Use the ConvNext classifier layer example

 refer to code

..

import torch.nn as nn

import torchvision

# Load the pre-trained ConvNext model

model = torchvision.models.convnext_base(pretrained=True, stochastic_depth_prob=0.1, layer_scale=1e-4)

# Define a new linear layer with 10 output channels

new_linear_layer = nn.Linear(1024, 10)

# Replace the last linear layer in the classifier with the new one

classifier = model.classifier

classifier[-1] = new_linear_layer

# Set the modified classifier as the new classifier for the model

model.classifier = classifier

# Print the modified model architecture

print(model)

..

In this code, we first load the pre-trained ConvNext model using torchvision.models.convnext_base. 

We then define a new linear layer with 10 output channels using nn.Linear. 

Next, we extract the existing classifier from the model using model.classifier, and replace the last linear layer in the classifier with the new one using indexing ([-1]). 

Finally, we set the modified classifier as the new classifier for the ConvNext model using model.classifier = classifier.

This code should print out the modified model architecture, which will have a classifier layer that is identical to the original ConvNext classifier except for the number of output channels in the last linear layer.

Thank you.