkit\labels\val.cache... 12 images, 0 backgrounds, 0 corrupt: 100%|██████████| 12/12 [00:00<?, ?it/s]

AutoAnchor: 5.13 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset Plotting labels to runs\train\exp23\labels.jpg... Image sizes 640 train, 640 val Using 4 dataloader workers Logging results to runs\train\exp23 Starting training for 5 epochs...

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    0/4      1.07G        nan        nan          0          6        640: 100%|██████████| 15/15 [00:08<00:00,  1.70it/s]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 2/2 [00:00<00:00,  5.10it/s]
               all         12         20          0          0          0          0

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    1/4      1.22G        nan        nan          0          7        640: 100%|██████████| 15/15 [00:07<00:00,  2.12it/s]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 2/2 [00:00<00:00,  5.00it/s]
               all         12         20          0          0          0          0

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    2/4      1.22G        nan        nan          0          4        640: 100%|██████████| 15/15 [00:06<00:00,  2.15it/s]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 2/2 [00:00<00:00,  5.01it/s]
               all         12         20          0          0          0          0

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    3/4      1.22G        nan        nan          0          6        640: 100%|██████████| 15/15 [00:06<00:00,  2.14it/s]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 2/2 [00:00<00:00,  5.13it/s]
               all         12         20          0          0          0          0

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    4/4      1.22G        nan        nan          0          4        640: 100%|██████████| 15/15 [00:07<00:00,  2.12it/s]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 2/2 [00:00<00:00,  5.12it/s]
               all         12         20          0          0          0          0

5 epochs completed in 0.011 hours. Optimizer stripped from runs\train\exp23\weights\last.pt, 14.4MB Optimizer stripped from runs\train\exp23\weights\best.pt, 14.4MB

Validating runs\train\exp23\weights\best.pt... Fusing layers... YOLOv5s summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 2/2 [00:00<00:00, 4.84it/s] all 12 20 0 0 0 0 Results saved to runs\train\exp23

Process finished with exit code 0 `

[DA-liu-a]

I hope to receive your help. Thank you

[glenn-jocher]

@DA-liu-a dear user,

Thank you for reaching out to us with your issue regarding YOLOv5. We are happy to assist you and provide any help that we can. Can you please provide us with more details about the issue you are facing and any error messages you may have encountered? This will help us better understand the problem and provide a more targeted solution.

Looking forward to your response.

Best regards, The YOLOv5 Team.

[github-actions[bot]]

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