WildSpotr CNN Model

This repository contains a simple Convolutional Neural Network (CNN) model trained for a wildlife spotting application. The model is implemented using TensorFlow/Keras and is designed to classify images as either containing wildlife or not.

Usage

1. Clone the Repository:

   git clone https://github.com/WildSpotr/mlm.git
   cd mlm
   

2. Install Dependencies:

   pip install -r requirements.txt
   

3. Prepare Your Dataset:

   • Replace data and labels in the code with your dataset and corresponding labels.
   • Ensure that your dataset is structured appropriately and contains images of wildlife and non-wildlife scenes.

4. Train the Model:

   python train_model.py
   

5. Evaluate the Model:

   python evaluate_model.py
   

Model Architecture

The CNN model architecture used in this project consists of several convolutional layers followed by max-pooling layers and dense layers. The final layer uses a sigmoid activation function for binary classification.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.

License

This project is licensed under the GPL-3.0 License. See the LICENSE file for details.