Large-Scale Long-Tailed Recognition in an Open World

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 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.

Update notifications

08/05/2019: Fixed a bug in utils.py. Update re-implemented ImageNet-LT weights at the end of this page.
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.

Requirements

PyTorch (version >= 0.4.1)
scikit-learn

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

Benchmarks and Model Zoo

ImageNet-LT Open-Set Setting

Backbone	Many-Shot	Medium-Shot	Few-Shot	F-Measure	Download
ResNet-10	41.2	33.6	17.5	47.4	model

Places-LT Open-Set Setting

Backbone	Many-Shot	Medium-Shot	Few-Shot	F-Measure	Download
ResNet-152	38.5	37.0	21.8	48.5	model

CAUTION

The current code was prepared using single GPU. The use of multi-GPU can cause problems.

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}
}

tonysy / OpenLongTailRecognition-OLTR