ResNet Classifier

Overview

This project implements a classifier using the Residual Neural Network (ResNet) architecture. The ResNet model is a deep neural network known for its ability to efficiently train very deep networks. In this project, the ResNet classifier is designed to perform image classification on a specific dataset.

Features

• Residual Blocks: The classifier utilizes residual blocks, allowing the training of deeper networks without encountering the vanishing gradient problem.

• Deep Learning: Implemented using deep learning frameworks such as TensorFlow and Keras.

• Transfer Learning: The ResNet architecture supports transfer learning, making it suitable for various image classification tasks.

Dataset

The classifier is trained and tested on a labeled dataset of images. The dataset used for this project is preprocessed to ensure compatibility with the ResNet architecture.

Model Architecture

The classifier is built on the ResNet architecture, which includes residual blocks with skip connections. The architecture is chosen for its ability to efficiently train deep neural networks.

Training

The model is trained on the specified dataset using appropriate training parameters. The training process involves optimizing the model's weights and biases to achieve high accuracy in image classification.

Usage

To use the ResNet classifier, follow these steps:

1. Install the required dependencies listed in the requirements.txt file.
2. Load the pre-trained ResNet model using the provided script.
3. Input an image for classification.

python classify_resnet.py --image_path path/to/your/image.jpg