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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] Running YOLOv5s(smallest version) and Checking Latency on Raspberry Pi 4 #12

Closed jaskiratsingh2000 closed 3 years ago

jaskiratsingh2000 commented 3 years ago

Here are the latency results for that:

YOLOv5 šŸš€ v5.0-100-g4a8d238 torch 1.7.0a0+e85d494 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

 time (ms)     GFLOPS     params  module
    128.92       0.00       3520  models.common.Focus
     92.05       0.00      18560  models.common.Conv
    214.69       0.00      18816  models.common.C3
     76.22       0.00      73984  models.common.Conv
    346.07       0.00     156928  models.common.C3
     56.44       0.00     295424  models.common.Conv
    306.50       0.00     625152  models.common.C3
     48.60       0.00    1180672  models.common.Conv
    104.59       0.00     656896  models.common.SPP
    130.20       0.00    1182720  models.common.C3
     26.57       0.00     131584  models.common.Conv
      5.61       0.00          0  torch.nn.modules.upsampling.Upsample
      8.89       0.00          0  models.common.Concat
    179.37       0.00     361984  models.common.C3
     26.49       0.00      33024  models.common.Conv
      7.10       0.00          0  torch.nn.modules.upsampling.Upsample
     14.17       0.00          0  models.common.Concat
    192.31       0.00      90880  models.common.C3
     46.88       0.00     147712  models.common.Conv
      8.45       0.00          0  models.common.Concat
    163.95       0.00     296448  models.common.C3
     27.12       0.00     590336  models.common.Conv
      0.32       0.00          0  models.common.Concat
    120.57       0.00    1182720  models.common.C3
     56.00       0.00     229245  Detect
2388.1ms total