mysterio

A platform for training, labeling, deploying and retraining image classification model.

Approach & Solution

-The trained model classifies images based on the dataset provided.

-The UI can run inference using the model trained above on 128 unseen and unlabeled images uploaded at the same time.

-Once inference is completed, the UI is then able to visualize these images and their predictions including the confidence score and provide other metrics as appropriate.

-The UI should have the functionality to change the labels of images that are wrong, add them to a database and run training again.

-The UI have an option to change the parameters of training. Parameters could be learning rate, number of filters, filter size etc.

-The newly trained model is available for use by the UI to run another round of inference.

Project Structure

main.py :
    embedding the machine learning model to flask
    routing functions
templates/ :
    frontend files
static/ :
    media files
model/ :
    pre-trained pickle file

Dependencies

Flask
PyTorch
Matplotlib

Installation & Execution Guide

Clone the repository :

git clone https://github.com/deyRupak/mysterio.git

Install the dependencies :

pip install <dependency_name>

Change the directory :

cd mysterio

Run main.py as:

py main.py
or
python main.py



- expected output
>py main.py
 * Serving Flask app "main" (lazy loading)
 * Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
 * Debug mode: on
 * Restarting with stat
 * Debugger is active!
 * Debugger PIN: 177-932-044
 * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)

Type in your browser [ will take you to the index page ]:

localhost:5000/