the estimated time of the three parts of the backbone, neck, and head?

[ahong007007]

Can the time of the entire network architecture be calculated? For example, the estimated time of the three parts of the backbone, neck, and head?

[glenn-jocher]

@ahong007007 yolo.py has built-in profiling:

$ python models/yolo.py --cfg yolov5s.yaml --profile

YOLOv5 🚀 v5.0-458-g6b02015 torch 1.9.0 CPU
Model Summary: 283 layers, 7276605 parameters, 7276605 gradients, 17.1 GFLOPs

 time (ms)     GFLOPs     params  module
     22.62       0.72       3520  models.common.Focus
     17.97       0.95      18560  models.common.Conv
     38.30       0.96      18816  models.common.C3
     13.23       0.95      73984  models.common.Conv
     35.74       2.01     156928  models.common.C3
     11.31       0.95     295424  models.common.Conv
     28.18       2.00     625152  models.common.C3
     11.92       0.94    1180672  models.common.Conv
     18.97       0.53     656896  models.common.SPP
     14.34       0.95    1182720  models.common.C3
      1.83       0.11     131584  models.common.Conv
      0.24       0.00          0  torch.nn.modules.upsampling.Upsample
      0.46       0.00          0  models.common.Concat
     17.28       1.16     361984  models.common.C3
      2.03       0.11      33024  models.common.Conv
      0.33       0.00          0  torch.nn.modules.upsampling.Upsample
      1.26       0.00          0  models.common.Concat
     22.19       1.16      90880  models.common.C3
      6.41       0.47     147712  models.common.Conv
      0.27       0.00          0  models.common.Concat
     14.58       0.95     296448  models.common.C3
      6.27       0.47     590336  models.common.Conv
      0.09       0.00          0  models.common.Concat
     13.81       0.95    1182720  models.common.C3
     12.94       0.74     229245  Detect
    312.55          -          -  Total

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