This repository contains a Python project focused on building a neural network model for classifying images of dogs and cats. The project utilizes deep learning techniques and libraries such as TensorFlow and Keras to achieve high accuracy in image classification tasks
The project aims to develop a model capable of distinguishing between images of dogs and cats with high precision. This is achieved by training a Convolutional Neural Network (CNN) on a dataset of labeled images. The model is then used to classify new, unseen images into one of the two categories.
- Python 3.x
- TensorFlow
- Keras
- NumPy
- Scikit-learn
- Pillow
- OpenCV
- Project Structure
- data/: Contains the dataset of dog and cat images.
- models/: Stores the trained neural network models.
- src/: Jupyter notebooks for exploratory data analysis and model development.
- README.md: This file, providing an overview and instructions for using the project.
Clone the repository. Install the required Python libraries listed in the requirements.txt file. Download the dataset of dog and cat images and place it in the data/ directory. Run the scripts in the scripts/ directory to preprocess the data, train the model, and evaluate its performance.
Data Preprocessing: Use the preprocess.py script to normalize and augment the dataset. Model Training: Execute the train.py script to train the neural network model. Model Evaluation: Run the evaluate.py script to test the model's performance on the test dataset.
Contributions to improve the model's accuracy, add new features, or enhance the documentation are welcome. Please follow the standard GitHub workflow: fork, branch, commit, and pull request.
This project is licensed under the MIT License. See the LICENSE file for more details.
This README provides a concise summary of the project, its requirements, and instructions for getting started. The detailed implementation, including data preprocessing, model architecture, and training scripts, is included in the project's code and documentation.