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https://github.com/obss/sahi

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
https://github.com/obss/sahi

coco computer-vision deep-learning detectron2 explainable-ai fiftyone huggingface instance-segmentation large-image machine-learning merge mmdetection object-detection python pytorch remote-sensing satellite small-object-detection tiling yolov5

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

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  SAHI: Slicing Aided Hyper Inference






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






    teaser






    downloads

    downloads

    


    pypi version

    conda version

    package testing

  


    ci

  


    Open In Colab

    HuggingFace Spaces



​    






## 
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.



| Command  | Description  |

|---|---|

| [predict](https://github.com/obss/sahi/blob/main/docs/cli.md#predict-command-usage)  | perform sliced/standard video/image prediction using any [yolov5](https://github.com/ultralytics/yolov5)/[mmdet](https://github.com/open-mmlab/mmdetection)/[detectron2](https://github.com/facebookresearch/detectron2)/[huggingface](https://huggingface.co/models?pipeline_tag=object-detection&sort=downloads) model |

| [predict-fiftyone](https://github.com/obss/sahi/blob/main/docs/cli.md#predict-fiftyone-command-usage)  | perform sliced/standard prediction using any [yolov5](https://github.com/ultralytics/yolov5)/[mmdet](https://github.com/open-mmlab/mmdetection)/[detectron2](https://github.com/facebookresearch/detectron2)/[huggingface](https://huggingface.co/models?pipeline_tag=object-detection&sort=downloads) model and explore results in [fiftyone app](https://github.com/voxel51/fiftyone) |

| [coco slice](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-slice-command-usage)  | automatically slice COCO annotation and image files |

| [coco fiftyone](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-fiftyone-command-usage)  | explore multiple prediction results on your COCO dataset with [fiftyone ui](https://github.com/voxel51/fiftyone) ordered by number of misdetections |

| [coco evaluate](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-evaluate-command-usage)  | evaluate classwise COCO AP and AR for given predictions and ground truth |

| [coco analyse](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-analyse-command-usage)  | calculate and export many error analysis plots |

| [coco yolov5](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-yolov5-command-usage)  | automatically convert any COCO dataset to [yolov5](https://github.com/ultralytics/yolov5) format |



## 
Quick Start Examples



[📜 List of publications that cite SAHI (currently 100+)](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0,5&cites=14065474760484865747&scipsc=&q=&scisbd=1)



[🏆 List of competition winners that used SAHI](https://github.com/obss/sahi/discussions/688)



### Tutorials



- [Introduction to SAHI](https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80)



- [Official paper](https://ieeexplore.ieee.org/document/9897990) (ICIP 2022 oral) (NEW)



- [Pretrained weights and ICIP 2022 paper files](https://github.com/fcakyon/small-object-detection-benchmark)



- ['Exploring SAHI' Research Article from 'learnopencv.com'](https://learnopencv.com/slicing-aided-hyper-inference/) (2023) (NEW)



- ['VIDEO TUTORIAL: Slicing Aided Hyper Inference for Small Object Detection - SAHI'](https://www.youtube.com/watch?v=UuOjJKxn-M8&t=270s) (2023) (NEW)



- [Video inference support is live](https://github.com/obss/sahi/discussions/626)



- [Kaggle notebook](https://www.kaggle.com/remekkinas/sahi-slicing-aided-hyper-inference-yv5-and-yx)



- [Satellite object detection](https://blog.ml6.eu/how-to-detect-small-objects-in-very-large-images-70234bab0f98)



- [Error analysis plots & evaluation](https://github.com/obss/sahi/discussions/622) (NEW)



- [Interactive result visualization and inspection](https://github.com/obss/sahi/discussions/624) (NEW)



- [COCO dataset conversion](https://medium.com/codable/convert-any-dataset-to-coco-object-detection-format-with-sahi-95349e1fe2b7)



- [Slicing operation notebook](demo/slicing.ipynb)



- `YOLOX` + `SAHI` demo: sahi-yolox (RECOMMENDED)



- `YOLOv5` + `SAHI` walkthrough: sahi-yolov5



- `MMDetection` + `SAHI` walkthrough: sahi-mmdetection



- `Detectron2` + `SAHI` walkthrough: sahi-detectron2



- `HuggingFace` + `SAHI` walkthrough: sahi-huggingface (NEW)



- `TorchVision` + `SAHI` walkthrough: sahi-torchvision (NEW)



sahi-yolox 



### Installation



sahi-installation



Installation details:



- Install `sahi` using pip:



```console

pip install sahi

```



- On Windows, `Shapely` needs to be installed via Conda:



```console

conda install -c conda-forge shapely

```



- Install your desired version of pytorch and torchvision (cuda 11.3 for detectron2, cuda 11.7 for rest):



```console

conda install pytorch=1.10.2 torchvision=0.11.3 cudatoolkit=11.3 -c pytorch

```



```console

conda install pytorch=1.13.1 torchvision=0.14.1 pytorch-cuda=11.7 -c pytorch -c nvidia

```



- Install your desired detection framework (yolov5):



```console

pip install yolov5==7.0.13

```



- Install your desired detection framework (mmdet):



```console

pip install mim

mim install mmdet==3.0.0

```



- Install your desired detection framework (detectron2):



```console

pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html

```



- Install your desired detection framework (huggingface):



```console

pip install transformers timm

```



### Framework Agnostic Sliced/Standard Prediction



sahi-predict



Find detailed info on `sahi predict` command at [cli.md](docs/cli.md#predict-command-usage).



Find detailed info on video inference at [video inference tutorial](https://github.com/obss/sahi/discussions/626).



Find detailed info on image/dataset slicing utilities at [slicing.md](docs/slicing.md).



### Error Analysis Plots & Evaluation



sahi-analyse



Find detailed info at [Error Analysis Plots & Evaluation](https://github.com/obss/sahi/discussions/622).



### Interactive Visualization & Inspection



sahi-fiftyone



Find detailed info at [Interactive Result Visualization and Inspection](https://github.com/obss/sahi/discussions/624).



### Other utilities



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



Find detailed info on MOT utilities (ground truth dataset creation, exporting tracker metrics in mot challenge format) at [mot.md](docs/mot.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



`sahi` library currently supports all [YOLOv5 models](https://github.com/ultralytics/yolov5/releases), [MMDetection models](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md), [Detectron2 models](https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md), and [HuggingFace object detection models](https://huggingface.co/models?pipeline_tag=object-detection&sort=downloads). Moreover, it is easy to add new frameworks.



All you need to do is, create a new .py file under [sahi/models/](https://github.com/obss/sahi/tree/main/sahi/models) folder and create a new class in that .py file that implements [DetectionModel class](https://github.com/obss/sahi/blob/7e48bdb6afda26f977b763abdd7d8c9c170636bd/sahi/models/base.py#L12). You can take the [MMDetection wrapper](https://github.com/obss/sahi/blob/7e48bdb6afda26f977b763abdd7d8c9c170636bd/sahi/models/mmdet.py#L18) or [YOLOv5 wrapper](https://github.com/obss/sahi/blob/7e48bdb6afda26f977b763abdd7d8c9c170636bd/sahi/models/yolov5.py#L17) as a reference.



Before opening a PR:



- Install required development packages:



```bash

pip install -e ."[dev]"

```



- Reformat with black and isort:



```bash

python -m scripts.run_code_style format

```



## 
Contributors






Fatih Cagatay Akyon



Sinan Onur Altinuc



Devrim Cavusoglu



Cemil Cengiz



Ogulcan Eryuksel



Kadir Nar



Burak Maden



Pushpak Bhoge



M. Can V.



Christoffer Edlund



Ishwor



Mehmet Ecevit



Kadir Sahin



Wey



Youngjae



Alzbeta Tureckova



Wei Ji



Aynur Susuz



Pranav Durai



Lakshay Mehra