An end to end model building, training and analytics dashboard with a focus on model explainability on the german trafic sign dataset.
React-Flask Based ML Web Application
GradCam Technique for identifying mislabelled hotspots
TSNE plots to visualize and evaluate model performance
- Create a complex Dataset
- Train additional images on the fly
- View model performances across different metrics
- Visualize model performance
- Get suggestions to various shortcomings in model training
- An explainable AI-based solution to comprehend network failures
- Git.
- Node & npm (version 12 or greater).
- A fork of the repo.
- Python3 environment to install flask
The following is a high-level overview of relevant files and folders.
backend/
├── backend/
│ ├── template/
│ └── app.py
└── frontend/
├── public/
│ ├── index.html
│ └── ...
├── images/
│ └── logo.png
├── src/
│ ├── assets/
│ │ ├── css
│ │ └── fonts...
│ ├── components/
│ │ ├── Sidebar
│ │ └── Navbars
│ └── views/
├── routes.js
├── package.json
├── local_vm.sh
└── .gitignore
- Clone this repo to your local machine using
https://github.com/kousikr26/ml-dashboard/
In order to install all packages follow the steps below:
- Download the static folder from this drive: https://drive.google.com/file/d/149fh2lq7fT35RQVP5rmTgUfcYPorE9kX/view
- Put it in the
backend/
- Move to backend folder
- For installing virtual environment -
python3 -m pip install --user virtualenv
- Create A Virtual env -
python3 -m venv env
- Activate virtual env -
source env/bin/activate
pip3 install -r requirements.txt
flask run
- Move to frontend folder
npm install
npm start
The model will be served on http://127.0.0.1:5000/
This project is licensed under the Apache License, Version 2.0.