Implement Image Classification on dermaMNIST about torch-control HOT 3 CLOSED

A basic tutorial style CNN + FCN gets to 0.69 train acc in one epoch and then stays there.

ResNet50 from torchvision gets:
Fresh weights: [98/100] train_loss: 0.333 - train_acc: 0.884 - eval_loss: 1.127 - eval_acc: 0.704
Pretrained weights: [100/100] train_loss: 0.207 - train_acc: 0.932 - eval_loss: 1.095 - eval_acc: 0.754

Using pretrained than random weights as a starting point is much better for optimization and generalizability, as always. Another way to help generalizability would be to train just some of the layers (e.g. add additional heads).

from torch-control.

Some fun with basics
D:\Source\torch-control\projects\ComputerVision\dermMNIST\train_basic_network.py

A) A network bottlenecked to 1x1 by CNN+maxpooling, that is torch.Size([100, 4096, 1, 1]) going into dense layers, will still train ok.

B) But a network that has another conv layer (no maxpool) following this won't. Likely because of issues with padding a 1x1 feature map for 3x3 Conv2d to run on it.

C) Maxpooling/increasing channels vs raw CNN layers helps optimization but has little impact on the final accuracy. The latter may not hold in a more difficult dataset.
Maxpool:
[22/100] train_loss: 0.129 - train_acc: 0.956 - eval_loss: 1.831 - eval_acc: 0.699

No maxpool:
[22/100] train_loss: 0.510 - train_acc: 0.809 - eval_loss: 1.175 - eval_acc: 0.620
[52/100] train_loss: 0.012 - train_acc: 0.996 - eval_loss: 2.166 - eval_acc: 0.737

D) Without activation layers, the network will take longer to optimize. It still fits train data okay but fails to generalize, even with dropout:
[22/100] train_loss: 0.609 - train_acc: 0.769 - eval_loss: 0.725 - eval_acc: 0.727
[70/100] train_loss: 0.143 - train_acc: 0.936 - eval_loss: 2.066 - eval_acc: 0.686

from torch-control.

The issue of the network underfitting this data was caused by a bug in the basic implementation. Pytorch CrossEntropyLoss applies nn.log_softmax() inside it and needs to be passed raw logits. If it is passed nn.log_softmax(), it works fine, but if it is passed nn.Softmax, this really hurts optimization
With nn.Softmax() activation - stuck at 0.67 train acc:
[22/100] train_loss: 1.499 - train_acc: 0.670 - eval_loss: 1.467 - eval_acc: 0.669

No nn.Softmax() layer - just pass logits
[22/100] train_loss: 0.129 - train_acc: 0.956 - eval_loss: 1.831 - eval_acc: 0.699

In the end, fitting the train data in dermaMNIST turned out to be very easy. There is still a class imbalance and generalizability issue to val data, which will be addressed in a future issue.

from torch-control.