This repository contains a Jupyter Notebook, KittenPuppyClassification.ipynb, dedicated to the task of classifying images as either kittens or puppies using machine learning techniques. The project utilizes convolutional neural networks (CNNs) to achieve this, leveraging the power of deep learning for image recognition tasks.
The Kitten and Puppy Image Classification project aims to demonstrate an end-to-end machine learning pipeline, from data preprocessing and augmentation to model training, validation, and testing. By analyzing images of kittens and puppies, the project showcases how CNNs can distinguish between these two popular pet categories with high accuracy.
Data Preprocessing: Steps to prepare the image data for training, including resizing, normalization, and augmentation to improve model robustness. Model Building: Construction of a CNN using TensorFlow and Keras, tailored for the binary classification of images. Training and Validation: Detailed process of training the CNN on the dataset, including the use of validation splits to monitor overfitting. Evaluation: Assessment of the trained model's performance on a separate test set, using accuracy and other relevant metrics. Visualization: Visualization of training progress, model predictions, and error analysis to provide insights into the model's behavior.
Python: The primary programming language for the project. TensorFlow and Keras: For building, training, and evaluating the CNN model. Matplotlib: For plotting training history and visualizing model predictions. NumPy: For numerical operations on image data.
To get started with the Kitten and Puppy Image Classification project, follow these instructions:
Clone the Repository: git clone https://github.com/CodCodingCode/KittenPuppyNNClassification.git
Navigate to the Project Directory: cd kitten-puppy-classification
Install Required Libraries: Ensure you have Python installed, then install the required libraries. pip install tensorflow numpy matplotlib
Launch the Notebook: jupyter notebook KittenPuppyClassification.ipynb
We welcome contributions! If you find an issue, have suggestions for improvements, or want to contribute code, please feel free to open an issue or submit a pull request.
License This project is licensed under the MIT License - see the LICENSE file for details.