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jaskiratsingh2000 / Research-Scalable-Vehicle-Detection-on-Edge-Devices

Research project tracker: Scalable Vehicle Detection on Edge Devices
MIT License
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[Testing] Figure out the Latency for YOLO #6

Closed jaskiratsingh2000 closed 3 years ago

jaskiratsingh2000 commented 3 years ago

Results with no GPU Enabled on Google Colab

/content/yolov5
YOLOv5 :rocket: v5.0-44-g5afe783 torch 1.8.1+cu101 CPU
                 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    229245  Detect                                  [80, [[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, 7276605 parameters, 7276605 gradients, 17.1 GFLOPS
 time (ms)     GFLOPS     params  module
     11.90       0.18       3520  models.common.Focus
      8.65       0.24      18560  models.common.Conv
     15.68       0.24      18816  models.common.C3
      6.20       0.24      73984  models.common.Conv
     20.03       0.50     156928  models.common.C3
      5.97       0.24     295424  models.common.Conv
     17.69       0.50     625152  models.common.C3
      8.36       0.24    1180672  models.common.Conv
     15.80       0.13     656896  models.common.SPP
     10.73       0.24    1182720  models.common.C3
      1.35       0.03     131584  models.common.Conv
      0.70       0.00          0  torch.nn.modules.upsampling.Upsample
      0.16       0.00          0  models.common.Concat
     11.79       0.29     361984  models.common.C3
      1.22       0.03      33024  models.common.Conv
      1.33       0.00          0  torch.nn.modules.upsampling.Upsample
      0.29       0.00          0  models.common.Concat
     11.75       0.29      90880  models.common.C3
      3.47       0.12     147712  models.common.Conv
      0.10       0.00          0  models.common.Concat
      8.68       0.24     296448  models.common.C3
      3.87       0.12     590336  models.common.Conv
      0.04       0.00          0  models.common.Concat
     10.87       0.24    1182720  models.common.C3
      5.37       0.18     229245  Detect
182.0ms total
jaskiratsingh2000 commented 3 years ago

All these experiments were done on this Google Colab link: https://colab.research.google.com/drive/1vF7WZhcpAKbQSUpGyU9-R4YGwafTTzI-?usp=sharing