myovision-app
Description
This is a sub-project of the main project myovision. It provides a Backend for the Application developed for the convenient use and further development of the myovision project. It is a RESTful API developed with the FastAPI framework and has a designated Frontend for the user interaction myovision-app-front
With a slight modification the Backend can be generalized and used for any computer vision project concerning instance segmentation.
Visualizations
-
Annotation Tool for collecting labeled data Video
-
Inference Tool for observing model's predictions and relevant metrics Video
Setup
To setup the Application backend locally follow the steps:
1. Get Model Checkpoint
- Get the model checkpoint from link
- Place it in the ./checkpoints/ directory
2. Install Dependencies
- Install python dependencies using:
pip install -r requirements.txt - Install redis: follow the simple instructions based on your OS link
3. Set Environment variables
- Copy the example .env file:
cp .env-example .env - Adjust the default variables in .env file if desired
Starting Application
-
Start Redis: follow the simple instructions based on your OS link
-
Start API:
uvicorn backend.main:app -
Start Frontend: Follow the Instructions
Finally, open the browser go to the url the frontend is running.
Using Container Images
Both Backend and Frontend contian Dockerfile to build docker images using: docker build (docs).
it can then be conveniently managed with docker-compose: docker-compose up (docs).
Contact Information
{
name = "Giorgi Nozadze",
email = "giorginozadze23@yahoo.com"
}