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SAHI: Slicing Aided Hyper Inference

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

SAHI helps developers overcome real-world challenges in object detection by enablingsliced inference for detecting small objects in large images. It supports various popular detection models and provides easy-to-use APIs.

📜 List of publications that cite SAHI (currently 400+)

🏆 List of competition winners that used SAHI

SAHI's documentation isindexed in Context7 MCP, providing AI coding assistants with up-to-date, version-specific code examples and API references. We also provide anllms.txt file following the emerging standard for AI-readable documentation. To integrate SAHI docs with your AI development workflow, check out theContext7 MCP installation guide.

pip install sahi

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

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

pip install ultralytics>=8.3.161

pip install transformers>=4.49.0 timm

pip install yolov5==7.0.14 sahi==0.11.21

pip install mimmim install mmdet==3.3.0

pip install inference>=0.50.3 rfdetr>=1.1.0

• Introduction to SAHI - explore thecomplete 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:

• YOLO12 +SAHI walkthrough:

• YOLO11-OBB +SAHI walkthrough: (NEW)

• YOLO11 +SAHI walkthrough:

• Roboflow/RF-DETR +SAHI walkthrough: (NEW)

• RT-DETR v2 +SAHI walkthrough: (NEW)

• RT-DETR +SAHI walkthrough:

• HuggingFace +SAHI walkthrough:

• YOLOv5 +SAHI walkthrough:

• MMDetection +SAHI walkthrough:

• TorchVision +SAHI walkthrough:

Find detailed info on usingsahi predict command in theCLI documentation and explore theprediction API for advanced usage.

Find detailed info on video inference atvideo inference tutorial.

Find detailed info atError Analysis Plots & Evaluation.

ExploreFiftyOne integration for interactive visualization and inspection.

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

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}}

We welcome contributions! Please see ourContributing Guide to get started. Thank you 🙏 to all our contributors!