obss · ZephyrKeXiner · Sep 26, 2025 · Sep 26, 2025 · Sep 26, 2025 · Sep 27, 2025
diff --git a/README.md b/README.md
@@ -34,6 +34,12 @@
 
 SAHI helps developers overcome real-world challenges in object detection by enabling **sliced inference** for detecting small objects in large images. It supports various popular detection models and provides easy-to-use APIs.
 
+<div align="center">
+
+🌐 [English](README.md) | 🇨🇳 [简体中文](docs/zh/README.md)
+
+</div>
+
 | Command  | Description  |
 |---|---|
 | [predict](https://github.com/obss/sahi/blob/main/docs/cli.md#predict-command-usage)  | perform sliced/standard video/image prediction using any [ultralytics](https://github.com/ultralytics/ultralytics)/[mmdet](https://github.com/open-mmlab/mmdetection)/[huggingface](https://huggingface.co/models?pipeline_tag=object-detection&sort=downloads)/[torchvision](https://pytorch.org/vision/stable/models.html#object-detection) model - see [CLI guide](docs/cli.md#predict-command-usage) |

diff --git a/docs/coco.md b/docs/coco.md
@@ -221,7 +221,7 @@ save_json(area_filtered_coco.json, "area_filtered_coco.json")
 
 <details closed>
 <summary>
-<big><b>Filter out images that does not contain any annotation:</b></big>
+<big><b>Filter out images that do not contain any annotation:</b></big>
 </summary>
 
 ```python

diff --git a/docs/index.md b/docs/index.md
@@ -97,7 +97,7 @@ SAHI addresses this by applying a unique methodology that can be used with any o
 
 ## Interactive Examples
 
-All documentation files are complemented by interactive Jupyter notebooks in the [demo directory](/notebooks/):
+All documentation files are complemented by interactive Jupyter notebooks in the [demo directory](notebooks/):
 
 <div class="grid cards" markdown>
 

diff --git a/docs/zh/README.md b/docs/zh/README.md
@@ -0,0 +1,236 @@
+<div align="center">
+<h1>
+  SAHI: 切片辅助高效推理
+</h1>
+
+<h4>
+  一个轻量级的计算机视觉库，可实现大规模的目标检测和实例分割
+</h4>
+
+<h4>
+    <img width="700" alt="teaser" src="https://raw.githubusercontent.com/obss/sahi/main/resources/sliced_inference.gif">
+</h4>
+
+<div>
+    <a href="https://pepy.tech/project/sahi"><img src="https://pepy.tech/badge/sahi" alt="downloads"></a>
+    <a href="https://pepy.tech/project/sahi"><img src="https://pepy.tech/badge/sahi/month" alt="downloads"></a>
+    <a href="https://github.com/obss/sahi/blob/main/LICENSE.md"><img src="https://img.shields.io/pypi/l/sahi" alt="License"></a>
+    <a href="https://badge.fury.io/py/sahi"><img src="https://badge.fury.io/py/sahi.svg" alt="pypi version"></a>
+    <a href="https://anaconda.org/conda-forge/sahi"><img src="https://anaconda.org/conda-forge/sahi/badges/version.svg" alt="conda version"></a>
+    <a href="https://github.com/obss/sahi/actions/workflows/ci.yml"><img src="https://github.com/obss/sahi/actions/workflows/ci.yml/badge.svg" alt="Continuous Integration"></a>
+  <br>
+    <a href="https://context7.com/obss/sahi"><img src="https://img.shields.io/badge/Context7%20MCP-Indexed-blue" alt="Context7 MCP"></a>
+    <a href="https://context7.com/obss/sahi/llms.txt"><img src="https://img.shields.io/badge/llms.txt-✓-brightgreen" alt="llms.txt"></a>
+    <a href="https://ieeexplore.ieee.org/document/9897990"><img src="https://img.shields.io/badge/DOI-10.1109%2FICIP46576.2022.9897990-orange.svg" alt="ci"></a>
+    <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_ultralytics.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+    <a href="https://huggingface.co/spaces/fcakyon/sahi-yolox"><img src="https://raw.githubusercontent.com/obss/sahi/main/resources/hf_spaces_badge.svg" alt="HuggingFace Spaces"></a>
+    <a href="https://deepwiki.com/obss/sahi"><img src="https://img.shields.io/badge/DeepWiki-obss%2Fsahi-blue.svg?logo=data:image/png;base64,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" alt="Sliced/tiled inference DeepWiki"></a>
+    <a href="https://squidfunk.github.io/mkdocs-material/"><img src="https://img.shields.io/badge/Material_for_MkDocs-526CFE?logo=MaterialForMkDocs&logoColor=white" alt="built-with-material-for-mkdocs"></a>
+
+</div>
+</div>
+
+## <div align="center">概览</div>
+
+SAHI 通过启用**切片推理**来检测大图像中的小物体，从而帮助开发人员克服了对象检测中的实际挑战。它支持各种流行的检测模型并提供易于使用的 API。
+
+<div align="center">
+
+🌐 [English](README.md) | 🇨🇳 [简体中文](docs/zh/README.md)
+
+</div>
+
+| 命令  | 描述  |
+|---|---|
+| [predict](https://github.com/obss/sahi/blob/main/docs/cli.md#predict-command-usage)  | 使用任意 [ultralytics](https://github.com/ultralytics/ultralytics)/[mmdet](https://github.com/open-mmlab/mmdetection)/[huggingface](https://huggingface.co/models?pipeline_tag=object-detection&sort=downloads)/[torchvision](https://pytorch.org/vision/stable/models.html#object-detection) 模型进行切片或标准视频 / 图像预测 - 参见 [命令行指南](docs/cli.md#predict-command-usage) |
+| [predict-fiftyone](https://github.com/obss/sahi/blob/main/docs/cli.md#predict-fiftyone-command-usage)  | 使用任意支持的模型进行切片或标准预测，并在 [fiftyone应用](https://github.com/voxel51/fiftyone) 中探索结果 - [了解更多](docs/fiftyone.md) |
+| [coco slice](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-slice-command-usage)  | 自动切片 COCO 标注和图像文件 - 参见 [切片工具](docs/slicing.md) |
+| [coco fiftyone](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-fiftyone-command-usage)  | 在 [fiftyone ui](https://github.com/voxel51/fiftyone) 中探索 COCO 数据集的多个预测结果，按错误检测数量排序 |
+| [coco evaluate](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-evaluate-command-usage)  | 针对给定的预测和真实数据评估 COCO 的类级别 AP 和 AR - 查看 [COCO 工具](docs/coco.md) |
+| [coco analyse](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-analyse-command-usage)  | 计算并导出多种错误分析图表 - 参见 [complete guide](docs/README.md) |
+| [coco yolo](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-yolo-command-usage)  | 将任意 COCO 数据集自动转换为 [ultralytics](https://github.com/ultralytics/ultralytics) 格式 |
+
+### 社区认可
+
+[📜 引用 SAHI 的出版物列表（当前超过 400 篇）](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0,5&cites=14065474760484865747&scipsc=&q=&scisbd=1)
+
+[🏆 使用 SAHI 的竞赛获奖者列表](https://github.com/obss/sahi/discussions/688)
+
+### AI 工具认可
+SAHI 的文档已在 [Context7 MCP](https://context7.com/obss/sahi) 中建立索引，为 AI 编码助手提供最新的，版本特定的代码示例和 API 参考。我们还提供了一个遵循 AI 可读文档新兴标准的 [llms.txt](https://context7.com/obss/sahi/llms.txt) 文件。要将 SAHI 文档集成到您的 AI 开发工作流程中，请查看 [Context7 MCP 安装指南](https://github.com/upstash/context7#%EF%B8%8F-installation).
+
+## <div align="center">安装</div>
+
+### 基本安装
+```bash
+pip install sahi
+```
+
+<details closed>
+<summary>
+<big><b>详细安装说明（点击展开）</b></big>
+</summary>
+
+- 安装您所需的 PyTorch 和 torchvision 版本：
+
+```console
+pip install torch==2.7.0 torchvision==0.22.0 --index-url https://download.pytorch.org/whl/cu126
+```
+（为了获得mmdet框架的支持，您需要安装torch 2.1.2版本）：
+
+```console
+pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu121
+```
+
+- 安装您所需的检测框架 (ultralytics):
+
+```console
+pip install ultralytics>=8.3.161
+```
+
+- 安装您所需的检测框架 (huggingface):
+
+```console
+pip install transformers>=4.49.0 timm
+```
+
+- 安装您所需的检测框架 (yolov5):
+
+```console
+pip install yolov5==7.0.14 sahi==0.11.21
+```
+
+- 安装您所需的检测框架 (mmdet):
+
+```console
+pip install mim
+mim install mmdet==3.3.0
+```
+
+- 安装您所需的检测框架 (roboflow):
+
+```console
+pip install inference>=0.50.3 rfdetr>=1.1.0
+```
+
+</details>
+
+## <div align="center">快速开始</div>
+
+### 教程
+
+- [SAHI 简介](https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80) - 请查阅 [完整的文档](docs/README.md) 以了解高级用法。
+
+- [官方论文](https://ieeexplore.ieee.org/document/9897990) (ICIP 2022 oral)
+
+- [预训练权重 和 ICIP 2022 论文文件](https://github.com/fcakyon/small-object-detection-benchmark)
+
+- [视频教程（2025年）](https://www.youtube.com/watch?v=ILqMBah5ZvI) (推荐)
+
+- [使用 FiftyOne 可视化并评估 SAHI 的预测结果](https://voxel51.com/blog/how-to-detect-small-objects/)
+
+- [《探索 SAHI》——来自 learnopencv.com 的研究文章](https://learnopencv.com/slicing-aided-hyper-inference/)
+
+- [Encord 对 Slicing Aided Hyper Inference（SAHI）的解读](https://encord.com/blog/slicing-aided-hyper-inference-explained/)
+
+- [视频教程：SAHI 在小目标检测中的应用](https://www.youtube.com/watch?v=UuOJKxn-M8&t=270s)
+
+- [视频推理支持现已上线](https://github.com/obss/sahi/discussions/626)
+
+- [Kaggle notebook](https://www.kaggle.com/remekkinas/sahi-slicing-aided-hyper-inference-yv5-and-yx)
+
+- [卫星图像目标检测](https://blog.ml6.eu/how-to-detect-small-objects-in-very-large-images-70234bab0f98)
+
+- [误差分析绘图 & 评估](https://github.com/obss/sahi/discussions/622) (推荐)
+
+- [交互式结果可视化与检查](https://github.com/obss/sahi/discussions/624) (推荐)
+
+- [COCO 数据集转换](https://medium.com/codable/convert-any-dataset-to-coco-object-detection-format-with-sahi-95349e1fe2b7)
+
+- [切片操作 notebook 示例](demo/slicing.ipynb)
+
+- `YOLOX` + `SAHI` 示例: <a href="https://huggingface.co/spaces/fcakyon/sahi-yolox"><img src="https://raw.githubusercontent.com/obss/sahi/main/resources/hf_spaces_badge.svg" alt="sahi-yolox"></a>
+
+- `YOLO12` + `SAHI` 实战教程: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_ultralytics.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-yolo12"></a>
+
+- `YOLO11-OBB` + `SAHI` 实战教程: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_ultralytics.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-yolo11-obb"></a> (NEW)
+
+- `YOLO11` + `SAHI` 实战教程: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_ultralytics.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-yolo11"></a>
+
+- `Roboflow/RF-DETR` + `SAHI` 实战教程: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_roboflow.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="roboflow"></a> (NEW)
+
+- `RT-DETR v2` + `SAHI` 实战教程: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_huggingface.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-rtdetrv2"></a> (NEW)
+
+- `RT-DETR` + `SAHI` 实战教程: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_rtdetr.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-rtdetr"></a>
+
+- `HuggingFace` + `SAHI` 实战教程: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_huggingface.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-huggingface"></a>
+
+- `YOLOv5` + `SAHI` 实战教程: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolov5.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-yolov5"></a>
+
+- `MMDetection` + `SAHI` 实战教程: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_mmdetection.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-mmdetection"></a>
+
+- `TorchVision` + `SAHI` 实战教程: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_torchvision.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-torchvision"></a>
+
+<a href="https://huggingface.co/spaces/fcakyon/sahi-yolox"><img width="600" src="https://user-images.githubusercontent.com/34196005/144092739-c1d9bade-a128-4346-947f-424ce00e5c4f.gif" alt="sahi-yolox"></a>
+
+### 与框架无关的切片/标准预测
+
+<img width="700" alt="sahi-predict" src="https://user-images.githubusercontent.com/34196005/149310540-e32f504c-6c9e-4691-8afd-59f3a1a457f0.gif">
+
+请在 [CLI 文档](docs/cli.md#predict-command-usage) 中查找关于使用 `sahi predict` 命令的详细信息，并查阅 [prediction API](docs/predict.md) 以了解高级用法。
+
+请在 [视频推理教程](https://github.com/obss/sahi/discussions/626) 中查找关于视频推理的详细信息。
+
+### 误差分析绘图 & 评估
+
+<img width="700" alt="sahi-analyse" src="https://user-images.githubusercontent.com/34196005/149537858-22b2e274-04e8-4e10-8139-6bdcea32feab.gif">
+
+请在 [误差分析绘图 & 评估](https://github.com/obss/sahi/discussions/622) 中查找相关的详细信息。
+
+### 交互式结果可视化与检查
+
+<img width="700" alt="sahi-fiftyone" src="https://user-images.githubusercontent.com/34196005/149321540-e6dd5f3-36dc-4267-8574-a985dd0c6578.gif">
+
+探索 [FiftyOne 集成](docs/fiftyone.md) 以实现交互式可视化与检查。
+
+### 其他实用工具
+
+请查阅全面的 COCO 工具指南，了解 YOLO 格式转换、数据集切片、子采样、筛选、合并与分割等操作。
+
+请查阅 [完整的 COCO 工具指南](docs/coco.md) 了解 YOLO 格式转换、数据集切片、子采样、筛选、合并与分割等操作。了解更多关于 [切片工具](docs/slicing.md) ，以实现对图像和数据集切片参数的精细控制。
+
+## <div align="center">引用</div>
+如果您在您的工作中使用了这个包，请如下文引用：
+
+```bibtex
+@article{akyon2022sahi,
+  title={Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection},
+  author={Akyon, Fatih Cagatay and Altinuc, Sinan Onur and Temizel, Alptekin},
+  journal={2022 IEEE International Conference on Image Processing (ICIP)},
+  doi={10.1109/ICIP46576.2022.9897990},
+  pages={966-970},
+  year={2022}
+}
+```
+
+```bibtex
+@software{obss2021sahi,
+  author       = {Akyon, Fatih Cagatay and Cengiz, Cemil and Altinuc, Sinan Onur and Cavusoglu, Devrim and Sahin, Kadir and Eryuksel, Ogulcan},
+  title        = {{SAHI: A lightweight vision library for performing large scale object detection and instance segmentation}},
+  month        = nov,
+  year         = 2021,
+  publisher    = {Zenodo},
+  doi          = {10.5281/zenodo.5718950},
+  url          = {https://doi.org/10.5281/zenodo.5718950}
+}
+```
+
+## <div align="center">贡献者</div>
+
+欢迎贡献！请参阅我们的 [贡献指南](CONTRIBUTING.md) 来开始使用. 感谢所有贡献者🙏！
+
+<p align="center">
+    <a href="https://github.com/obss/sahi/graphs/contributors">
+      <img src="https://contrib.rocks/image?repo=obss/sahi" />
+    </a>
+</p>