Advanced CNN for CIFAR-10 Classification

This project implements an advanced Convolutional Neural Network (CNN) in PyTorch to classify images from the CIFAR-10 dataset into 10 categories: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.

Features

• Pretrained Dataset: Utilizes CIFAR-10, a 60,000-image dataset split into 50,000 training and 10,000 test images.
• Customizable CNN Model: Includes multiple convolutional, batch normalization, pooling, and fully connected layers for accurate classification.
• GPU Support: Automatically utilizes CUDA if available for faster computation.
• Data Augmentation: Enhances training with random cropping, flipping, and normalization.
• Training Scheduler: Reduces learning rate dynamically for fine-tuning.

Setup and Requirements

Prerequisites

• Python 3.x
• Required packages: torch, torchvision, tqdm, Pillow

Install dependencies with:

pip install torch torchvision tqdm Pillow

Clone the Repository

git clone https://github.com/your-username/advanced-cnn-cifar10.git
cd advanced-cnn-cifar10

Training the Model

Run the script to train the model:

python advanced_cnn_cifar10.py

• Adjust num_epochs, batch_size, or learning_rate in the script for your requirements.
• Trained models are saved in the checkpoints/ directory.

Inference

Predict Single Image

from predict import predict_image

model_path = 'checkpoints/advanced_cnn.pth'
image_path = 'path/to/your/image.jpg'

prediction = predict_image(image_path, model_path)
print(f'Predicted class: {prediction}')

Predict Batch of Images

from predict import predict_batch

batch_predictions = predict_batch('path/to/dataset', model_path)
print(batch_predictions)

Results

• Achieves 85-90% accuracy on the CIFAR-10 test set after 30 epochs.
• Checkpoint and accuracy logs are generated during training.

Additional Notes

• Modify the architecture or hyperparameters to experiment with different configurations.
• GPU is highly recommended for faster training.

For more details, check the code files: advanced_cnn_cifar10.py (training) and predict.py (inference).