This project demonstrates image classification using Convolutional Neural Networks (CNNs) implemented in PyTorch. The goal is to classify images into predefined categories, showcasing expertise in handling unstructured image data using deep learning techniques.
Deep Learning Framework: Built and trained CNNs using PyTorch.
Data Preprocessing: Used torchvision.transforms for resizing, normalization, and augmentation. Handled datasets using torchvision.datasets (e.g., CIFAR-10) and DataLoader for efficient batching. Optimization: Trained the model using SGD with momentum and evaluated its performance with accuracy and loss metrics.
Explainability: Implemented Grad-CAM to visualize regions of images influencing model predictions.
Transfer Learning: Integrated pre-trained models (e.g., ResNet) to enhance performance.
Saved and loaded the model using torch.save and torch.load. Tools and Frameworks Programming Language: Python Framework: PyTorch Data Handling: torchvision, Pillow Visualization: Matplotlib, Grad-CAM
Real-world use cases in healthcare, autonomous vehicles, and e-commerce. Demonstrates core skills in deep learning, image processing, and explainable AI.