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Pytorch implementation for "Large-Scale Long-Tailed Recognition in an Open World" (CVPR 2019)
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README.md

Large-Scale Long-Tailed Recognition in an Open World

[Project] [Paper]

Overview

Open Long-Tailed Recognition (OLTR) is the author's re-implementation of the long-tail recognizer described in:
"Large-Scale Long-Tailed Recognition in an Open World"
Ziwei Liu*Zhongqi Miao*Xiaohang ZhanJiayun WangBoqing GongStella X. Yu  (CUHK & UC Berkeley / ICSI)  in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019, Oral Presentation

Further information please contact Zhongqi Miao and Ziwei Liu.

Requirements

Data Preparation

NOTE: Places-LT dataset have been updated since the first version. Please download again if you have the first version.

  • First, please download the ImageNet_2014 and Places_365 (256x256 version). Please also change the data_root in main.py accordingly.

  • Next, please download ImageNet-LT and Places-LT from here. Please put the downloaded files into the data directory like this:

data
  |--ImageNet_LT
    |--ImageNet_LT_open
    |--ImageNet_LT_train.txt
    |--ImageNet_LT_test.txt
    |--ImageNet_LT_val.txt
    |--ImageNet_LT_open.txt
  |--Places_LT
    |--Places_LT_open
    |--Places_LT_train.txt
    |--Places_LT_test.txt
    |--Places_LT_val.txt
    |--Places_LT_open.txt

Download Caffe Pre-trained Models for Places_LT Stage_1 Training

  • Caffe pretrained ResNet152 weights can be downloaded from here, and save the file to .logs/caffe_resnet152.pth

Getting Started (Training & Testing)

ImageNet-LT

  • Stage 1 training:
python main.py --config ./config/ImageNet_LT/stage_1.py
  • Stage 2 training:
python main.py --config ./config/ImageNet_LT/stage_2_meta_embedding.py
  • Close-set testing:
python main.py --config ./config/ImageNet_LT/stage_2_meta_embedding.py --test
  • Open-set testing (thresholding)
python main.py --config ./config/ImageNet_LT/stage_2_meta_embedding.py --test_open
  • Test on stage 1 model
python main.py --config ./config/ImageNet_LT/stage_1.py --test

Places-LT

  • Stage 1 training:
python main.py --config ./config/Places_LT/stage_1.py
  • Stage 2 training:
python main.py --config ./config/Places_LT/stage_2_meta_embedding.py
  • Close-set testing:
python main.py --config ./config/Places_LT/stage_2_meta_embedding.py --test
  • Open-set testing (thresholding)
python main.py --config ./config/Places_LT/stage_2_meta_embedding.py --test_open

License and Citation

The use of this software is RESTRICTED to non-commercial research and educational purposes.

@inproceedings{openlongtailrecognition,
  title={Large-Scale Long-Tailed Recognition in an Open World},
  author={Liu, Ziwei and Miao, Zhongqi and Zhan, Xiaohang and Wang, Jiayun and Gong, Boqing and Yu, Stella X.},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019}
}
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