MYIA

this is a nocode platform for training, testing, evaluating, and building image classifier models

Table of Contents

Features

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Installation

  1. Clone the repository.

        git clone https://github.com/bethropolis/myia.git

  2. Install the dependencies by running the following command:

    pip install -r requirements.txt

  3. Setup project:

    python setup.py

  4. create a virtual environment (optional):

    python -m venv myenv

    activate the virtual environment

    # windows
myenv\Scripts\activate

# linux / mac
source myenv/bin/activate

  5. run the app:

     python app.py

    open your browser and go to http://localhost:5000

Usage

  1. Upload images to the app

    • upload images to the training directory
    • head to http://localhost:5000/directory?path=training/train and upload your images

  2. label the images

    • Open the train page (http://localhost:5000/train)

    • label the images either as good or bad by clicking the thumbs up or thumbs down button.

    • the app can only generate binary classification models so you can only label the images as good or bad

      • thumbs up for good which could represent classification A

      • thumbs down for bad which could represent classification B

    currently the app only supports two labels good and bad

  3. build the model

    • To build the model, head to the home page (http://localhost:5000/) and click the build model button

    • In the next page you will have to input: No of epochs - the number of times the model will be trained (default is 15) No of layers - the number of layers the model will have (default is 3) Model name - the name of the model (default is myia_image_classifier)

    • click the build model button to start building the model

    The model will be saved in the model/image_model directory as a keras model

    Note: The higher the number of epochs the longer it will take to build the model

  4. test the model - To test the model, open the test page (http://localhost:5000/test) and upload an image to test the model with or test with images in the test directory (http://localhost:5000/directory?path=training/test)

  5. evaluate the model

    • To evaluate the model, open the evaluate page (http://localhost:5000/evaluate) and upload an image to evaluate the model with or evaluate with images in the evaluate directory (http://localhost:5000/directory?path=model/evaluation)

    • The evaluation results will be saved in the model directory as a json file and a graph will be generated and saved in the static directory as a png file

Screenshots

Image Description
A screenshot of the Home page A screenshot of the Home page
A screenshot of the Training page A screenshot of the Training page
A screenshot of the Testing page A screenshot of the Testing page
A screenshot of the Evaluation page A screenshot of the Evaluation page
A screenshot of a directory A screenshot of a directory
A screenshot of a directory A screenshot of a directory
A screenshot of the page for building a model A screenshot of the page for building a model

packages used

Contributing

Feel free to ping me a pull requests if you want to contribute.

License

This project is licensed under the MIT License.

happy coding 💜

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