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-# Yolov6-SAHI
+<div align="center">
+<h1>
+  SAHI: Slicing Aided Hyper Inference
+</h1>
+
+<h4>
+  A lightweight vision library for performing large scale object detection & instance segmentation
+</h4>
+
+<h4>
+    <img width="700" alt="teaser" src="https://raw.githubusercontent.com/obss/sahi/main/resources/sliced_inference.gif">
+</h4>
+
+<div>
+    <a href="https://pepy.tech/project/sahi"><img src="https://pepy.tech/badge/sahi" alt="downloads"></a>
+    <a href="https://pepy.tech/project/sahi"><img src="https://pepy.tech/badge/sahi/month" alt="downloads"></a>
+    <br>
+    <a href="https://badge.fury.io/py/sahi"><img src="https://badge.fury.io/py/sahi.svg" alt="pypi version"></a>
+    <a href="https://anaconda.org/conda-forge/sahi"><img src="https://anaconda.org/conda-forge/sahi/badges/version.svg" alt="conda version"></a>
+    <a href="https://github.com/obss/sahi/actions/workflows/package_testing.yml"><img src="https://github.com/obss/sahi/actions/workflows/package_testing.yml/badge.svg" alt="package testing"></a>
+  <br>
+    <a href="https://ieeexplore.ieee.org/document/9897990"><img src="https://img.shields.io/badge/DOI-10.1109%2FICIP46576.2022.9897990-orange.svg" alt="ci"></a>
+  <br>
+    <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolov5.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+    <a href="https://huggingface.co/spaces/fcakyon/sahi-yolox"><img src="https://raw.githubusercontent.com/obss/sahi/main/resources/hf_spaces_badge.svg" alt="HuggingFace Spaces"></a>
+
+
+</div>
+</div>
+
+## <div align="center">Overview</div>
+
+Object detection and instance segmentation are by far the most important fields of applications in Computer Vision. However, detection of small objects and inference on large images are still major issues 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)  | calcualate 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 |
+
+## <div align="center">Quick Start Examples</div>
+
+[📜 List of publications that cite SAHI (currently 20+)](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)
+
+- [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: <a href="https://huggingface.co/spaces/fcakyon/sahi-yolox"><img src="https://raw.githubusercontent.com/obss/sahi/main/resources/hf_spaces_badge.svg" alt="sahi-yolox"></a> (RECOMMENDED)
+
+- `YOLOv5` + `SAHI` walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolov5.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-yolov5"></a>
+
+- `MMDetection` + `SAHI` walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_mmdetection.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-mmdetection"></a>
+
+- `Detectron2` + `SAHI` walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_detectron2.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-detectron2"></a>
+
+- `HuggingFace` + `SAHI` walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_huggingface.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-huggingface"></a> (NEW)
+
+- `TorchVision` + `SAHI` walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_torchvision.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-torchvision"></a> (NEW)
+
+<a href="https://huggingface.co/spaces/fcakyon/sahi-yolox"><img width="600" src="https://user-images.githubusercontent.com/34196005/144092739-c1d9bade-a128-4346-947f-424ce00e5c4f.gif" alt="sahi-yolox"></a> 
+
+
+</details>
+
+### Installation
+
+<img width="700" alt="sahi-installation" src="https://user-images.githubusercontent.com/34196005/149311602-b44e6fe1-f496-40f2-a7ae-5ea1f66e1550.gif">
+
+
+<details closed>
+<summary>
+<big><b>Installation details:</b></big>
+</summary>
+
+- Install `sahi` using pip:
+
+```console
+pip install sahi
+```
+
+- On Windows, `Shapely` needs to be installed via Conda:
+
+```console
+conda install -c conda-forge shapely
+```
 
-## Introduction of Yolov6
-YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance.
+- Install your desired version of pytorch and torchvision:
 
-<img width="959" alt="image" src="https://user-images.githubusercontent.com/62583018/176825240-abba13fd-94e9-43ed-b7a4-066debd4072f.png">
+```console
+conda install pytorch=1.11.0 torchvision=0.12.0 cudatoolkit=11.3 -c pytorch
+```
+
+- Install your desired detection framework (yolov5):
 
+```console
+pip install yolov5==6.2.3
+```
 
-YOLOv6-nano achieves 35.0 mAP on COCO val2017 dataset with 1242 FPS on T4 using TensorRT FP16 for bs32 inference, and YOLOv6-s achieves 43.1 mAP on COCO val2017 dataset with 520 FPS on T4 using TensorRT FP16 for bs32 inference.
+- Install your desired detection framework (mmdet):
 
-YOLOv6 is composed of the following methods:
+```console
+pip install mmcv-full==1.7.0 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html
+```
 
-Hardware-friendly Design for Backbone and Neck
-Efficient Decoupled Head with SIoU Loss
+```console
+pip install mmdet==2.25.3
+```
 
+- Install your desired detection framework (detectron2):
 
-github :https://github.com/meituan/YOLOv6
+```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
+```
+
+</details>
+
+### Framework Agnostic Sliced/Standard Prediction
+
+<img width="700" alt="sahi-predict" src="https://user-images.githubusercontent.com/34196005/149310540-e32f504c-6c9e-4691-8afd-59f3a1a457f0.gif">
+
+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
+
+<img width="700" alt="sahi-analyse" src="https://user-images.githubusercontent.com/34196005/149537858-22b2e274-04e8-4e10-8139-6bdcea32feab.gif">
+
+Find detailed info at [Error Analysis Plots & Evaluation](https://github.com/obss/sahi/discussions/622).
+
+### Interactive Visualization & Inspection
+
+<img width="700" alt="sahi-fiftyone" src="https://user-images.githubusercontent.com/34196005/149321540-e6ddd5f3-36dc-4267-8574-a985dd0c6578.gif">
+
+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).
+
+## <div align="center">Citation</div>
+
+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}
+}
+```
+
+## <div align="center">Contributing</div>
+
+`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
+```
+
+## <div align="center">Contributors</div>
 
 <div align="center">
-<h1>
-  SAHI: Slicing Aided Hyper Inference
-</h1>
 
-<h4>
-  A lightweight vision library for performing large scale object detection & instance segmentation
-</h4>
+<a align="left" href="https://github.com/fcakyon" target="_blank">Fatih Cagatay Akyon</a>
 
-<h4>
-    <img width="700" alt="teaser" src="https://raw.githubusercontent.com/obss/sahi/main/resources/sliced_inference.gif">
-</h4>
+<a align="left" href="https://github.com/sinanonur" target="_blank">Sinan Onur Altinuc</a>
 
-A lightweight vision library for performing large scale object detection & instance segmentation
+<a align="left" href="https://github.com/devrimcavusoglu" target="_blank">Devrim Cavusoglu</a>
 
-Object detection and instance segmentation are by far the most important fields of applications in Computer Vision. However, detection of small objects and inference on large images are still major issues in practical usage. Here comes the SAHI to help developers overcome these real-world problems with many vision utilities.
+<a align="left" href="https://github.com/cemilcengiz" target="_blank">Cemil Cengiz</a>
+
+<a align="left" href="https://github.com/oulcan" target="_blank">Ogulcan Eryuksel</a>
+
+<a align="left" href="https://github.com/kadirnar" target="_blank">Kadir Nar</a>
+
+<a align="left" href="https://github.com/madenburak" target="_blank">Burak Maden</a>
+
+<a align="left" href="https://github.com/PushpakBhoge" target="_blank">Pushpak Bhoge</a>
+
+<a align="left" href="https://github.com/mcvarer" target="_blank">M. Can V.</a>
+
+<a align="left" href="https://github.com/ChristofferEdlund" target="_blank">Christoffer Edlund</a>
+
+<a align="left" href="https://github.com/ishworii" target="_blank">Ishwor</a>
+
+<a align="left" href="https://github.com/mecevit" target="_blank">Mehmet Ecevit</a>
+
+<a align="left" href="https://github.com/ssahinnkadir" target="_blank">Kadir Sahin</a>
+
+<a align="left" href="https://github.com/weypro" target="_blank">Wey</a>
+
+<a align="left" href="https://github.com/youngjae-avikus" target="_blank">Youngjae</a>
+
+<a align="left" href="https://github.com/tureckova" target="_blank">Alzbeta Tureckova</a>
+
+<a align="left" href="https://github.com/weiji14" target="_blank">Wei Ji</a>
 
+<a align="left" href="https://github.com/aynursusuz" target="_blank">Aynur Susuz</a>
 
-github:https://github.com/obss/sa
+</div>