Image Classifier with PyTorch

Project roadmap

1. 01

   Prepare the dataset and loaders

   ~2h

   Set up a torchvision dataset with train/validation splits and data augmentation transforms.

2. 02

   Define the CNN architecture

   ~2h

   Build a small convolutional network with a few conv and pooling layers plus a classification head.

3. 03

   Write the training loop

   ~2.5h

   Implement forward/backward passes, loss computation, and optimizer steps, tracking accuracy per epoch.

4. 04

   Evaluate and visualize results

   ~2h

   Evaluate on a held-out test set, compute a confusion matrix, and plot training/validation loss curves.

5. 05

   Save and load model checkpoints

   ~1.5h

   Add checkpointing so training can resume and the trained model can be loaded for inference later.

Resources

• DocsPython Documentation