Solved by ptrblck in post #2 
              
              
                The in_features used in the tutorial represent the number of input features to the last linear layer. 
Based on your code snippet it seems that the last activations are calculated in: 
        bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1)
    …