usls

usls is a Rust library integrated with ONNXRuntime, offering a suite of advanced models for Computer Vision and Vision-Language tasks, including:

• YOLO Models: YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9, YOLOv10, YOLO11
• SAM Models: SAM, SAM2, MobileSAM, EdgeSAM, SAM-HQ, FastSAM
• Vision Models: RT-DETR, RTMO, Depth-Anything, DINOv2, MODNet, Sapiens, DepthPro, FastViT, BEiT, MobileOne
• Vision-Language Models: CLIP, jina-clip-v1, BLIP, GroundingDINO, YOLO-World, Florence2
• OCR Models: FAST, DB(PaddleOCR-Det), SVTR(PaddleOCR-Rec), SLANet, TrOCR, DocLayout-YOLO

👉 More Supported Models

Model	Task / Description	Example	CoreML	CUDA FP32	CUDA FP16	TensorRT FP32	TensorRT FP16
BEiT	Image Classification	demo	✅	✅	✅
ConvNeXt	Image Classification	demo	✅	✅	✅
FastViT	Image Classification	demo	✅	✅	✅
MobileOne	Image Classification	demo	✅	✅	✅
DeiT	Image Classification	demo	✅	✅	✅
DINOv2	Vision Embedding	demo	✅	✅	✅	✅	✅
YOLOv5	Image Classification Object Detection Instance Segmentation	demo	✅	✅	✅	✅	✅
YOLOv6	Object Detection	demo	✅	✅	✅	✅	✅
YOLOv7	Object Detection	demo	✅	✅	✅	✅	✅
YOLOv8 YOLO11	Object Detection Instance Segmentation Image Classification Oriented Object Detection Keypoint Detection	demo	✅	✅	✅	✅	✅
YOLOv9	Object Detection	demo	✅	✅	✅	✅	✅
YOLOv10	Object Detection	demo	✅	✅	✅	✅	✅
RT-DETR	Object Detection	demo	✅	✅	✅
PP-PicoDet	Object Detection	demo	✅	✅	✅
DocLayout-YOLO	Object Detection	demo	✅	✅	✅
D-FINE	Object Detection	demo	✅	✅	✅
DEIM	Object Detection	demo	✅	✅	✅
RTMO	Keypoint Detection	demo	✅	✅	✅	❌	❌
SAM	Segment Anything	demo	✅	✅	✅
SAM2	Segment Anything	demo	✅	✅	✅
MobileSAM	Segment Anything	demo	✅	✅	✅
EdgeSAM	Segment Anything	demo	✅	✅	✅
SAM-HQ	Segment Anything	demo	✅	✅	✅
FastSAM	Instance Segmentation	demo	✅	✅	✅	✅	✅
YOLO-World	Open-Set Detection With Language	demo	✅	✅	✅	✅	✅
GroundingDINO	Open-Set Detection With Language	demo	✅	✅	✅
CLIP	Vision-Language Embedding	demo	✅	✅	✅	❌	❌
jina-clip-v1	Vision-Language Embedding	demo	✅	✅	✅	❌	❌
BLIP	Image Captioning	demo	✅	✅	✅	❌	❌
DB(PaddleOCR-Det)	Text Detection	demo	✅	✅	✅	✅	✅
FAST	Text Detection	demo	✅	✅	✅	✅	✅
LinkNet	Text Detection	demo	✅	✅	✅	✅	✅
SVTR(PaddleOCR-Rec)	Text Recognition	demo	✅	✅	✅	✅	✅
SLANet	Tabel Recognition	demo	✅	✅	✅
TrOCR	Text Recognition	demo	✅	✅	✅
YOLOPv2	Panoptic Driving Perception	demo	✅	✅	✅	✅	✅
DepthAnything v1 DepthAnything v2	Monocular Depth Estimation	demo	✅	✅	✅	❌	❌
DepthPro	Monocular Depth Estimation	demo	✅	✅	✅
MODNet	Image Matting	demo	✅	✅	✅	✅	✅
Sapiens	Foundation for Human Vision Models	demo	✅	✅	✅
Florence2	a Variety of Vision Tasks	demo	✅	✅	✅

⛳️ Cargo Features

By default, none of the following features are enabled. You can enable them as needed:

• auto: Automatically downloads prebuilt ONNXRuntime binaries from Pyke’s CDN for supported platforms.

  • If disabled, you'll need to compile ONNXRuntime from source or download a precompiled package, and then link it manually.

    👉 For Linux or macOS Users

    • Download from the Releases page.
    • Set up the library path by exporting the ORT_DYLIB_PATH environment variable:

      export ORT_DYLIB_PATH=/path/to/onnxruntime/lib/libonnxruntime.so.1.20.1

• ffmpeg: Adds support for video streams, real-time frame visualization, and video export.

  • Powered by video-rs and minifb. For any issues related to ffmpeg features, please refer to the issues of these two crates.

• cuda: Enables the NVIDIA TensorRT provider.

• trt: Enables the NVIDIA TensorRT provider.

• mps: Enables the Apple CoreML provider.

🎈 Example

• Using CUDA

  cargo run -r -F cuda --example yolo -- --device cuda:0
  

• Using Apple CoreML

  cargo run -r -F mps --example yolo -- --device mps
  

• Using TensorRT

  cargo run -r -F trt --example yolo -- --device trt
  

• Using CPU

  cargo run -r --example yolo
  

All examples are located in the examples directory.

🥂 Integrate Into Your Own Project

Add usls as a dependency to your project's Cargo.toml

cargo add usls -F cuda

Or use a specific commit:

[dependencies]
usls = { git = "https://github.com/jamjamjon/usls", rev = "commit-sha" }

🥳 If you find this helpful, please give it a star ⭐

📌 License

This project is licensed under LICENSE.