PyTorch Card Classifier

A multiclass neural network for card recognition using PyTorch.

Overview

Organized in three main parts:

• Using PyTorch datasets and data loaders
• Defining a PyTorch model with a pretrained backbone
• Setting up a training loop

Steps

1. Dataset
   Organize your data for PyTorch and use data loaders for batching and shuffling. The cards dataset can be found here: https://www.kaggle.com/datasets/gpiosenka/cards-image-datasetclassification/data.

2. Model

   • Use a pretrained model from timm or similar.
   • Modify the final layer for 53 targets.
   • Structure:
     • self.base_model – full pretrained model
     • self.features – backbone layers
     • self.classifier – new head
   • Connect layers in forward.

3. Training Loop
   Standard PyTorch loop with loss calculation, backpropagation, and optimizer steps.