This project implements a Convolutional Neural Network (CNN) for detecting rats in images. The model is trained using a dataset of images containing rats and images without rats.
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dataset/
: Contains the training data for the model.rat/
: Folder with images of rats for training.no_rat/
: Folder with images without rats for training.
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testing_folder/
: Contains sample images used for testing the trained model.test_image.jpeg
: A sample image for testing the model's predictions.
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model/
: Stores the trained CNN model.rat_cnn_model.h5
: The file where the trained model is saved.
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rat_detection.ipynb
: A Jupyter notebook that includes:- Code for training the CNN model.
- Evaluation of the model's performance.
- Making predictions on new images.
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Clone the repository:
git clone <repository-url>
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Navigate to the project directory:
cd Rat_Detection_CNN
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Install the required packages:
pip install -r requirements.txt
- Open the
rat_detection.ipynb
notebook in Jupyter. - Follow the instructions in the notebook to train the model and evaluate its performance.
- Use the trained model to make predictions on new images.
This project is licensed under the MIT License.