implementation_plan.md

Implementation Plan - CIFAR-10 CNN Classifier

This plan outlines the steps to build, train, and evaluate a Convolutional Neural Network (CNN) for the CIFAR-10 dataset.

1. Environment Setup

• Verify installation of torch, torchvision, matplotlib.
• Import necessary modules.

2. Data Preparation

• Load CIFAR-10 dataset using torchvision.datasets.
• Normalize and transform data to Tensors.
• Explore data shapes and visualize sample images.

3. Model Architecture

• Build a PyTorch nn.Module CNN:
  • Input layer: 32x32x3 images.
  • Multiple Convolutional blocks (Conv2d -> BatchNorm2d -> ReLU -> MaxPool2d -> Dropout).
  • Flatten layer.
  • Fully connected layers with BatchNorm and Dropout.
  • Output layer: 10 units.

4. Training Configuration

• Loss Function: nn.CrossEntropyLoss().
• Optimizer: optim.Adam.
• Device: Use CUDA if available, else CPU.

5. Model Training

• Train the model on the training set.
• Validate on the test/validation set.
• Save the training history.

6. Evaluation and Visualization

• Evaluate the model on the test set.
• Plot Training vs. Validation Accuracy/Loss.
• Display a confusion matrix or classification report.
• Save the final model.

7. Inference Script (Optional)

• Create a script to load the model and predict labels for new images.