# usls
[](https://crates.io/crates/usls)  [](https://docs.rs/usls) [](https://github.com/jamjamjon/usls)
A Rust library integrated with **ONNXRuntime**, providing a collection of **Computer Vison** and **Vision-Language** models including [YOLOv5](https://github.com/ultralytics/yolov5), [YOLOv8](https://github.com/ultralytics/ultralytics), [YOLOv9](https://github.com/WongKinYiu/yolov9), [YOLOv10](https://github.com/THU-MIG/yolov10), [RTDETR](https://arxiv.org/abs/2304.08069), [CLIP](https://github.com/openai/CLIP), [DINOv2](https://github.com/facebookresearch/dinov2), [FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM), [YOLO-World](https://github.com/AILab-CVC/YOLO-World), [BLIP](https://arxiv.org/abs/2201.12086), [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR), [Depth-Anything](https://github.com/LiheYoung/Depth-Anything), [MODNet](https://github.com/ZHKKKe/MODNet) and others.
| Monocular Depth Estimation |
| :--------------------------------------------------------------: |
| <img src='examples/depth-anything/demo.png' width="800px"> |
| Panoptic Driving Perception | Text-Detection-Recognition |
| :----------------------------------------------------: | :------------------------------------------------: |
| <img src='examples/yolop/demo.png' width="385px"> | <img src='examples/db/demo.png' width="385x"> |
| Portrait Matting |
| :------------------------------------------------------: |
| <img src='examples/modnet/demo.png' width="800px"> |
## Supported Models
| Model | Task / Type | Example | CUDA<br />f32 | CUDA<br />f16 | TensorRT<br />f32 | TensorRT<br />f16 |
| :---------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------: | :--------------------------: | :-----------: | :-----------: | :------------------------: | :-----------------------: |
| [YOLOv5](https://github.com/ultralytics/yolov5) | Classification<br />Object Detection<br />Instance Segmentation | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
| [YOLOv6](https://github.com/meituan/YOLOv6) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
| [YOLOv7](https://github.com/WongKinYiu/yolov7) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
| [YOLOv8](https://github.com/ultralytics/ultralytics) | Object Detection<br />Instance Segmentation<br />Classification<br />Oriented Object Detection<br />Keypoint Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
| [YOLOv9](https://github.com/WongKinYiu/yolov9) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
| [YOLOv10](https://github.com/THU-MIG/yolov10) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
| [RTDETR](https://arxiv.org/abs/2304.08069) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
| [FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) | Instance Segmentation | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
| [YOLO-World](https://github.com/AILab-CVC/YOLO-World) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
| [DINOv2](https://github.com/facebookresearch/dinov2) | Vision-Self-Supervised | [demo](examples/dinov2) | ✅ | ✅ | ✅ | ✅ |
| [CLIP](https://github.com/openai/CLIP) | Vision-Language | [demo](examples/clip) | ✅ | ✅ | ✅ visual<br />❌ textual | ✅ visual<br />❌ textual |
| [BLIP](https://github.com/salesforce/BLIP) | Vision-Language | [demo](examples/blip) | ✅ | ✅ | ✅ visual<br />❌ textual | ✅ visual<br />❌ textual |
| [DB](https://arxiv.org/abs/1911.08947) | Text Detection | [demo](examples/db) | ✅ | ✅ | ✅ | ✅ |
| [SVTR](https://arxiv.org/abs/2205.00159) | Text Recognition | [demo](examples/svtr) | ✅ | ✅ | ✅ | ✅ |
| [RTMO](https://github.com/open-mmlab/mmpose/tree/main/projects/rtmo) | Keypoint Detection | [demo](examples/rtmo) | ✅ | ✅ | ❌ | ❌ |
| [YOLOPv2](https://arxiv.org/abs/2208.11434) | Panoptic Driving Perception | [demo](examples/yolop) | ✅ | ✅ | ✅ | ✅ |
| [Depth-Anything<br />(v1, v2)](https://github.com/LiheYoung/Depth-Anything) | Monocular Depth Estimation | [demo](examples/depth-anything) | ✅ | ✅ | ❌ | ❌ |
| [MODNet](https://github.com/ZHKKKe/MODNet) | Image Matting | [demo](examples/modnet) | ✅ | ✅ | ✅ | ✅ |
## Installation
Refer to [ort docs](https://ort.pyke.io/setup/linking)
<details close>
<summary>For Linux or MacOS users</summary>
- Download from [ONNXRuntime Releases](https://github.com/microsoft/onnxruntime/releases)
- Then linking
```Shell
export ORT_DYLIB_PATH=/Users/qweasd/Desktop/onnxruntime-osx-arm64-1.17.1/lib/libonnxruntime.1.17.1.dylib
```
</details>
## Quick Start
```Shell
cargo run -r --example yolo # blip, clip, yolop, svtr, db, ...
```
## Integrate into your own project
### 1. Add `usls` as a dependency to your project's `Cargo.toml`
```Shell
cargo add usls
```
Or you can use specific commit
```Shell
usls = { git = "https://github.com/jamjamjon/usls", rev = "???sha???"}
```
### 2. Build model
```Rust
let options = Options::default()
.with_yolo_version(YOLOVersion::V5) // YOLOVersion: V5, V6, V7, V8, V9, V10, RTDETR
.with_yolo_task(YOLOTask::Classify) // YOLOTask: Classify, Detect, Pose, Segment, Obb
.with_model("xxxx.onnx")?;
let mut model = YOLO::new(options)?;
```
- If you want to run your model with TensorRT or CoreML
```Rust
let options = Options::default()
.with_trt(0) // using cuda by default
// .with_coreml(0)
```
- If your model has dynamic shapes
```Rust
let options = Options::default()
.with_i00((1, 2, 4).into()) // dynamic batch
.with_i02((416, 640, 800).into()) // dynamic height
.with_i03((416, 640, 800).into()) // dynamic width
```
- If you want to set a confidence for each category
```Rust
let options = Options::default()
.with_confs(&[0.4, 0.15]) // class_0: 0.4, others: 0.15
```
- Go check [Options](src/core/options.rs) for more model options.
#### 3. Load images
- Build `DataLoader` to load images
```Rust
let dl = DataLoader::default()
.with_batch(model.batch.opt as usize)
.load("./assets/")?;
for (xs, _paths) in dl {
let _y = model.run(&xs)?;
}
```
- Or simply read one image
```Rust
let x = vec![DataLoader::try_read("./assets/bus.jpg")?];
let y = model.run(&x)?;
```
#### 4. Annotate and save
```Rust
let annotator = Annotator::default().with_saveout("YOLO");
annotator.annotate(&x, &y);
```
#### 5. Get results
The inference outputs of provided models will be saved to `Vec<Y>`.
- You can get detection bboxes with `y.bboxes()`:
```Rust
let ys = model.run(&xs)?;
for y in ys {
// bboxes
if let Some(bboxes) = y.bboxes() {
for bbox in bboxes {
println!(
"Bbox: {}, {}, {}, {}, {}, {}",
bbox.xmin(),
bbox.ymin(),
bbox.xmax(),
bbox.ymax(),
bbox.confidence(),
bbox.id(),
)
}
}
}
```
- Other: [Docs](https://docs.rs/usls/latest/usls/struct.Y.html)