SAHI: Slicing Aided Hyper Inference

A lightweight vision library for performing large scale object detection & instance segmentation

Overview

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.

🌐 English | 🇨🇳 简体中文

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.

Installation

Basic Installation

pip install sahi

Detailed Installation (Click to open)

• 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==0.12.1

• Install your desired detection framework (mmdet):

pip install mim
mim install mmdet==3.3.0

• Install your desired detection framework (roboflow):

pip install inference>=0.51.5 rfdetr>=1.6.2

Quick Start

Learning Resources

Resource	Type
Introduction to SAHI	Blog Post
2025 Video Tutorial ⭐	Video
Official Paper (ICIP 2022 oral)	Paper
Pretrained Weights & ICIP 2022 Paper Files	Benchmark
Visualizing and Evaluating SAHI Predictions with FiftyOne	Blog Post
Exploring SAHI – learnopencv.com	Article
Slicing Aided Hyper Inference Explained by Encord	Article
Video Tutorial: SAHI for Small Object Detection	Video
Satellite Object Detection	Blog Post
COCO Dataset Conversion	Blog Post
Kaggle Notebook	Notebook
Error Analysis Plots & Evaluation ⭐	Discussion
Interactive Result Visualization and Inspection ⭐	Discussion
Video Inference Support	Discussion
Slicing Operation Notebook	Notebook
Complete Documentation	Docs

Notebooks & Demos

| Framework | Notebook | Demo | | ------------------ | ---------------------------------------------------------------------------------------------