ultralytics/examples/YOLO-Series-ONNXRuntime-Rust/README.md

YOLO-Series ONNXRuntime Rust Demo for Core YOLO Tasks

This repository provides a Rust demo for key YOLO-Series tasks such as Classification, Segmentation, Detection, Pose Detection, and OBB using ONNXRuntime. It supports various YOLO models (v5 - 11) across multiple vision tasks.

Introduction

• This example leverages the latest versions of both ONNXRuntime and YOLO models.
• We utilize the usls crate to streamline YOLO model inference, providing efficient data loading, visualization, and optimized inference performance.

Features

• Extensive Model Compatibility: Supports YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9, YOLOv10, YOLO11, YOLO-world, RTDETR, and others, covering a wide range of YOLO versions.
• Versatile Task Coverage: Includes Classification, Segmentation, Detection, Pose, and OBB.
• Precision Flexibility: Works with FP16 and FP32 ONNX models.
• Execution Providers: Accelerated support for CPU, CUDA, CoreML, and TensorRT.
• Dynamic Input Shapes: Dynamically adjusts to variable batch, width, and height dimensions for flexible model input.
• Flexible Data Loading: The DataLoader handles images, folders, videos, and video streams.
• Real-Time Display and Video Export: Viewer provides real-time frame visualization and video export functions, similar to OpenCV’s imshow() and imwrite().
• Enhanced Annotation and Visualization: The Annotator facilitates comprehensive result rendering, with support for bounding boxes (HBB), oriented bounding boxes (OBB), polygons, masks, keypoints, and text labels.

Setup Instructions

1. ONNXRuntime Linking

You have two options to link the ONNXRuntime library:

• Option 1: Manual Linking

  • For detailed setup, consult the ONNX Runtime linking documentation.
  • Linux or macOS:
    1. Download the ONNX Runtime package from the Releases page.
    2. Set up the library path by exporting the ORT_DYLIB_PATH environment variable:

       export ORT_DYLIB_PATH=/path/to/onnxruntime/lib/libonnxruntime.so.1.19.0
       

• Option 2: Automatic Download

  • Use the --features auto flag to handle downloading automatically:

    cargo run -r --example yolo --features auto
    

2. [Optional] Install CUDA, CuDNN, and TensorRT

• The CUDA execution provider requires CUDA version 12.x.
• The TensorRT execution provider requires both CUDA 12.x and TensorRT 10.x.

3. [Optional] Install ffmpeg

To view video frames and save video inferences, install rust-ffmpeg. For instructions, see:https://github.com/zmwangx/rust-ffmpeg/wiki/Notes-on-building#dependencies

Get Started

# customized
cargo run -r -- --task detect --ver v8 --nc 6 --model xxx.onnx  # YOLOv8

# Classify
cargo run -r -- --task classify --ver v5 --scale s --width 224 --height 224 --nc 1000  # YOLOv5
cargo run -r -- --task classify --ver v8 --scale n --width 224 --height 224 --nc 1000  # YOLOv8
cargo run -r -- --task classify --ver v11 --scale n --width 224 --height 224 --nc 1000  # YOLOv11

# Detect
cargo run -r -- --task detect --ver v5 --scale n  # YOLOv5
cargo run -r -- --task detect --ver v6 --scale n  # YOLOv6
cargo run -r -- --task detect --ver v7 --scale t  # YOLOv7
cargo run -r -- --task detect --ver v8 --scale n  # YOLOv8
cargo run -r -- --task detect --ver v9 --scale t  # YOLOv9
cargo run -r -- --task detect --ver v10 --scale n  # YOLOv10
cargo run -r -- --task detect --ver v11 --scale n  # YOLOv11
cargo run -r -- --task detect --ver rtdetr --scale l  # RTDETR

# Pose
cargo run -r -- --task pose --ver v8 --scale n   # YOLOv8-Pose
cargo run -r -- --task pose --ver v11 --scale n  # YOLOv11-Pose

# Segment
cargo run -r -- --task segment --ver v5 --scale n  # YOLOv5-Segment
cargo run -r -- --task segment --ver v8 --scale n  # YOLOv8-Segment
cargo run -r -- --task segment --ver v11 --scale n  # YOLOv8-Segment
cargo run -r -- --task segment --ver v8 --model yolo/FastSAM-s-dyn-f16.onnx  # FastSAM

# OBB
cargo run -r -- --ver v8 --task obb --scale n --width 1024 --height 1024 --source images/dota.png  # YOLOv8-Obb
cargo run -r -- --ver v11 --task obb --scale n --width 1024 --height 1024 --source images/dota.png  # YOLOv11-Obb

cargo run -- --help for more options

For more details, please refer to usls-yolo.