Question about v4.0 release - 'Transferred 642/650 items from yolov5l.pt'

❔Question

I am trying to train a custom dataset using a Colab notebook from some months ago. I noticed that recent training produced somewhat poorer results. I noted that Yolov5 v4.0 has been released 9 days ago and I am wondering if the architecture changes are incompatible with the code I have been using.

One thing I did note was the line from the training output shown below:

Transferred 642/650 items from yolov5l.pt

and I wondered whether the difference of 8 items is a result of the removal of one convolution layer from BottleneckCSP (one for each of the eight new C3 layers).

My training line contains the parameter "--weights yolov5l.pt" so I am pulling the .pt file from the repository. Would I be right to assume that the stock .pt file might still contain the additional layers that were part of the v3.1 release. Should this be an issue?

Additional context

                from  n    params  module                                  arguments                     
  0                -1  1      7040  models.common.Focus                     [3, 64, 3]                    
  1                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  2                -1  1    156928  models.common.C3                        [128, 128, 3]                 
  3                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  4                -1  1   1611264  models.common.C3                        [256, 256, 9]                 
  5                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  6                -1  1   6433792  models.common.C3                        [512, 512, 9]                 
  7                -1  1   4720640  models.common.Conv                      [512, 1024, 3, 2]             
  8                -1  1   2624512  models.common.SPP                       [1024, 1024, [5, 9, 13]]      
  9                -1  1   9971712  models.common.C3                        [1024, 1024, 3, False]        
 10                -1  1    525312  models.common.Conv                      [1024, 512, 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   2757632  models.common.C3                        [1024, 512, 3, False]         
 14                -1  1    131584  models.common.Conv                      [512, 256, 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    690688  models.common.C3                        [512, 256, 3, False]          
 18                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1   2495488  models.common.C3                        [512, 512, 3, False]          
 21                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1   9971712  models.common.C3                        [1024, 1024, 3, False]        
 24      [17, 20, 23]  1    102315  models.yolo.Detect                      [14, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [256, 512, 1024]]
Model Summary: 499 layers, 46701355 parameters, 46701355 gradients, 114.5 GFLOPS

Transferred 642/650 items from yolov5l.pt

👋 Hello @Transigent, 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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@Transigent you shouldn't mix pretrained weights from earlier releases with the current release. If in doubt simply reclone a fresh copy of the repo and let it autodownload new pretrained weights.

Only layers with identical names and shapes are transferred, so for example if you are training a model on a different dataset from coco pretrained weights the output layers would not normally transfer (being a different shape due to their different class count).

Thanks for the response @glenn-jocher much appreciated.

ultralytics / yolov5