An object detection web app using TF(inference), React(frontend) and Flask(backend) that runs inference on camera feed. Based on this Medium article).
This app has been tested only for local deployment so far. It works but takes around 3s per detection on my system.
v0.1 notes:
- Originally, decided to try the tutorial since they had a working web app running on Heroku, which is what my end goal was. However, couldn't get it to work locally just from the post. Weirdly, it did work only after adding a
GET
request check - if anyone can explain that to me, would be nice 😓
Still need to learn how react and flask communicate, I think there was something wrong
with the urls/requests, but no clue how to debug...
Would be a good idea to create a virtual environment. I used conda
for this containing Python 3.9 and Tensorflow 2.6.
The backend is handled by Python and the requirements are in backend
folder
cd backend
pip install -r requirments.txt
Node handles frontend stuff and the requirements are in frontend
folder
cd frontend
npm install
To run locally
cd frontend
npm run start:server-dev
The model used is a Mobilenetv2 SSD trained on the COCO2017 dataset.