SAHI: Slicing Aided Hyper Inference

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

Overview

Object detection and instance segmentation are by far the most important applications in Computer Vision. However, the detection of small objects and inference on large images still need to be improved in practical usage. Here comes the SAHI to help developers overcome these real-world problems with many vision utilities.

Quick Start Examples

📜 List of publications that cite SAHI (currently 300+)

🏆 List of competition winners that used SAHI

Tutorials

• Introduction to SAHI

• 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: (NEW)

• YOLO11-OBB + SAHI walkthrough: (NEW)

• YOLO11 + SAHI walkthrough:

• RT-DETR v2 + SAHI walkthrough: (NEW)

• RT-DETR + SAHI walkthrough: (NEW)

• DeepSparse + SAHI walkthrough:

• HuggingFace + SAHI walkthrough:

• YOLOv5 + SAHI walkthrough:

• MMDetection + SAHI walkthrough:

• TorchVision + SAHI walkthrough:

Installation

pip install sahi

conda install -c conda-forge shapely

pip install torch==2.6.0 torchvision==0.21.0 --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 yolov5==7.0.14 sahi==0.11.21

pip install ultralytics>=8.3.86

pip install mim
mim install mmdet==3.3.0

pip install transformers>=4.42.0 timm

Framework Agnostic Sliced/Standard Prediction

Find detailed info on sahi predict command at cli.md.

Find detailed info on video inference at video inference tutorial.

Find detailed info on image/dataset slicing utilities at slicing.md.

Error Analysis Plots & Evaluation

Find detailed info at Error Analysis Plots & Evaluation.

Interactive Visualization & Inspection

Find detailed info at Interactive Result Visualization and Inspection.

Other utilities

Find detailed info on COCO utilities (yolov5 conversion, slicing, subsampling, filtering, merging, splitting) at coco.md.

Citation

If you use this package in your work, please cite it 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

Add new frameworks

sahi library currently supports all Ultralytics (YOLOv8/v10/v11/RTDETR) models, MMDetection models, Detectron2 models, and HuggingFace object detection models. Moreover, it is easy to add new frameworks.

All you need to do is, create a new .py file under sahi/models/ folder and create a new class in that .py file that implements DetectionModel class. You can take the MMDetection wrapper or YOLOv5 wrapper as a reference.

Open a Pull Request

• Install the uv package manager on your system.
• Install pre-commit hooks with uv run pre-commit install.

Contributors