For a detailed explanation please refer to the documentation https://www.stereolabs.com/docs/yolo/
This repository contains two samples to use YOLO with the ZED in C++ using the highly optimized library TensorRT, and a Python sample that uses Pytorch and the official package of ultralytics for YOLOv8
Other sample using OpenCV DNN or YOLOv5 using the TensorRT API in C++ or Pytorch can be found in the main ZED SDK repository
In the folder tensorrt_yolov5-v6-v8_onnx you will find a sample that is able to run an ONNX model exported from YOLO architecture and using it with the ZED.
This sample is designed to run a state of the art object detection model using the highly optimized TensorRT framework. The image are taken from the ZED SDK, and the 2D box detections are then ingested into the ZED SDK to extract 3D informations (localization, 3D bounding boxes) and tracking.
This sample is using a TensorRT optimized ONNX model. It is compatible with YOLOv8, YOLOv5 and YOLOv6. It can be used with the default model trained on COCO dataset (80 classes) provided by the framework maintainers.
A custom detector can be trained with the same architecture.
In the folder pytorch_yolov8 you will find a sample that interface PyTorch ultralytics package with the ZED SDK in Python.
This sample shows how to detect custom objects using the official Pytorch implementation of YOLOv8 from a ZED camera and ingest them into the ZED SDK to extract 3D informations and tracking for each objects.