yolov5/utils/flask_rest_api
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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Auto-format by https://ultralytics.com/actions

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2024-06-08 22:29:29 +02:00
..
README.md Update banners for YOLOv8 release v8.1.0 (#12605) 2024-01-10 16:16:40 +01:00
example_request.py Update header line in Python files (#13072) 2024-06-08 22:29:29 +02:00
restapi.py Update header line in Python files (#13072) 2024-06-08 22:29:29 +02:00

README.md

Flask REST API

REST APIs are commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API created using Flask to expose the YOLOv5s model from PyTorch Hub.

Requirements

Flask is required. Install with:

$ pip install Flask

Run

After Flask installation run:

$ python3 restapi.py --port 5000

Then use curl to perform a request:

$ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s'

The model inference results are returned as a JSON response:

[
  {
    "class": 0,
    "confidence": 0.8900438547,
    "height": 0.9318675399,
    "name": "person",
    "width": 0.3264600933,
    "xcenter": 0.7438579798,
    "ycenter": 0.5207948685
  },
  {
    "class": 0,
    "confidence": 0.8440024257,
    "height": 0.7155083418,
    "name": "person",
    "width": 0.6546785235,
    "xcenter": 0.427829951,
    "ycenter": 0.6334488392
  },
  {
    "class": 27,
    "confidence": 0.3771208823,
    "height": 0.3902671337,
    "name": "tie",
    "width": 0.0696444362,
    "xcenter": 0.3675483763,
    "ycenter": 0.7991207838
  },
  {
    "class": 27,
    "confidence": 0.3527112305,
    "height": 0.1540903747,
    "name": "tie",
    "width": 0.0336618312,
    "xcenter": 0.7814827561,
    "ycenter": 0.5065554976
  }
]

An example python script to perform inference using requests is given in example_request.py