This is a demo FastAPI app that allows a user to upload image(s), perform inference using a pretrained YOLOv5 model, and receive results in JSON format. This repo also includes Jinja2 HTML templates, so you can access this interface through a web browser at localhost:8000

Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7 (per https://github.com/ultralytics/yolov5).

To install run:

pip install -r requirements.txt

You can initialize the server with python server.py or uvicorn server:app --reload

You can test the server a couple of ways:

1. Using client.py - this is a basic example of using the Requests library to upload a batch of images + model name to localhost:8000/detect/ and receive JSON inference results.
2. Open localhost:8000 in your web browser, use the web form to upload image(s) and select a model, then click submit. You should see inference results displayed in the web browser shortly.
3. Open localhost:8000/docs in your web browser, clicking on "POST /detect/ Detect Via API" and then click the "Try It Out" button. You should be able to upload files and enter a YOLOv5 model here as well.

Models will automatically be downloaded the first time they are used and are cached on disc.

Full Swagger API endpoint documentation is auto-generated in localhost:8000/docs. The general idea is that humans use the "/" route (HTML form + inference results displayed in the browser) and programs use the "/detect/" API route to receive JSON inference results.

See the fastapi_yolov5_minimal_client_server_example folder for a minimal client/server wrapper of YOLOv5 with FastAPI and HTML forms.

Again, you can try this out by:

1. Running the server with python server_minimal.py or uvicorn server_minimal:app --reload
2. Test the server with python client_minimal.py, navigating to localhost:8000 in your web browser or localhost:8000/docs -> POST Request -> Try It Out

This repository is a wrapper around YOLOv5 from Ultralytics: https://github.com/ultralytics/yolov5

Also grabbed some code/ideas from: https://gist.github.com/decent-engineer-decent-datascientist/81e04ad86e102eb083416e28150aa2a1