github30 / crest

Repo for CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning

CReST in Tensorflow 2

Code for the paper: "CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning" by Chen Wei, Kihyuk Sohn, Clayton Mellina, Alan Yuille and Fan Yang.

• This is not an officially supported Google product.

Install dependencies

sudo apt install python3-dev python3-virtualenv python3-tk imagemagick
virtualenv -p python3 --system-site-packages env3
. env3/bin/activate
pip install -r requirements.txt

• The code has been tested on Ubuntu 18.04 with CUDA 10.2.

Environment setting

. env3/bin/activate
export ML_DATA=/path/to/your/data
export ML_DIR=/path/to/your/code
export RESULT=/path/to/your/result
export PYTHONPATH=$PYTHONPATH:$ML_DIR

Datasets

Download or generate the datasets as follows:

• CIFAR10 and CIFAR100: Follow the steps to download and generate balanced CIFAR10 and CIFAR100 datasets. Put it under ${ML_DATA}/cifar, for example, ${ML_DATA}/cifar/cifar10-test.tfrecord.
• Long-tailed CIFAR10 and CIFAR100: Follow the steps to download the datasets prepared by Cui et al. Put it under ${ML_DATA}/cifar-lt, for example, ${ML_DATA}/cifar-lt/cifar-10-data-im-0.1.

Running experiment on Long-tailed CIFAR10, CIFAR100

Run MixMatch (paper) and FixMatch (paper):

• Specify method to run via --method. It can be fixmatch or mixmatch.

• Specify dataset via --dataset. It can be cifar10lt or cifar100lt.

• Specify the class imbalanced ratio, i.e., the number of training samples from the most minority class over that from the most majority class, via --class_im_ratio.

• Specify the percentage of labeled data via --percent_labeled.

• Specify the number of generations for self-training via --num_generation.

• Specify whether to use distribution alignment via --do_distalign.

• Specify the initial distribution alignment temperature via --dalign_t.

• Specify how distribution alignment is applied via --how_dalign. It can be constant or adaptive.

  python -m train_and_eval_loop \
    --model_dir=/tmp/model \
    --method=fixmatch \
    --dataset=cifar10lt \
    --input_shape=32,32,3 \
    --class_im_ratio=0.01 \
    --percent_labeled=0.1 \
    --fold=1 \
    --num_epoch=64 \
    --num_generation=6 \
    --sched_level=1 \
    --dalign_t=0.5 \
    --how_dalign=adaptive \
    --do_distalign=True

Results

The code reproduces main results of the paper. For all settings and methods, we run experiments on 5 different folds and report the mean and standard deviations. Note that the numbers may not exactly match those from the papers as there are extra randomness coming from the training.

Results on Long-tailed CIFAR10 with 10% labeled data (Table 1 in the paper).

	gamma=50	gamma=100	gamma=200
FixMatch	79.4 (0.98)	66.2 (0.83)	59.9 (0.44)
CReST	83.7 (0.40)	75.4 (1.62)	63.9 (0.67)
CReST+	84.5 (0.41)	77.7 (1.22)	67.5 (1.36)

Training with Multiple GPUs

• Simply set CUDA_VISIBLE_DEVICES=0,1,2,3 or any number of GPUs.
• Make sure that batch size is divisible by the number of GPUs.

Augmentation

• One can concatenate different augmentation shortkeys to compose an augmentation sequence.
  • d: default augmentation, resize and shift.
  • h: horizontal flip.
  • ra: random augment with all augmentation ops.
  • rc: random augment with color augmentation ops only.
  • rg: random augment with geometric augmentation ops only.
  • c: cutout.
  • For example, dhrac applies shift, flip, random augment with all ops, followed by cutout.

Citing this work

@article{wei2021crest,
    title={CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning},
    author={Chen Wei and Kihyuk Sohn and Clayton Mellina and Alan Yuille and Fan Yang},
    journal={arXiv preprint arXiv:2102.09559},
    year={2021},
}