# usls
A Rust library integrated with **ONNXRuntime**, providing a collection of **Computer Vison** and **Vision-Language** models including [YOLOv8](https://github.com/ultralytics/ultralytics) `(Classification, Segmentation, Detection and Pose Detection)`, [YOLOv9](https://github.com/WongKinYiu/yolov9), [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), and others. Many execution providers are supported, sunch as `CUDA`, `TensorRT` and `CoreML`.
## Supported Models
| Model | Example | CUDA(f32) | CUDA(f16) | TensorRT(f32) | TensorRT(f16) |
| :-------------------: | :----------------------: | :----------------: | :----------------: | :------------------------: | :-----------------------: |
| YOLOv8-detection | [demo](examples/yolov8) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8-pose | [demo](examples/yolov8) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8-classification | [demo](examples/yolov8) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8-segmentation | [demo](examples/yolov8) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8-OBB | ***TODO*** | ***TODO*** | ***TODO*** | ***TODO*** | ***TODO*** |
| YOLOv9 | [demo](examples/yolov9) | ✅ | ✅ | ✅ | ✅ |
| RT-DETR | [demo](examples/rtdetr) | ✅ | ✅ | ✅ | ✅ |
| FastSAM | [demo](examples/fastsam) | ✅ | ✅ | ✅ | ✅ |
| YOLO-World | [demo](examples/yolo-world) | ✅ | ✅ | ✅ | ✅ |
| DINOv2 | [demo](examples/dinov2) | ✅ | ✅ | ✅ | ✅ |
| CLIP | [demo](examples/clip) | ✅ | ✅ | ✅ visual<br />❌ textual | ✅ visual<br />❌ textual |
| BLIP | [demo](examples/blip) | ✅ | ✅ | ✅ visual<br />❌ textual | ✅ visual<br />❌ textual |
| OCR(DB, SVTR) | ***TODO*** | ***TODO*** | ***TODO*** | ***TODO*** | ***TODO*** |
## Solution Models
Additionally, this repo also provides some solution models such as pedestrian `fall detection`, `head detection`, `trash detection`, and more.
| Model | Example |
| :---------------------: | :------------------------------: |
| face-landmark detection | [demo](examples/yolov8-face) |
| head detection | [demo](examples/yolov8-head) |
| fall detection | [demo](examples/yolov8-falldown) |
| trash detection | [demo](examples/yolov8-plastic-bag) |
## Demo
```
cargo run -r --example yolov8 # fastsam, yolov9, blip, clip, dinov2, yolo-world...
```
## Integrate into your own project
#### 1. Install [ort](https://github.com/pykeio/ort)
check **[ort guide](https://ort.pyke.io/setup/linking)**
<details close>
<summary>For Linux or MacOS users</summary>
- Firstly, download from latest release 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>
#### 2. Add `usls` as a dependency to your project's `Cargo.toml`
```shell
cargo add --git https://github.com/jamjamjon/usls
# or
cargo add usls
```
#### 3. Set `Options` and build model
```Rust
let options = Options::default()
.with_model("../models/yolov8m-seg-dyn-f16.onnx")
.with_trt(0) // using cuda(0) by default
// when model with dynamic shapes
.with_i00((1, 2, 4).into()) // dynamic batch
.with_i02((416, 640, 800).into()) // dynamic height
.with_i03((416, 640, 800).into()) // dynamic width
.with_confs(&[0.4, 0.15]) // person: 0.4, others: 0.15
.with_dry_run(3)
.with_saveout("YOLOv8"); // save results
let mut model = YOLO::new(&options)?;
```
#### 4. Prepare inputs, and then you're ready to go
- 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 = DataLoader::try_read("./assets/bus.jpg")?;
let _y = model.run(&[x])?;
```
## Script: converte ONNX model from `float32` to `float16`
```python
import onnx
from pathlib import Path
from onnxconverter_common import float16
model_f32 = "onnx_model.onnx"
model_f16 = float16.convert_float_to_float16(onnx.load(model_f32))
saveout = Path(model_f32).with_name(Path(model_f32).stem + "-f16.onnx")
onnx.save(model_f16, saveout)
```