This repository contains a Keras-based implementation of a convolutional neural network (CNN) for image classification on the CIFAR-100 dataset.
The CIFAR-100 dataset is a collection of 60,000 32x32 color images in 100 classes, with 600 images per class. The dataset is divided into 50,000 training images and 10,000 test images.
The CNN model consists of multiple convolutional and max pooling layers, followed by flatten and dense layers. The model is trained using the Adam optimizer and categorical cross-entropy loss function.
- Implementation of a CNN model using Keras
- Training and testing on the CIFAR-100 dataset
- Data preprocessing using Keras
- Visualization of training and validation accuracy and loss using matplotlib
- Prediction and visualization of test images using matplotlib
- Python 3.x
- Keras 2.x
- TensorFlow 2.x
- NumPy
- Pandas
- Matplotlib
- OpenCV
- Clone the repository and navigate to the project directory.
- Install the required packages using
pip install -r requirements.txt. - Follow the instructions in the notebook to train and test the model.
This project is licensed under the MIT License. See LICENSE for details.
Contributions are welcome! If you'd like to contribute to this project, please fork the repository and submit a pull request.