Object detection using YOLO v3 in Docker

A dockerized implementation of YOLO v3 object detection running on gunicorn. The prediction code was directly inspired/modified by qqwweee/keras-yolo3.

Build command

$ docker build -t yolov3-coco .

Run command

Run with interactive shell access to container

$ docker run -it \
    -v /srv/downloads/ml-datasets/pretrained-models/yolov3-coco:/usr/src/app/yolov3-coco:ro \
    yolov3-coco bash

Run to test the app in action. We needed to increase the memory limit using -m 2g for the object detection model to work.

$ docker run \
    -v /srv/downloads/ml-datasets/pretrained-models/yolov3-coco:/usr/src/app/yolov3-coco:ro \
    -p 10080:10080 \
    -m 4g yolov3-coco

Testing the prediction

A sample php script uploader.php is given which will send a POST request with an image to http://localhost:10080 where the docker service is running.

The output from the service will be a json encoded string looks like this:

{
    "status": "success", 
    "time_taken": 3.6899283000000196, 
    "msg": "", 
    "detections": [
        {
            "label": "chair", 
            "score": 0.582422137260437, 
            "left": 74, 
            "top": 602, 
            "right": 421, 
            "bottom": 904
        }, {
            "label": "person", 
            "score": 0.9987449645996094, 
            "left": 98, 
            "top": 101, 
            "right": 1025, 
            "bottom": 935
        }
    ]
}