Sahi

<div align="center"> <h1> SAHI: Slicing Aided Hyper Inference </h1> <h4> A lightweight vision library for performing large scale object detection & instance segmentation </h4> <h4> <img width="700" alt="teaser" src="https://raw.githubusercontent.com/obss/sahi/main/resources/sahi-sliced-inference-overview.avif"> </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">Overview</div>

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 | 🇨🇳 简体中文

</div>

| Command | Description | |---|---| | predict | perform sliced/standard video/image prediction using any ultralytics/mmdet/huggingface/torchvision model - see CLI guide | | predict-fiftyone | perform sliced/standard prediction using any supported model and explore results in fiftyone app - learn more | | coco slice | automatically slice COCO annotation and image files - see slicing utilities | | coco fiftyone | explore multiple prediction results on your COCO dataset with fiftyone ui ordered by number of misdetections | | coco evaluate | evaluate classwise COCO AP and AR for given predictions and ground truth - check COCO utilities | | coco analyse | calculate and export many error analysis plots - see the complete guide | | coco yolo | automatically convert any COCO dataset to ultralytics format |

Approved by the Community

📜 List of publications that cite SAHI (currently 600+)

🏆 List of competition winners that used SAHI

Approved by AI Tools

SAHI's documentation is indexed in Context7 MCP, providing AI coding assistants with up-to-date, version-specific code examples and API references. We also provide an llms.txt file following the emerging standard for AI-readable documentation. To integrate SAHI docs with your AI development workflow, check out the Context7 MCP installation guide.

<div align="center">Installation</div>

Basic Installation

pip install sahi

<details closed> <summary> <big><b>Detailed Installation (Click to open)</b></big> </summary>

• Install your desired version of pytorch and torchvision:

pip install torch==2.7.0 torchvision==0.22.0 --index-url https://download.pytorch.org/whl/cu126

(torch 2.1.2 is required for mmdet support):

pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu121

• Install your desired detection framework (ultralytics):

pip install ultralytics>=8.3.161

• Install your desired detection framework (huggingface):

pip install transformers>=4.49.0 timm

• Install your desired detection framework (yolov5):

pip install yolov5==7.0.14 sahi==0.11.21

• Install your desired detection framework (mmdet):

pip install mim
mim install mmdet==3.3.0

• Install your desired detection framework (roboflow):

pip install inference>=0.50.3 rfdetr>=1.1.0

</details>

<div align="center">Quick Start</div>

Tutorials

• Introduction to SAHI - explore the complete documentation for advanced usage

• Official paper (ICIP 2022 oral)

• Pretrained weights and ICIP 2022 paper files

• 2025 Video Tutorial (RECOMMENDED)

• Visualizing and Evaluating SAHI predictions with FiftyOne

• 'Exploring SAHI' Research Article from 'learnopencv.com'

• Slicing Aided Hyper Inference Explained by Encord

• 'VIDEO TUTORIAL: Slicing Aided Hyper Inference for Small Object Detection - SAHI'

• Video inference support is live

• Kaggle notebook

• Satellite object detection

• Error analysis plots & evaluation (RECOMMENDED)

• Interactive result visualization and inspection (RECOMMENDED)

• COCO dataset conversion

• Slicing operation notebook

• YOLOX + SAHI demo: <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 walkthrough: <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 walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_ult