TensorFlow.js Static Webapp - Mobilenet Finetuned

This is a simple example of how to finetune a Mobilenet model and use it in a web application using Tensorflow.js. The web application will allow you to upload a picture and classify it using the finetuned model.

Demo

You can try the web application here (Hosted on github-pages).

Installation

First, clone the repository and install the required packages:

git clone https://github.com/abinthomasonline/tfjs-static-webapp.git
cd tfjs-static-webapp
pip install -r requirements.txt

The code is tested with Python 3.11.7 in OSX.

Kaggle Authentication for Dataset

To download the dataset, you need to have a Kaggle account and a Kaggle API key. You can create a new API key from your Kaggle account settings. Once you have the API key, create a new file kaggle.json in the secrets directory of the project and add the API key to the file as shown below:

{
  "username": "your-kaggle-username",
  "key": "your-kaggle-api-key"
}

Finetune Mobilenet

To finetune the Mobilenet model, run the main.ipynb notebook. The notebook will download the dataset, finetune the model, save the model in the runs directory, and convert the model to a Tensorflow.js model.

Run the Web Application

To run the web application, run the following command:

cd webapp
python -m http.server

Open your browser and go to http://localhost:8000 to use the web application.

License

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