Cat and Dog Prediction Using CNN Overview This project implements a Convolutional Neural Network (CNN) to classify images of cats and dogs. The model is trained on a dataset of labeled images to distinguish between cat and dog images with high accuracy.

Table of Contents Installation Usage Features Code Structure Contributing License Contact Installation Prerequisites Ensure you have the following installed:

Python 3.11 TensorFlow Keras NumPy Matplotlib Steps to Install Clone the repository:

git clone https://github.com/Jadhavsakshi7887/Projects/blob/main/CAT_VS_DOG.ipynb Navigate to the project directory: cd cat-dog-prediction Install the required libraries: pip install -r requirements.txt Training the Model To train the CNN model, run the following command: python train_model.py This script will load the dataset, train the model, and save the trained model to a file. Making Predictions To classify a new image as either a cat or a dog, use the following command: python predict.py --image path/to/image.jpg Replace path/to/image.jpg with the path to the image you want to classify. Features Model Training: Trains a CNN model with the provided dataset. Image Classification: Classifies new images as either cat or dog using the trained model. Code Structure train_model.py: Script for training the CNN model. predict.py: Script for making predictions on new images. model.py: Contains the CNN architecture and model definition. data/: Directory containing training and test datasets. requirements.txt: Lists the Python libraries required to run the project. Contributing Contributions are welcome! To contribute: Fork the repository. Create a new branch (git checkout -b feature-branch). Make your changes. Commit your changes (git commit -am 'Add new feature'). Push to the branch (git push origin feature-branch). Create a pull request. Please follow the guidelines provided in CONTRIBUTING.md for detailed instructions.

Contact For questions or support, please contact Sakshi Jadhav at [email protected]