usls

Library Listed

A Rust library integrated with ONNXRuntime, providing a collection of ML models.

Jobs: Run and serve models, Images and vision
Category: Computer vision

Checked 9 Nov 2026 · Sources

Downloads: 355
Dependents: 1
Stars: 447
Forks: 46
Contributors: 14
Commits: 190
Latest release: 0.1.11 · 9 months ago

About

From the project README · 6 Nov 2026

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).

(Generated by Seedream4.5)

🌟 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:

Read the full README

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

CPU

cargo run -r --example yolo -- --task detect --ver 26 --scale n --dtype fp16

Nvidia CUDA + CUDA Image Processor

cargo run -r -F cuda --example yolo -- --task segment --ver 11 --scale m --device cuda:0 --processor-device cuda:0

Nvidia TensorRT + CUDA Image Processor

cargo run -r -F tensorrt-full --example yolo -- --device tensorrt:0 --processor-device cuda:0

Nvidia TensorRT-RTX + CUDA Image Processor

cargo run -r -F nvrtx-full --example yolo -- --device nvrtx:0 --processor-device cuda:0

Apple Silicon CoreML

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

Intel OpenVINO (CPU/GPU/VPU)

cargo run -r -F openvino -F ort-load-dynamic --example yolo -- --device openvino:CPU

📊 Performance Benchmarks

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 Processor	DType	Batch	Preprocess	Inference	Postprocess	Total
n	TensorRT	CUDA	FP16	1	~233µs	~1.3ms	~14µs	~1.55ms
n	TensorRT-RTX	CUDA	FP32	1	~233µs	~2.0ms	~10µs	~2.24ms
n	TensorRT-RTX	CUDA	FP16	1	❓	❓	❓	❓
n	CUDA	CUDA	FP32	1	~233µs	~5.0ms	~17µs	~5.25ms
n	CUDA	CUDA	FP16	1	~233µs	~3.6ms	~17µs	~3.85ms
n	CUDA	CPU	FP32	1	~800µs	~6.5ms	~14µs	~7.31ms
n	CUDA	CPU	FP16	1	~800µs	~5.0ms	~14µs	~5.81ms
n	CPU	CPU	FP32	1	~970µs	~20.5ms	~14µs	~21.48ms
n	CPU	CPU	FP16	1	~970µs	~25.0ms	~14µs	~25.98ms
n	TensorRT	CUDA	FP16	8	~1.2ms	~6.0ms	~55µs	~7.26ms
n	TensorRT	CPU	FP16	8	~18.0ms	~25.5ms	~55µs	~43.56ms
m	TensorRT	CUDA	FP16	1	~233µs	~3.6ms	~14µs	~3.85ms
m	TensorRT	CUDA	Int8	1	~233µs	~2.6ms	~14µs	~2.84ms
m	CUDA	CUDA	FP32	1	~233µs	~16.1ms	~17µs	~16.35ms
m	CUDA	CUDA	FP16	1	~233µs	~8.8ms	~17µs	~9.05ms

🔍 What's Next?

📦 Model Zoo

[!NOTE]
Status: ✅ Supported | ❓ Unknown | ❌ Not Supported For Now

🔍 All ONNX models are available from the ONNX Models Repository

🔥 YOLO-Series

Model	Task / Description	Demo	Dynamic Batch	TensorRT	FP32	FP16	Q8	Q4f16	BNB4
YOLOv5	Image Classification Object Detection Instance Segmentation	demo	✅	✅	✅	✅	✅	❌	❌
YOLOv6	Object Detection	demo	✅	✅	✅	✅	✅	❌	❌
YOLOv7	Object Detection	demo	✅	✅	✅	✅	✅	❌	❌
YOLOv8	Object Detection Instance Segmentation Image Classification Oriented Object Detection Keypoint Detection	demo	✅	✅	✅	✅	✅	❌	❌
YOLO11	Object Detection Instance Segmentation Image Classification Oriented Object Detection Keypoint Detection	demo	✅	✅	✅	✅	✅	❌	❌
YOLOv9	Object Detection	demo	✅	✅	✅	✅	✅	❌	❌
YOLOv10	Object Detection	demo	✅	✅	✅	✅	✅	❌	❌
YOLOv12	Image Classification Object Detection Instance Segmentation	demo	✅	✅	✅	✅	✅	✅	✅
YOLOv13	Object Detection	demo	✅	✅	✅	✅	✅	✅	✅
YOLO26	Object Detection Instance Segmentation Image Classification Oriented Object Detection Keypoint Detection	demo	✅	✅	✅	✅	✅	✅	✅

🏷️ Image Classification & Tagging

Model	Task / Description	Demo	Dynamic Batch	TensorRT	FP32	FP16	Q8	Q4f16	BNB4
BEiT	Image Classification	demo	✅	✅	✅	✅	❌	❌	❌
ConvNeXt	Image Classification	demo	✅	✅	✅	✅	❌	❌	❌
FastViT	Image Classification	demo	✅	✅	✅	✅	❌	❌	❌
MobileOne	Image Classification	demo	✅	✅	✅	✅	❌	❌	❌
DeiT	Image Classification	demo	✅	✅	✅	✅	❌	❌	❌
RAM	Image Tagging	demo	✅	❓	✅	✅	✅	✅	✅
RAM++	Image Tagging	demo	✅	❓	✅	✅	✅	✅	✅

🎯 Object Detection

Model	Task / Description	Demo	Dynamic Batch	TensorRT	FP32	FP16	Q8	Q4f16	BNB4
RT-DETRv1	Object Detection	demo	✅	✅	✅	✅	✅	✅	✅
RT-DETRv2	Object Detection	demo	✅	✅	✅	✅	✅	✅	✅
RT-DETRv4	Object Detection	demo	✅	✅	✅	✅	✅	✅	✅
RF-DETR	Object Detection	demo	✅	✅	✅	✅	✅	✅	✅
PP-PicoDet	Object Detection	demo	❌	❓	✅	❌	❌	❌	❌
D-FINE	Object Detection	demo	✅	❓	✅	❌	❌	❌	❌
DEIM	Object Detection	demo	✅	❓	✅	❌	❌	❌	❌
DEIMv2	Object Detection	demo	✅	❓	✅	✅	✅	✅	✅

🎨 Image Segmentation

Model	Task / Description	Demo	Dynamic Batch	TensorRT	FP32	FP16	Q8	Q4f16	BNB4
SAM	Segment Anything	demo	✅	❓	✅	❌	❌	❌	❌
SAM-HQ	Segment Anything	demo	✅	❓	✅	❌	❌	❌	❌
MobileSAM	Segment Anything	demo	✅	❓	✅	❌	❌	❌	❌
EdgeSAM	Segment Anything	demo	✅	❓	✅	❌	❌	❌	❌
YOLOE-v8/11-Prompt-Free	Open-Set Detection And Segmentation	demo	✅	✅	✅	✅	✅	✅	✅
YOLOE-26-Prompt-Free	Open-Set Detection And Segmentation	demo	✅	✅	✅	✅	✅	✅	✅
FastSAM	Instance Segmentation	demo	✅	✅	✅	✅	✅	✅	✅
EdgeCrafter (ECDetSeg)	Instance Segmentation	demo	❓	✅	✅	✅	❌	❌	❌
SAM2	Segment Anything	demo	✅	❓	✅	❌	❌	❌	❌
SAM3-Tracker	Segment Anything	demo	✅	✅	✅	✅	✅	✅	✅
BiRefNet - COD	Camouflaged Object Detection	demo	✅	❓	✅	✅	✅	✅	✅
BiRefNet - DIS	Dichotomous Image Segmentation	demo	✅	❓	✅	✅	✅	✅	✅
BiRefNet - HRSOD	High-Resolution Salient Object Detection	demo	✅	❓	✅	✅	✅	✅	✅
BiRefNet - Massive	Multi-Dataset Robust Segmentation	demo	✅	❓	✅	✅	✅	✅	✅

✨ Background Removal

Model	Task / Description	Demo	Dynamic Batch	TensorRT	FP32	FP16	Q8	Q4f16	BNB4
RMBG	Image Segmentation Background Removal	demo	✅	❓	✅	✅	✅	✅	✅
BEN2	Image Segmentation Background Removal	demo	✅	❓	✅	✅	❌	❌	❌

👀 Gaze Estimation

Model	Task / Description	Demo	Dynamic Batch	TensorRT	FP32	FP16	Q8	Q4f16	BNB4
MobileGaze	Eye Gaze Estimation	demo	✅	❓	✅	✅	✅	✅	✅

✂️ Image Matting & Portrait Segmentation

Model	Task / Description	Demo	Dynamic Batch	TensorRT	FP32	FP16	Q8	Q4f16	BNB4
MODNet	Image Matting	demo	✅	❓	✅	✅	✅	❌	❌
MediaPipe Selfie	Image Segmentation	demo	✅	❓	✅	✅	✅	❌	❌
BiRefNet - Portrait	Portrait Background Removal	demo	✅	❓	✅	✅	✅	✅	✅
BiRefNet - Matting	Portrait Matting & Background Removal	demo	✅	❓	✅	✅	✅	✅	✅
BiRefNet - HR Matting	High-Resolution Portrait Matting	demo	✅	❓	✅	✅	✅	✅	✅
BiRefNet - General	General Purpose Segmentation	demo	✅	❓	✅	✅	✅	✅	✅
BiRefNet - HR General	High-Resolution General Segmentation	demo	✅	❓	✅	✅	✅	✅	✅
BiRefNet - Lite General	Lightweight General Segmentation (2K)	demo	✅	❓	✅	✅	✅	✅	✅
BiRefNet - General Tiny	Lightweight General Segmentation with Swin-V1-Tiny	demo	✅	❓	✅	✅	✅	✅	✅

🗺️ Open-Set Detection & Segmentation

Model	Task / Description	Demo	Dynamic Batch	TensorRT	FP32	FP16	Q8	Q4f16	BNB4
GroundingDINO	Open-Set Detection With Language	demo	✅	❓	✅	✅	✅	✅	✅
MM-GDINO	Open-Set Detection With Language	demo	✅	❓	✅	✅	✅	✅	✅
LLMDet	Open-Set Detection With Language	demo	✅	❓	✅	✅	✅	✅	✅
OWLv2	Open-Set Object Detection	demo	✅	❓	✅	✅	❌	❌	❌
YOLO-World	Open-Set Detection With Language	demo	✅	✅	✅	✅	✅	✅	✅
YOLOE-Prompt-Based	Open-Set Detection And Segmentation	demo	✅	✅	✅	✅	✅	✅	✅
YOLOE-26-Prompt-Based	Open-Set Detection And Segmentation	demo	✅	✅	✅	✅	✅	✅	✅
SAM3-Image	Open-Set Detection And Segmentation	demo	✅	✅	✅	✅	✅	✅	✅

🏃 Multi-Object Tracking

Model	Task / Description	Demo	Dynamic Batch	TensorRT	FP32	FP16	Q8	Q4f16	BNB4
ByteTrack	Multi-Object Tracking	demo	❌	❌	❌	❌	❌	❌	❌

💎 Image Super-Resolution

Model	Task / Description	Demo	Dynamic Batch	TensorRT	FP32	FP16	Q8	Q4f16	BNB4
Swin2SR	Image Restoration	demo	✅	❓	✅	✅	✅	✅	✅
APISR	Anime Super-Resolution	demo	✅	❓	✅	✅	✅	✅	✅

🤸 Pose Estimation

Model	Task / Description	Demo	Dynamic Batch	TensorRT	FP32	FP16	Q8	Q4f16	BNB4
RTMPose	Keypoint Detection	demo	✅	❓	✅	✅	✅	✅	✅
DWPose	Keypoint Detection	demo	✅	❓	✅	✅	✅	✅	✅
RTMW	Keypoint Detection	demo	✅	❓	✅	✅	✅	✅	✅
RTMO	Keypoint Detection	demo	✅	❓	✅	✅	✅	✅	❌
ECPose	Keypoint Detection	demo	✅	❓	✅	✅	❌	❌	❌

🔍 OCR & Document Understanding

Model	Task / Description	Demo	Dynamic Batch	TensorRT	FP32	FP16	Q8	Q4f16	BNB4
DB (PaddleOCR det v4 / v5)	Text Detection	demo	✅	❓	✅	✅	❌	❌	❌
FAST	Text Detection	demo	✅	❓	✅	✅	❌	❌	❌
LinkNet	Text Detection	demo	✅	❓	✅	✅	❌	❌	❌
SVTR (PaddleOCR rec v4 / v5)	Text Recognition	demo	✅	❓	✅	✅	❌	❌	❌
TrOCR	Text Recognition	demo	✅	❓	✅	✅	❌	❌	❌
SLANet (PaddleOCR tab v4 / v5)	Table Recognition	demo	✅	❓	✅	✅	❌	❌	❌
DocLayout-YOLO	Object Detection	demo	✅	❓	✅	✅	✅	❌	❌
PP-DocLayout-v1-Plus-L	Object Detection	demo	✅	✅	✅	✅	✅	❌	❌
PP-DocLayout-v2	Object Detection	demo	✅	❓	✅	✅	✅	✅	✅
PP-DocLayout-v3	Object Detection	demo	✅	✅	✅	✅	✅	✅	✅

🧩 Vision-Language Models (VLM)

Model	Task / Description	Demo	Dynamic Batch	TensorRT	FP32	FP16	Q8	Q4f16	BNB4
BLIP	Image Captioning	demo	✅	❓	✅	❓	❌	❌	❌
Florence2	A Variety of Vision Tasks	demo	✅	❓	✅	✅	❌	❌	❌
Moondream2	Open-Set Object Detection Open-Set Keypoints Detection Image Captioning Visual Question Answering	demo	✅	❓	❌	❌	✅	✅	❌
SmolVLM	Visual Question Answering	demo	✅	❓	✅	❓	❓	❓	❓
SmolVLM2	Visual Question Answering	demo	✅	❓	✅	❓	❓	❓	❓
FastVLM	Vision Language Models	demo	✅	❓	✅	✅	✅	✅	✅

🧬 Embedding Model

Model	Task / Description	Demo	Dynamic Batch	TensorRT	FP32	FP16	Q8	Q4f16	BNB4
CLIP	Vision-Language Embedding	demo	✅	❓	✅	✅	✅	✅	✅
jina-clip-v1	Vision-Language Embedding	demo	✅	❓	✅	✅	✅	✅	✅
jina-clip-v2	Vision-Language Embedding	demo	✅	❓	✅	✅	✅	✅	✅
mobileclip	Vision-Language Embedding	demo	✅	❓	✅	✅	✅	✅	✅
SigLIP	Vision-Language Embedding	demo	✅	❓	✅	✅	✅	✅	✅
SigLIPv2	Vision-Language Embedding	demo	✅	❓	✅	✅	✅	✅	✅
DINOv2	Vision Embedding	demo	✅	❓	✅	❌	❌	❌	❌
DINOv3	Vision Embedding	demo	✅	❓	✅	✅	✅	✅	✅

📐 Depth Estimation

Model	Task / Description	Demo	Dynamic Batch	TensorRT	FP32	FP16	Q8	Q4f16	BNB4
DepthAnything v1	Monocular Depth Estimation	demo	✅	❓	✅	✅	✅	✅	✅
DepthAnything v2	Monocular Depth Estimation	demo	✅	❓	✅	✅	✅	✅	✅
DepthPro	Monocular Depth Estimation	demo	✅	❓	✅	✅	✅	✅	✅
Depth-Anything-3	Monocular Metric Multi-View	demo	✅	❓	✅	✅	✅	✅	✅

🌌 Others

Model	Task / Description	Demo	Dynamic Batch	TensorRT	FP32	FP16	Q8	Q4f16	BNB4
Sapiens	Foundation for Human Vision Models	demo	✅	❓	✅	✅	✅	✅	✅
YOLOPv2	Panoptic Driving	demo	✅	❓	✅	❌	❌	❌	❌

🤝 Contributing

This is a personal project maintained in spare time, so progress on performance optimization and new model support may vary.

We highly welcome PRs for model optimization! If you have expertise in specific models and can help optimize their interfaces or post-processing, your contributions would be invaluable. Feel free to open an issue or submit a pull request for suggestions, bug reports, or new features.

🙏 Acknowledgments

This project is built on top of ort (ONNX Runtime for Rust), which provides seamless Rust bindings for ONNX Runtime. Special thanks to the ort maintainers.
Special thanks to @kadu-v for the jamtrack-rs project, which inspired our ByteTracker implementation

Thanks to all the open-source libraries and their maintainers that make this project possible. See Cargo.toml for a complete list of dependencies.

📜 License

This project is licensed under LICENSE.

Compatibility

How each fact is measured

Runs on: CUDA, ROCm

What shows it

CUDA: usls feature cuda

ROCm: usls feature rocm
Native code: Optional

Binds native code: ffmpeg-sys-next (feature video), ort-sys (feature acl)
Python bindings: No Python binding crate found
License: MIT

Compare with similar projects

Order: strongest tier first, then most downloads, then name, with unmeasured values last. A library counts downloads over 30 days. An app counts binary downloads per release. An app with none counts its crate downloads over 30 days. ML Rust runs this catalogue, so its closest project always gets a row.

usls beside the projects that share a topic with it
Project	Downloads	Dependents	Stars	Latest release	Runs on (GPU)	Native code	License
usls This page Listed A Rust library integrated with ONNXRuntime, providing a collection of ML models.	355 downloads over 30 days	1	447	0.1.115 Feb 2026	CUDA, ROCm	Binds native code: ffmpeg-sys-next (feature video), ort-sys (feature acl)	MIT
opencv-rust Featured Rust bindings for OpenCV Also in Images and vision.	262,425 downloads over 30 days	96	2,500	0.101.19 Oct 2026	GPU support unknown	Build uses a native toolchain: cc, pkg-config, vcpkg	MIT
oar-ocr Listed An Optical Character Recognition (OCR) and Document Layout Analysis library written in Rust. Also in Run and serve models.	34,387 downloads over 30 days	9	204	0.10.226 Oct 2026	CUDA, WebGPU, Metal	Binds native code: ort-sys	Apache-2.0
inference Listed Ultralytics YOLO inference library and CLI for Rust Also in Run and serve models.	2,609 downloads over 30 days	1	217	0.0.563 Nov 2026	CUDA, WebGPU, ROCm	Binds native code: ort-sys, ffmpeg-sys-next (feature video), openvino-sys (feature intel)	AGPL-3.0
kornia-rs Listed Low-level computer vision library in Rust Also in Images and vision.	2,294 downloads over 30 days	6	751	0.1.512 Oct 2026	CUDA	Binds native code: glib-sys (feature gstreamer), gobject-sys (feature gstreamer), gstreamer-app-sys (feature gstreamer), gstreamer-base-sys (feature gstreamer), gstreamer-sys (feature gstreamer) and 1 more	Apache-2.0
blazr App Listed Inference server for LLMs, speech and embeddings: OpenAI-compatible routes with streaming, plus speech synthesis (TTS) and transcription (ASR). Serves Llama, Mistral, Qwen, Mamba2, MLA+MoE and hybrid models from SafeTensors, AWQ, GPTQ and GGUF. Also in Run and serve models.	8 binary downloads per release	Not applicable	7	0.7.16 Nov 2026	CUDA, WebGPU	Binds native code: ring (feature distributed)	Apache-2.0
image-ssd App Listed Dormant A Rust library for object detection via SSD MobileNet. Also in Images and vision.	Not measured	Not applicable	5	Not measured	Not measured	Not measured	MIT

Compare these

All projects in Run and serve models All projects in Images and vision

Why ListedNo archive, New or Featured rule places it, so it is Listed.

What it lacks for Featured

It has 1 active dependents and 1 new dependents in 365 days. Featured needs 11 active dependents or 9 new dependents in 365 days. The repository route reads 447 stars, 46 forks and 14 contributors. It needs 4,539 stars, 434 forks and 79 contributors together.

What else the check found

The last activity is on 30 Jul 2026, 102 days ago. The rules expect an update within 450 days. Dormancy rule
The latest release requires the deprecated dependency paste. RUSTSEC-2024-0436 Rot signals rule

Exact rules: Listed, Featured library. The check on 9 Nov 2026 applied thresholds version 9.

Every rule outcome, as the check records it

expected-interval: version 0.1.11 base 450: E = 450 days
dormancy: last activity 2026-07-30, 102 days ago, E = 450: active
rot: deprecated requirement `paste (RUSTSEC-2024-0436, https://rustsec.org/advisories/RUSTSEC-2024-0436.html)`
featured: not featured: active_users 1 < 11 and new_adopters 1 < 9 and repository route stars 447 (needs 4539), forks 46 (needs 434), contributors 14 (needs 79)
listed: listed: no archive, new or featured rule applies

Measurements and sources

Each signal row shows the raw value, its source and the fetch date. The Sources list gathers every distinct source on this page. Movement needs more history.

Sources6 sources

Description: the crates.io description
crates.io database dump, fetched 8 Nov 2026
RustSec advisory database, fetched 8 Nov 2026
GitHub repository, fetched 9 Nov 2026
crates.io package, fetched 6 Nov 2026
GitHub contributors, fetched 6 Nov 2026

Adoption1 active dependents, 1 new dependents in 365 days1 not measured

Adoption measurements
Signal	Value	Source	Fetched	Counts
Active dependents	1	crates.io database dump	8 Nov 2026	Yes
New dependents (365 days)	1	crates.io database dump	8 Nov 2026	Yes
Peak active dependents	1	crates.io database dump	8 Nov 2026	Yes
Active dependents as a share of the peak	1	crates.io database dump	8 Nov 2026	Yes
Binary downloads per release	Not measured			Yes

MaintenanceLatest human commit: 3 months ago3 not measured

Maintenance measurements
Signal	Value	Source	Fetched	Counts
Latest human commit	2026-07-30	GitHub repository	9 Nov 2026	Yes
Latest release	2026-02-05	crates.io database dump	8 Nov 2026	Yes
First release	2024-03-30	crates.io database dump	8 Nov 2026	Yes
Latest version	0.1.11	crates.io database dump	8 Nov 2026	Yes
Days the oldest outside issue waits for a reply	136	GitHub repository	9 Nov 2026	Yes
Maintainer replies over 365 days	77	GitHub repository	9 Nov 2026	Yes
Repository archived	false	GitHub repository	9 Nov 2026	Yes
Repository gone	false	GitHub repository	9 Nov 2026	Yes
Release commit or manifest in the repo	true	crates.io package	6 Nov 2026	Yes
Published docs	true	crates.io database dump	8 Nov 2026	Yes
Latest release fails cargo check	Not measured			Yes
Build check date	Not measured			Yes
Build check toolchain	Not measured			Yes

SecurityUnpatched vulnerabilities: none

Security measurements
Signal	Value	Source	Fetched	Counts
Unpatched vulnerabilities past the grace window	none	RustSec advisory database	8 Nov 2026	Yes
RustSec unmaintained advisory	none	RustSec advisory database	8 Nov 2026	Yes
Deprecation notice	none	crates.io database dump	8 Nov 2026	Yes
Deprecated requirement	paste (RUSTSEC-2024-0436, https://rustsec.org/advisories/RUSTSEC-2024-0436.html)	crates.io database dump	8 Nov 2026	Yes

Distribution35 releases, 355 downloads over 30 days5 not measured

Distribution measurements
Signal	Value	Source	Fetched	Counts
Releases	35	crates.io database dump	8 Nov 2026	Yes
Downloads over 30 days	355	crates.io database dump	8 Nov 2026	Yes
Binary downloads over the last 3 releases	Not measured			Yes
Homebrew installs on request over 365 days	Not measured			Yes
Repology repository families	Not measured			Yes
Docker pull growth over 90 days	Not measured			Yes
Docker pulls at the growth baseline	Not measured			Yes
Every version a prerelease	false	crates.io database dump	8 Nov 2026	Yes
Every version 0.0.x	false	crates.io database dump	8 Nov 2026	Yes
Every version yanked	false	crates.io database dump	8 Nov 2026	Yes
Crate deleted from crates.io	false	crates.io database dump	8 Nov 2026	Yes

Repository447 stars, 46 forks, 14 contributors, 190 commits. Stars alone never qualify a project.

Repository measurements
Signal	Value	Source	Fetched	Counts
GitHub stars	447	GitHub repository	9 Nov 2026	Yes — one part of the repository route
GitHub forks	46	GitHub repository	9 Nov 2026	Yes — one part of the repository route
Contributors with a GitHub account	14	GitHub contributors	6 Nov 2026	Yes — one part of the repository route
Commits on the default branch	190	GitHub repository	9 Nov 2026	No
Repository's main crate left out of the entry	none	crates.io database dump	8 Nov 2026	Yes

Facts that never count

Facts that never count measurements
Signal	Value	Source	Fetched	Counts
License	MIT	crates.io database dump	8 Nov 2026	No
Runs on, from listed crates and their direct dependencies	CUDA, ROCm	crates.io database dump	8 Nov 2026	No
What shows each backend	CUDA: usls feature cuda; ROCm: usls feature rocm	crates.io database dump	8 Nov 2026	No
Native code, from listed crates and their dependencies	Binds native code: ffmpeg-sys-next (feature video), ort-sys (feature acl)	crates.io database dump	8 Nov 2026	No
Python bindings, from direct dependencies of listed crates	No Python binding crate found	crates.io database dump	8 Nov 2026	No
Crates that depend on a listed crate directly	1	crates.io database dump	8 Nov 2026	No

Corrections and review history

Report a correction

No correction applies to this entry.

Judgments

A judgment enters only through an approved issue and a merged pull request.

No judgment applies to this entry.