Plant Identification using Transfer Learning

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Overview

This project aims to utilize transfer learning techniques to develop a plant identification system. Transfer learning involves leveraging pre-trained models to solve related tasks, reducing the need for large datasets and computational resources. In this project, we'll build upon a pre-trained neural network model to recognize different plant species from images.

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Installation

1. Clone the Repository:

   git clone https://github.com/himanshugupta09/plant-identification.git

2. Navigate to Project Directory:

   cd plant-identification

3. Install Dependencies:

   pip install -r requirements.txt

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Usage

1. Data Preparation:

   • Gather a dataset containing images of various plant species. Ensure the dataset is labeled correctly.
   • Organize the dataset into appropriate directories (e.g., train, validation, test) for training the model.

2. Model Training:

   • Run the training script, providing the path to the dataset.

   python train.py --data_path /path/to/dataset

3. Model Evaluation:

   • Evaluate the trained model's performance on a separate test dataset.

   python evaluate.py --model_path /path/to/trained/model --test_data_path /path/to/test/dataset

4. Inference:

   • Use the trained model for inference on new images.

   python predict.py --model_path /path/to/trained/model --image_path /path/to/image

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Model Architecture

The model architecture used in this project is based on a pre-trained convolutional neural network (CNN), such as VGG16, ResNet, or Inception. We replace the top layers of the pre-trained model with custom fully connected layers and train it on our specific dataset.

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Contributing

Contributions are welcome! If you'd like to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/improvement).
3. Make your changes.
4. Commit your changes (git commit -am 'Add new feature').
5. Push to the branch (git push origin feature/improvement).
6. Create a new Pull Request.

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Acknowledgements

• Special thanks to the developers and contributors of TensorFlow, PyTorch, and other open-source libraries used in this project.
• Dataset used: [].
• Pre-trained models obtained from [].

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Contact

For any inquiries or feedback, please contact [Himanshu Gupta] at [[email protected]].

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