tasks.md

How To Guide

MLCoach is a browser-based framework to train and test deep-learning based approaches for image classification purposes.

Data Format

Currently, MLCoach supports directory based label definition, similar to the following example:

data_directory
│
└─── label1
│   │   image001.jpeg
│   │   image002.jpeg
│   │   ...
│   
└───label2
    │   image001.jpeg
    │   image001.jpeg

The supported image formats are: TIFF, TIF, JPG, JPEG, and PNG.

Additionally, this application supports NPZ files (in development feature).

Training

To train a model, please follow the following steps:

1. Choose your dataset.
   1. As a standalone application: Click on "Open File Manager", and upload your dataset as a ZIP file OR choose your data_directory from the table (click on "Browse", choose a row from the table directory, and click "Import").
   2. From Label Maker: The dataset you uploaded in Label Maker should be visible in MLCoach by default at start-up. When selecting a different dataset in Label Maker after start-up, you can refresh the dataset in MLCoach by clicking "Refresh Images".
2. Choose "Model Training" in Actions.
3. Modify the training parameters as needed.
4. Click Execute.
5. Choose the computing resources that should be used for this task. Please note that these should not exceed the constraints defined in the computing API. Recommended values: CPU - 4 and GPU - 0. Click "Submit".
6. The training job has been successfully submitted! You can check the progress of this job in the "List of Jobs", where you can select the corresponding row to display the loss plot in real-time. Additionally, you can check the logs and parameters of each job by clicking on it's corresponding cells.

Testing

To test a mode, please follow the following steps:

1. Choose your dataset.
   1. As a standalone application: Click on "Open File Manager", and upload your dataset as a ZIP file OR choose your data_directory from the table (click on "Browse", choose a row from the table directory, and click "Import").
   2. From Label Maker: The dataset you uploaded in Label Maker should be visible in MLCoach by default at start-up. When selecting a different dataset in Label Maker after start-up, you can refresh the dataset in MLCoach by clicking "Refresh Images".
2. Choose "Test Prediction using Model" in Actions.
3. Modify the testing parameters as needed.
4. Choose a trained model from the "List of Jobs".
5. Click Execute.
6. Choose the computing resources that should be used for this task. Please note that these should not exceed the constraints defined in the computing API. Recommended values: CPU - 4 and GPU - 0. Click "Submit".
7. The testing job has been successfully submitted! You can check the progress of this job in the "List of Jobs", where you can select the corresponding row to display the classification results in real-time. Additionally, you can check the logs and parameters of each job by clicking on it's corresponding cells.