This repository contains the official Pytorch implementation of training & evaluation code and the pretrained models for:
Not All Labels Are Equal:Rationalizing The Labeling Costs for Training Object Detection
Ismail Elezi, Zhiding Yu, Anima Anandkumar, Laura Leal-Taixe, and Jose M. Alvarez.
CVPR 2022.
We experimented with the SSD in the PyTorch framework. To use our model, complete the installation & preparation on the SSD pytorch homepage
python train.pypython eval.pyCopyright © 2022-2023, NVIDIA Corporation and Affiliates. All rights reserved.
This work is made available under the Nvidia Source Code License-NC. Click here to view a copy of this license.
The pre-trained models are shared under CC-BY-NC-SA-4.0. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
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If you find this code useful, please consider citing the following paper:
@inproceedings{DBLP:conf/cvpr/EleziYALA22,
author = {Ismail Elezi and
Zhiding Yu and
Anima Anandkumar and
Laura Leal{-}Taix{\'{e}} and
Jose M. Alvarez},
title = {Not All Labels Are Equal: Rationalizing The Labeling Costs for Training
Object Detection},
booktitle = {{IEEE/CVF} Conference on Computer Vision and Pattern Recognition,
{CVPR} 2022, New Orleans, LA, USA, June 18-24, 2022},
pages = {14472--14481},
publisher = {{IEEE}},
year = {2022},
url = {https://doi.org/10.1109/CVPR52688.2022.01409},
doi = {10.1109/CVPR52688.2022.01409},
timestamp = {Wed, 05 Oct 2022 16:31:19 +0200},
biburl = {https://dblp.org/rec/conf/cvpr/EleziYALA22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}We use the semi-consistency method CSD as developed by Jeong et al. Consider citing the following paper
@inproceedings{DBLP:conf/nips/JeongLKK19,
author = {Jisoo Jeong and
Seungeui Lee and
Jeesoo Kim and
Nojun Kwak},
editor = {Hanna M. Wallach and
Hugo Larochelle and
Alina Beygelzimer and
Florence d'Alch{\'{e}}{-}Buc and
Emily B. Fox and
Roman Garnett},
title = {Consistency-based Semi-supervised Learning for Object detection},
booktitle = {Advances in Neural Information Processing Systems 32: Annual Conference
on Neural Information Processing Systems 2019, NeurIPS 2019, December
8-14, 2019, Vancouver, BC, Canada},
pages = {10758--10767},
year = {2019},
url = {https://proceedings.neurips.cc/paper/2019/hash/d0f4dae80c3d0277922f8371d5827292-Abstract.html},
timestamp = {Mon, 16 May 2022 15:41:51 +0200},
biburl = {https://dblp.org/rec/conf/nips/JeongLKK19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}