https://github.com/dheeraj8601/a-fast-and-lightweight-detection-model-for-cashew-anthracnose-in-coastal-karnataka

A fast and lightweight detection model for Cashew Anthracnose in Coastal Karnataka, with a comprehensive dataset, saved model, training notebook, front-end code, and FastAPI for predictions.
https://github.com/dheeraj8601/a-fast-and-lightweight-detection-model-for-cashew-anthracnose-in-coastal-karnataka

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A fast and lightweight detection model for Cashew Anthracnose in Coastal Karnataka, with a comprehensive dataset, saved model, training notebook, front-end code, and FastAPI for predictions.

• Host: GitHub
• URL: https://github.com/dheeraj8601/a-fast-and-lightweight-detection-model-for-cashew-anthracnose-in-coastal-karnataka
• Owner: Dheeraj8601
• Created: 2024-06-15T03:51:24.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-06-29T10:07:40.000Z (5 months ago)
• Last Synced: 2024-06-29T11:24:48.965Z (5 months ago)
• Topics: api, bootstrap, css, fastapi, html, javascript, jupyter-notebook, python
• Language: Jupyter Notebook
• Homepage:
• Size: 2.5 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# A FAST AND LIGHTWEIGHT DETECTION MODEL FOR CASHEW ANTHRACNOSE IN COASTAL KARNATAKA

## Dataset

The dataset used for training and testing the model can be found [here](https://drive.google.com/drive/folders/1fwfc_5jFW0Nf_bxNVxRfESQT5lE3Yo8x?usp=drive_link).

## Saved Model

The trained model can be downloaded from [this link](https://drive.google.com/file/d/1K5IUGquqRK_rdlvrw7VLsWOIUbnwxWFN/view?usp=drive_link).

## Model Training

The `new_bufferless.ipynb` file contains the code for training the model. Ensure you have the necessary dependencies installed before running the notebook.

## Frontend

The `frontend` folder contains the code for the front-end development. This includes all necessary files for running the web interface for the detection model.

## API

The `api` folder contains the FastAPI code, which handles CORS, model loading, and prediction APIs. Follow the instructions in the `api` folder to set up and run the FastAPI server.

## Getting Started

1. Clone the repository.

2. Download the dataset and saved model using the provided links.

3. Follow the instructions in the `new_bufferless.ipynb` file to train the model if needed.

4. Set up the front-end by navigating to the `frontend` folder and following the instructions there.

5. Set up the API by navigating to the `api` folder and following the instructions there.

## Requirements

- Python 3.x

- FastAPI

- Any additional libraries as mentioned in `new_bufferless.ipynb` and `api` folder requirements.

## Usage

1. Start the FastAPI server to handle model predictions.

2. Open the front-end application to interact with the model and make predictions on new data.

For any issues or contributions, please open an issue or submit a pull request on the GitHub repository.