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Dockerized YOLO object detection service
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README.md

YOLO Object Detection Service

Dockerized object detection service using YOLO based on AlexeyAB's darknet fork and exposed as a REST API using connexion. For details see this post.

Quick start

Pull the image from Docker Hub and spin up a container:

docker run -d --rm --name yolo_service -p 8080:8080 johannestang/yolo_service:1.0-yolov3_coco 

This will expose a single endpoint detect that accepts GET and POST requests where the former takes a URL of an image and the latter lets you upload an image for detection. The service provides a user interface at localhost:8080/ui where the endpoint can be tested and the details of the input parameters are listed.

Image variants

You can build the images yourself using the build-local.sh script or pull them from Docker Hub. They come in nine variants based on three different models/data sets and three different configurations of the darknet library. The different models are:

  1. YOLOv3 trained on the COCO dataset covering 80 classes listed here. Tag: yolov3_coco.
  2. YOLOv3 trained on the Open Images dataset covering 601 classes listed here. Tag: yolov3_openimages.
  3. YOLO9000 covering more than 9000 classes listed here. Tag: yolo90000.

The different darknet configurations:

  1. The base configuration set up to run on a CPU. Tag: 1.0
  2. Compiled using CUDA 10.0 and cudNN in order to utilize a GPU. Tag: 1.0_cuda10.0.
  3. Compiled using CUDA 10.0 and cudNN with Tensor Cores enabled in order to utilize a GPU with Tensor Cores. Tag: 1.0_cuda10.0_tc.

When using the CUDA images make sure to use Docker version 19.03 (or newer) and have NVIDIA Container Toolkit installed, then the container can be started by running e.g.:

docker run -d --rm --name yolo_service -p 8080:8080 --gpus all johannestang/yolo_service:1.0_cuda10.0-yolov3_coco 
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