Automated Object Detection and Classification Using Faster R-CNN Architecture

This project is a deep learning-based object detection task that uses PyTorch and Faster R-CNN, a well-known neural network model for identifying and localizing objects in images. The code imports essential libraries for image processing, model training, and data handling, then extracts a dataset from a ZIP file for use in training and evaluation. It likely involves preparing a custom dataset class, applying image transformations, and fine-tuning a pre-trained Faster R-CNN model using labeled data. The goal is to train the model to accurately detect and draw bounding boxes around specific objects within images, demonstrating how transfer learning and deep learning techniques can be applied for real-world computer vision applications such as automated surveillance, medical imaging, or autonomous driving.