[Project] [Paper]

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 Zhan,  Jiayun Wang,  Boqing Gong,  Stella 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.

• 05/02/2019: Fixed a bug in run_network.py so the models train properly. Update configuration file for Imagenet-LT stage 1 training so that the results from the paper can be reproduced.

• PyTorch (version >= 0.4.1)
• scikit-learn

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

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

• 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

• 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

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}
}