• Features
• Installation
• Usage
• Screenshots
• packages used
• Contributing
• License

• model training
• model testing
• model evaluation
• file explorer
• bulk image upload
• screenshot script (go)
• image crawler/scrapper (go)
• graph generation
• dark/light mode

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

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

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

• Flask
• Pillow
• Tensorflow
• Keras
• Numpy
• matplotlib
• chromedp
• goquery

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

This project is licensed under the MIT License.

happy coding 💜