yolov3-object-detection.md

Object Detection

This service uses YOLOv3 to perform object detection on images.

It is part of our third party DNN Model Services.

Welcome

The service receives an image and uses it as an input for a pre-trained YOLOv3 model.

The model can detect objects (80 classes) from COCO Dataset.

What’s the point?

The service makes prediction using computer vision and machine learning techniques.

The service outputs an image with a bounding box for each object that it has predicted (and its confidence).

How does it work?

The user must provide the following inputs in order to start the service and get a response:

Inputs:

• model: DNN Model ("yolov3").
• img_path: An image URL.
• confidence: Confidence of object detection (between 0 and 1).

You can use this service from SingularityNET DApp, clicking on SNET/ObjectDetection.

You can also call the service from SingularityNET CLI (snet).

Assuming that you have an open channel to this service:

$ snet client call snet yolov3-object-detection default_group detect '{"model": "yolov3", "img_path": "https://hips.hearstapps.com/amv-prod-cad-assets.s3.amazonaws.com/images/media/51/2017-10best-lead-photo-672628-s-original.jpg", "confidence": "0.5"}'
...
Read call params from cmdline...

Calling service...

    response:
        boxes: '[[8.5, 151.0, 223, 118], [294.0, 138.0, 78, 48], [127.0, 185.5, 250, 209],
            [605.0, 152.5, 224, 115], [432.0, 129.5, 86, 55], [205.5, 129.0, 81, 38],
            [18.5, 127.0, 127, 40], [439.5, 187.5, 299, 225], [525.0, 132.0, 88, 34],
            [694.5, 126.0, 115, 40]]'
        class_ids: '[2, 2, 2, 2, 2, 2, 2, 2, 2, 2]'
        confidences: '[0.998349130153656, 0.9982008337974548, 0.9977825284004211, 0.995550811290741,
            0.9875208735466003, 0.980316698551178, 0.9753901362419128, 0.969804048538208,
            0.9632347226142883, 0.9579626321792603]'
        delta_time: '2.0124'
        img_base64: ... (BASE64_BBOX_IMAGE)

What to expect from this service?

Input Image:

with:

• model: yolov3
• confidence: 0.1

Response: