https://github.com/robmarkcole/yolov5-fastapi
FastAPI app exposing yolov5 object detection
https://github.com/robmarkcole/yolov5-fastapi
Last synced: about 1 year ago
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FastAPI app exposing yolov5 object detection
- Host: GitHub
- URL: https://github.com/robmarkcole/yolov5-fastapi
- Owner: robmarkcole
- License: apache-2.0
- Created: 2021-04-24T04:21:42.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2022-07-05T04:21:40.000Z (almost 4 years ago)
- Last Synced: 2024-12-24T20:02:02.814Z (over 1 year ago)
- Language: Python
- Size: 6.8 MB
- Stars: 11
- Watchers: 2
- Forks: 2
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# Yolov5 object detection with FastAPI
Credit: originally a fork of [OurNemanja/YOLOv5-fastapi-celery-redis-rabbitm](https://github.com/OurNemanja/YOLOv5-fastapi-celery-redis-rabbitmq)
Application to expose Yolov5 model using FastAPI. Inferencing requests are submitted to a Celery task queue, and an asynchronous API is available for polling for results. This approach is described in detail in the excellent article [Serving ML Models in Production with FastAPI and Celery](https://towardsdatascience.com/deploying-ml-models-in-production-with-fastapi-and-celery-7063e539a5db) by [@jonathanreadshaw](https://github.com/jonathanreadshaw/ServingMLFastCelery)
- [FastAPI](https://fastapi.tiangolo.com): python framework for building APIs
- [Celery](https://celeryproject.org): task queue with focus on real-time processing
- [RabbitMQ](https://www.rabbitmq.com): message broker used to route messages between API and the workers from Celery
- [Redis](https://redis.io): in-memory database to store results and process status from the tasks
# Usage
Run all containers:
```bash
docker-compose up
```
This will start:
- rabbitmq: message broker
- redis: in-memory database on port 6379, simply used by rabbitmq
- worker: application logic (Yolo model, FastAPI and Celery) on port 8000
Perform some API requests using the integrated Swagger UI http://localhost:8000/docs
API Services available
| Endpoint | Method | Description
| --- | --- | --- |
| http://localhost:8000/api/process | POST | Send one or more pictures to be processed by Yolov5. Return the task_id of each task.
| http://localhost:8000/api/status/ | GET | Retrieve the status of a given task
| http://localhost:8000/api/result/ | GET | Retrieve the results of a given task
| http://localhost:8000/docs | GET | Documentation generated for each endpoint
| http://localhost:15672 | GET | RabbitMQ monitor. User: guest Password: guest.
| http://localhost | GET | Simple webapp to show how to use and display results from the API.
## Development
* `python3 -m venv venv`
* `source venv/bin/activate`
* `pip install -r requirements.txt`
* `isort .`
* `black .`
* `pytest .` # none implemented
Overview of the code
- [api/app.py](api/app.py): expose the endpoints and send the request task to celery.
- [celery_tasks/tasks.py](celery_tasks/tasks.py): receive the task and send (enqueue) to workers process.
- [celery_tasks/yolo.py](celery_tasks/yolo.py): code to initialize and expose a method receive a picture and return the predictions.