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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
yolort is a runtime stack for yolov5 on specialized accelerators such as tensorrt, libtorch, onnxruntime, tvm and ncnn.
| Date | Stars |
|---|---|
| 2026-07-24 | 730 |
| 2026-07-25 | 730 |
| 2026-07-28 | 730 |
| 2026-07-30 | 729 |
| 2026-08-06 | 729 |
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<div align="center"> <img src="docs/source/_static/yolort_logo.png" width="400px"> **YOLOv5 Runtime Stack** ______________________________________________________________________ [Documentation](https://zhiqwang.com/yolort/) • [Installation Instructions](https://zhiqwang.com/yolort/installation.html) • [Deployment](#-deployment) • [Contributing](.github/CONTRIBUTING.md) • [Reporting Issues](https://github.com/zhiqwang/yolort/issues/new?assignees=&labels=&template=bug-report.yml) ______________________________________________________________________ [](https://pypi.org/project/yolort/) [](https://badge.fury.io/py/yolort) [](https://pepy.tech/project/yolort) [](https://github.com/zhiqwang/yolort/releases) [](https://join.slack.com/t/yolort/shared_invite/zt-mqwc7235-940aAh8IaKYeWclrJx10SA) [](.github/CONTRIBUTING.md) [](https://github.com/zhiqwang/yolort/actions/workflows/ci-test.yml) [](https://github.com/zhiqwang/yolort/tree/gh-pages) [](https://results.pre-commit.ci/latest/github/zhiqwang/yolort/main) [](https://codecov.io/gh/zhiqwang/yolort) ______________________________________________________________________ </div> ## 🤗 Introduction **What it is.** Yet another implementation of Ultralytics's [YOLOv5](https://github.com/ultralytics/yolov5). yolort aims to make the training and inference of the object detection task integrate more seamlessly together. yolort now adopts the same model structure as the official YOLOv5. The significant difference is that we adopt the dynamic shape mechanism, and within this, we can embed both pre-processing (letterbox) and post-processing (nms) into the model graph, which simplifies the deployment strategy. In this sense, yolort makes it possible to deploy the object detection more easily and friendly on `LibTorch`, `ONNX Runtime`, `TVM`, `TensorRT` and so on. **About the code.** Follow the design principle of [detr](https://github.com/facebookresearch/detr): > object detection should not be more difficult than classification, and should not require complex libraries for training and inference. `yolort` is very simple to implement and experiment with. Do you like the implementation of torchvision's faster-rcnn, retinanet or detr? Do you like yolov5? You'll love `yolort`! <a href="notebooks/assets/zidane.jpg"><img src="notebooks/assets/zidane.jpg" alt="YOLO inference demo" width="500"/></a> ## 🆕 What's New - *Dec. 27, 2021*. Add `TensorRT` C++ interface example. Thanks to [Shiquan](https://github.com/ShiquanYu). - *Dec. 25, 2021*. Support exporting to `TensorRT`, and inferencing with `TensorRT` Python interface. - *Sep. 24, 2021*. Add `ONNX Runtime` C++ interface example. Thanks to [Fidan](https://github.com/itsnine). - *Feb. 5, 2021*. Add `TVM` compile and inference notebooks. - *Nov. 21, 2020*. Add graph visualization tools. - *Nov. 17, 2020*. Support exporting to `ONNX`, and inferencing with `ONNX Runtime` Python interface. - *Nov. 16, 2020*. Refacto
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f11cfdce69b262f2, topic:inference, topic:tensorrt, topic:onnx
