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Object detection using a model zoo model

Object detection is a computer vision technique for locating instances of objects in images or videos.

In this example, you learn how to implement inference code with a ModelZoo model to detect dogs in an image.

The source code can be found at

You can also use the Jupyter notebook tutorial. The Jupyter notebook explains the key concepts in detail.

Setup guide

To configure your development environment, follow setup.

Run object detection example

Input image file

You can find the image used in this example in the project test resource folder: src/test/resources/dog_bike_car.jpg


Build the project and run

Use the following command to run the project:

cd examples
./gradlew run

Your output should look like the following:

[INFO ] - Detected objects image has been saved in: build/output/detected-dog_bike_car.png
[INFO ] - [
    {"class": "car", "probability": 0.99991, "bounds": {"x"=0.611, "y"=0.137, "width"=0.293, "height"=0.160}}
    {"class": "bicycle", "probability": 0.95385, "bounds": {"x"=0.162, "y"=0.207, "width"=0.594, "height"=0.588}}
    {"class": "dog", "probability": 0.93752, "bounds": {"x"=0.168, "y"=0.350, "width"=0.274, "height"=0.593}}

An output image with bounding box will be saved as build/output/detected-dog_bike_car.png:


Run object detection example with other engines

For objection detection application, other than the default model zoo with the default engine, we can also run it with other engines and model zoo. Here, we demonstrate with a pre-trained YOLOV5s ONNX model.

The model can be easily loaded with the following criteria

Criteria<Image, DetectedObjects> criteria =
                .setTypes(Image.class, DetectedObjects.class)
                .optProgress(new ProgressBar())

where the optFilter is removed and optEngine is specified. The rest would be the same.