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🐭 rat detection using cnn this beginner-level computer vision project uses a convolutional neural network (cnn) to classify images as containing a rat or not. built in jupyter notebook with tensorflow and keras, it uses a pre-trained dataset and allows you to test custom images by placing them in a testing_folder.

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Rat Detection CNN

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.

Project Structure

  • 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.
  • testing_folder/: Contains sample images used for testing the trained model.

    • test_image.jpeg: A sample image for testing the model's predictions.
  • model/: Stores the trained CNN model.

    • rat_cnn_model.h5: The file where the trained model is saved.
  • 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.

Setup Instructions

  1. Clone the repository:

    git clone <repository-url>
    
  2. Navigate to the project directory:

    cd Rat_Detection_CNN
    
  3. Install the required packages:

    pip install -r requirements.txt
    

Usage

  1. Open the rat_detection.ipynb notebook in Jupyter.
  2. Follow the instructions in the notebook to train the model and evaluate its performance.
  3. Use the trained model to make predictions on new images.

License

This project is licensed under the MIT License.

About

🐭 rat detection using cnn this beginner-level computer vision project uses a convolutional neural network (cnn) to classify images as containing a rat or not. built in jupyter notebook with tensorflow and keras, it uses a pre-trained dataset and allows you to test custom images by placing them in a testing_folder.

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