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.
| 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==2.13.0 torchvision==0.28.0 --index-url https://download.pytorch.org/whl/cu130
(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}
}
Contributing
We welcome contributions! Please see our Contributing Guide to get started. Thank you 🙏 to all our contributors!
1 maintainer
Release files for sahi 0.12.7
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sahi-0.12.7.tar.gz | 4.8 MB | Details |
Built distribution (wheel)
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|---|---|---|---|---|
| sahi-0.12.7-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 4.9 MB
Release files / sahi-0.12.7.tar.gz
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