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# 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](https://github.com/jamjamjon/usls/tree/main) 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
<details>
<summary>You have two options to link the ONNXRuntime library:</summary>
- **Option 1: Manual Linking**
- For detailed setup, consult the [ONNX Runtime linking documentation](https://ort.pyke.io/setup/linking).
- **Linux or macOS**:
1. Download the ONNX Runtime package from the [Releases page](https://github.com/microsoft/onnxruntime/releases).
2. Set up the library path by exporting the `ORT_DYLIB_PATH` environment variable:
```shell
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:
```shell
cargo run -r --example yolo --features auto
```
</details>
### 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](https://github.com/zmwangx/rust-ffmpeg/wiki/Notes-on-building#dependencies)
## Get Started
```Shell
# 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 # YOLO11
# 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 # YOLO11
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 # YOLO11-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 # YOLO11-Obb
```
**`cargo run -- --help` for more options**
For more details, please refer to [usls-yolo](https://github.com/jamjamjon/usls/tree/main/examples/yolo).
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