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A Rust library integrated with ONNXRuntime, providing a collection of Computer Vison and Vision-Language models such as YOLO, FastVLM, and more.
| Date | Stars |
|---|---|
| 2026-07-24 | 432 |
| 2026-07-25 | 432 |
| 2026-07-28 | 432 |
| 2026-07-30 | 432 |
| 2026-08-06 | 436 |
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<h2 align="center">usls</h2>
<p align="center">
<a href="https://github.com/jamjamjon/usls/actions/workflows/rust-ci.yml">
<img src="https://github.com/jamjamjon/usls/actions/workflows/rust-ci.yml/badge.svg" alt="Rust CI">
</a>
<a href='https://crates.io/crates/usls'>
<img src='https://img.shields.io/crates/v/usls?logo=rust&logoColor=white' alt='Crates.io Version'>
</a>
<a href='https://github.com/microsoft/onnxruntime/releases'>
<img src='https://img.shields.io/badge/onnxruntime-%3E%3D%201.22.0-3399FF?logo=onnx&logoColor=white' alt='ONNXRuntime MSRV'>
</a>
<a href='https://crates.io/crates/usls'>
<img src='https://img.shields.io/crates/msrv/usls?color=yellow&logo=rust' alt='Rust MSRV'>
</a>
</p>
<br/>
**usls** is a cross-platform Rust library powered by ONNX Runtime for efficient inference of SOTA vision and vision-language models (***typically under 1B parameters***).
<p align="center">
<img src='https://github.com/jamjamjon/assets/releases/download/images/pipeline.png'>
(Generated by Seedream4.5)
</p>
## 🌟 Highlights
- **⚡ High Performance**: Multi-threading, SIMD, and CUDA-accelerated processing
- **✨ Cross-Platform**: Linux, macOS, Windows with ONNX Runtime execution providers (CUDA, TensorRT, CoreML, OpenVINO, DirectML, etc.)
- **🎯 Precision Support**: FP32, FP16, INT8, UINT8, Q4, Q4F16, BNB4, and more
- **🛠️ Full-Stack Suite**: `DataLoader`, `Annotator`, and `Viewer` for complete workflows
- **🏗️ Unified API**: Single `Model` trait inference with `run()`/`forward()`/`encode_images()`/`encode_texts()` and unified `Y` output
- **📥 Auto-Management**: Automatic model download (HuggingFace/GitHub), caching and path resolution
- **📦 Multiple Inputs**: Image, directory, video, webcam, stream and combinations
- **🌱 Model Ecosystem**: **50+** SOTA vision and VLM models
## 🚀 Quick Start
Run the **YOLO-Series demo** to explore models with different tasks, precision and execution providers:
- **Tasks**: `detect`, `segment`, `pose`, `classify`, `obb`
- **Versions**: `v5`, `v6`, `v7`, `v8`, `v9`, `v10`, `11`, `12`, `v13`, `26`
- **Scales**: `n`, `s`, `m`, `l`, `x`
- **Precision**: `fp32`, `fp16`, `q8`, `int8`, `q4`, `q4f16`, `bnb4`, and more
- **Execution Providers**: `CPU`, `CUDA`, `TensorRT`, `TensorRT-RTX`, `CoreML`, `OpenVINO`, and more
<details open>
<summary><strong>CPU</strong></summary>
```bash
cargo run -r --example yolo -- --task detect --ver 26 --scale n --dtype fp16
```
</details>
<details>
<summary><strong>Nvidia CUDA + CUDA Image Processor</strong></summary>
```bash
cargo run -r -F cuda --example yolo -- --task segment --ver 11 --scale m --device cuda:0 --processor-device cuda:0
```
</details>
<details>
<summary><strong>Nvidia TensorRT + CUDA Image Processor</strong></summary>
```bash
cargo run -r -F tensorrt-full --example yolo -- --device tensorrt:0 --processor-device cuda:0
```
</details>
<details>
<summary><strong>Nvidia TensorRT-RTX + CUDA Image Processor</strong></summary>
```bash
cargo run -r -F nvrtx-full --example yolo -- --device nvrtx:0 --processor-device cuda:0
```
</details>
<details>
<summary><strong>Apple Silicon CoreML</strong></summary>
```bash
cargo run -r -F coreml --example yolo -- --device coreml
```
</details>
<details>
<summary><strong>Intel OpenVINO (CPU/GPU/VPU)</strong></summary>
```bash
cargo run -r -F openvino -F ort-load-dynamic --example yolo -- --device openvino:CPU
```
</details>
<details>
<summary><strong>📊 Performance Benchmarks</strong></summary>
>**Environment:** NVIDIA RTX 3060Ti (TensorRT-10.11.0.33, CUDA 12.8, TensorRT-RTX-1.3.0.35) / Intel i5-12400F
>
>**Setup:** YOLO26 Detection, COCO2017-val (5,000 images), 640x640, Conf thresholds: [0.35, 0.3, ..]
>
> ***Results are for rough reference only.***
| Scale | EP | Image<br>Processor | DType | Batch | Preprocess | Inference | Postprocess | Total |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| n | TensorRT | CUDA | FP16Excerpt of 26,022 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9224a99ae695bfd1, topic:tensorrt, topic:onnx, topic:onnxruntime