This repository contains a deep learning model for image classification on the CIFAR-10 dataset using TensorFlow and Keras.
The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 different classes, with 6,000 images per class. The goal of this project is to develop a deep neural network that can accurately classify these images into their respective classes.
- Utilizes TensorFlow and Keras for building and training the deep learning model.
- Preprocesses the dataset by normalizing the image data.
- Implements a convolutional neural network (CNN) architecture with multiple layers.
- Utilizes RMSprop as the optimizer and categorical cross-entropy as the loss function.
- Provides detailed model summary and training statistics.
- Clone this repository to your local machine.
git clone https://github.com/Banji575/Deep-Learning-for-CIFAR-10-Image-Classification.git
- Install the required dependencies using
pip.
pip install -r requirements.txt
- Run the Jupyter notebook or Python script to train the model.
jupyter notebook cifar10_image_classification.ipynb
- You can adjust hyperparameters and experiment with the model architecture as needed.
The deep learning model is trained with the following configuration:
- Number of Epochs: 40
- Batch Size: 64
- Optimizer: RMSprop
- Validation Split: 30%
- Number of Classes: 10
After training, the model's performance will be evaluated on the test set, and the results will be displayed. You can analyze the accuracy and loss to assess the model's performance.
This project is licensed under the MIT License - see the LICENSE file for details.
Feel free to contribute, make improvements, and use this code for your image classification projects.