TinyYOLOv2Barracuda is a Unity sample project that shows how to run the YOLO object detection system on the Unity Barracuda neural network inference library.
This project uses a Tiny YOLOv2 model from ONNX Model Zoo. See the model description page for details.
- Unity 2021.3
- Barracuda 3.0.0
This repository doesn't contain the ONNX model file to avoid hitting the storage
quota. Download the model file from the ONNX Model Zoo page and put it in the
Assets/ONNX directory.
All these samples use the ImageSource class to feed images to the model. You can change the image source type from the drop down list.
VisualizerGpu runs all the object detection & visualization processes (preprocess, inference, post-process, overlap removal, and visualization) solely on GPU. It minimizes the CPU load and visualization latency, but you can't do anything more complicated than simple visualization like drawing rectangles or something on detected objects.
VisualizerCpu runs the object detection on GPU and then reads the detection results back to the CPU side. After that, it visualizes them using the Unity UI system. Even though this method runs slower than the GPU-only method, you can do complex processes using C# scripting.
Pixelizer detects people from the input video stream and applies a pixelation effect to the person regions. It shows how to implement an image effect with the YOLO detector.
