Large-Scale Long-Tailed Recognition in an Open World

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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

• 03/04/2020: We changed all valirables named selfatt to modulatedatt so that the attention module can be properly trained in the second stage for Places-LT. ImageNet-LT does not have this problem since the weights are not freezed. We have updated new results using fixed code, which is still better than reported. The weights are also updated. Thanks!
• 02/11/2020: We updated configuration files for Places_LT dataset. The current results are a little bit higher than reported, even with updated F-measure calculation. One important thing to be considered is that we have unfrozon the model weights for the first stage training of Places-LT, which means it is not suitable for single-GPU training in most cases (we used 4 1080ti in our implementation). However, for the second stage, since the memory and center loss do not support multi-GPUs currently, please switch back to single-GPU training. Thank you very much!
• 01/29/2020: We updated the False Positive calculation in util.py so that the numbers are normal again. The reported F-measure numbers in the paper might be a little bit higher than actual numbers for all baselines. We will update it as soon as possible. We have updated the new F-measure number in the following table. Thanks.
• 12/19/2019: Updated modules with 'clone()' methods and set use_fc in ImageNet-LT stage-1 config to False. Currently, the results for ImageNet-LT is comparable to reported numbers in the paper (a little bit better), and the reproduced results are updated below. We also found the bug in Places-LT. We will update the code and reproduced results as soon as possible.
• 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 (At this stage, multi-GPU might be necessary since we are finetuning a ResNet-152.):

python main.py --config ./config/Places_LT/stage_1.py

• Stage 2 training (At this stage, only single-GPU is supported, please switch back to single-GPU 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

Reproduced Benchmarks and Model Zoo (Updated on 03/05/2020)

ImageNet-LT Open-Set Setting

Backbone	Many-Shot	Medium-Shot	Few-Shot	F-Measure	Download
ResNet-10	44.2	35.2	17.5	44.6	model

Places-LT Open-Set Setting

Backbone	Many-Shot	Medium-Shot	Few-Shot	F-Measure	Download
ResNet-152	43.7	40.2	28.0	50.0	model

CAUTION

The current code was prepared using single GPU. The use of multi-GPU can cause problems except for the first stage of Places-LT.

License and Citation

The use of this software is released under BSD-3.

@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}
}