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<div >
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<a href="https://pepy.tech/project/sahi"><img src="https://pepy.tech/badge/sahi" alt="downloads"></a>
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<a href="https://pepy.tech/project/sahi"><img src="https://pepy.tech/badge/sahi/month" alt="downloads"></a>
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- <a href="https://doi.org/10.48550/arXiv.2202.06934"><img src="https://img.shields.io/badge/arXiv-2202.06934-b31b1b.svg" alt="ci"></a>
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<br>
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<a href="https://badge.fury.io/py/sahi"><img src="https://badge.fury.io/py/sahi.svg" alt="pypi version"></a>
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<a href="https://anaconda.org/conda-forge/sahi"><img src="https://anaconda.org/conda-forge/sahi/badges/version.svg" alt="conda version"></a>
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- <br>
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+ <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>
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+ <br >
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+ <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>
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+ <br >
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<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>
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<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>
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- <br>
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- <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>
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</div >
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</div >
@@ -49,7 +50,7 @@ Object detection and instance segmentation are by far the most important fields
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- [ Introduction to SAHI] ( https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80 )
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- - [ Official paper] ( https://arxiv. org/abs/2202.06934 ) (ICIP 2022 oral, 17 + citations) (NEW)
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+ - [ Official paper] ( https://ieeexplore.ieee. org/document/9897990 ) (ICIP 2022 oral, 18 + citations) (NEW)
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- [ Pretrained weights and ICIP 2022 paper files] ( https://github.com/fcakyon/small-object-detection-benchmark )
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@@ -178,7 +179,9 @@ If you use this package in your work, please cite it as:
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@article{akyon2022sahi,
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title={Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection},
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author={Akyon, Fatih Cagatay and Altinuc, Sinan Onur and Temizel, Alptekin},
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- journal={arXiv preprint arXiv:2202.06934},
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+ journal={2022 IEEE International Conference on Image Processing (ICIP)},
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+ doi={10.1109/ICIP46576.2022.9897990},
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+ pages={966-970},
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year={2022}
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}
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```
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