This repository contains code and resources for performing image classification using various machine learning and deep learning techniques. Image classification is the process of categorizing images into predefined classes or categories. This README provides an overview of the project, its structure, and instructions for getting started.
Image classification is a fundamental task in computer vision that has a wide range of applications, including object recognition, medical image analysis, and more. This repository aims to provide a collection of image classification methods implemented using popular libraries and frameworks such as TensorFlow and PyTorch. The project also serves as a learning resource for those interested in understanding and implementing image classification algorithms.
To get started with this project, follow these installation steps:
- Clone this repository:
git clone https://github.com/himanshunanda22/image-classification.git - Navigate to the project directory:
cd image-classification - Set up a virtual environment (optional but recommended):
virtualenv venv - Activate the virtual environment:
source venv/bin/activate - Install the required dependencies:
pip install -r requirements.txt
- Prepare your dataset (see the Dataset section for more details).
- Choose a model from the Models section or implement your own.
- Train the selected model using the provided code.
- Evaluate the trained model using the evaluation techniques outlined in the Evaluation section.
- Experiment with hyperparameters, data augmentation, and other techniques to improve performance.
The success of image classification heavily depends on the quality and diversity of the dataset used for training and evaluation. In this project, we recommend using well-known datasets such as CIFAR-10, CIFAR-100, MNIST, or custom datasets relevant to your application. Make sure to preprocess the data appropriately and split it into training, validation, and testing subsets.
This project includes the following image classification models:
- Convolutional Neural Network (CNN)
- Residual Neural Network (ResNet)
- Transfer Learning (using pre-trained models like VGG16, ResNet50, etc.)
Feel free to explore each model's implementation in the respective directories. You can choose a model based on the complexity of your task and the available computing resources.
Evaluating the performance of your image classification model is crucial. Common evaluation metrics include accuracy, precision, recall, F1-score, and confusion matrices. You can use these metrics to understand how well your model is performing and make necessary adjustments to improve its performance.
Contributions to this image classification project are welcome! If you've implemented a new model, improved an existing one, or have any other valuable additions, feel free to create a pull request. Please ensure your code follows good coding practices and includes appropriate documentation.
This project is licensed under the MIT License - see the LICENSE file for details.
Happy classifying! If you have any questions, issues, or suggestions, please don't hesitate to contact us or open an issue. We hope this repository helps you dive into the exciting world of image classification.# Image-Classification