obss/sahi

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

5,494

stars

818

commits

Python

primary language

Sep 3, 2026

updated

obss.github.io/sahi/

coco

computer-vision

deep-learning

explainable-ai

fiftyone

hacktoberfest

huggingface

instance-segmentation

large-image

machine-learning

object-detection

open-vocabulary-detection

oriented-object-detection

python

pytorch

remote-sensing

satellite

small-object-detection

tiling

yolo26

README

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 | 🇹🇷 Türkçe

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 on 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
YOLO26		-
YOLO11		-
YOLO11-OBB		-
YOLOE		-
Roboflow / RF-DETR		-
RT-DETR v2		-
RT-DETR		-
HuggingFace		-
GroundingDINO		-
YOLOv5		-
MMDetection		-
Detectron2		-
TorchVision		-
YOLOX	-

Framework Agnostic Sliced/Standard Prediction

Find detailed info on using sahi predict command in the CLI documentation and explore the prediction API for advanced usage.

Find detailed info on video inference at video inference tutorial.

Error Analysis Plots & Evaluation

Find detailed info at Error Analysis Plots & Evaluation.

Interactive Visualization & Inspection

Explore FiftyOne integration for interactive visualization and inspection.

Other Utilities

Check the comprehensive COCO utilities guide for YOLO conversion, dataset slicing, subsampling, filtering, merging, and splitting operations. Learn more about the slicing utilities for detailed control over image and dataset slicing parameters.

Citation

If you use this package in your work, please cite as:

@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}
}

@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}
}