[NIPS 2021] UOTA: Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration.

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Overview

UOTA: Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration

This repository is the official PyTorch implementation of UOTA (Unsupervised OuTlier Arbitration).

0 Requirements

  • Python 3.6
  • PyTorch install = 1.6.0
  • torchvision install = 0.7.0
  • CUDA 10.1
  • Apex with CUDA extension
  • Other dependencies: opencv-python, scipy, pandas, numpy

1 Pretraining

We release a demo to pretrain ResNet50 on ImageNet1K with SwAV+UOTA pretrained models.

1.1 SwAV+UOTA pretrain

To train SwAV+UOTA on a single node with 4 gpus for 200 epochs, run:

DATASET_PATH="path/to/ImageNet1K/train"
EXPERIMENT_PATH="path/to/experiment"

python -m torch.distributed.launch --nproc_per_node=4 main_uota.py \
--data_path ${DATASET_PATH} \
--nmb_crops 2 6 \
--size_crops 224 96 \
--min_scale_crops 0.14 0.05 \
--max_scale_crops 1. 0.14 \
--crops_for_assign 0 1 \
--use_pil_blur true \
--epochs 200 \
--warmup_epochs 0 \
--batch_size 64 \
--base_lr 0.6 \
--final_lr 0.0006 \
--uota_tau 350. \
--epoch_uota_starts 100 \
--wd 0.000001 \
--use_fp16 true \
--dist_url "tcp://localhost:40000" \
--arch uota_r50 \
--sync_bn pytorch \
--dump_path ${EXPERIMENT_PATH}

2 Linear Evaluation

To train a linear classifier on frozen features out of deep network pretrained via various self-supervised pretraining methods, run:

DATASET_PATH="path/to/ImageNet1K"
EXPERIMENT_PATH="path/to/experiment"
LINCLS_PATH="path/to/lincls"

python -m torch.distributed.launch --nproc_per_node=4 eval_linear.py \
--data_path ${DATASET_PATH} \
--arch resnet50 \
--lr 1.2 \
--dump_path ${LINCLS_PATH} \
--pretrained ${EXPERIMENT_PATH}/swav_uota_r50_e200_pretrained.pth \
--batch_size 64 \
--num_classes 100 \

3 Results

To compare with SwAV fairly, we provide a SwAV+UOTA model with ResNet-50 architecture pretrained on ImageNet1K for 200 epochs, and release the pretrained model and the linear classier.

method epochs batch-size multi-crop ImageNet1K top-1 acc. pretrained model linear classifier
SwAV 200 256 2x224 + 6x96 72.7 / /
SwAV + UOTA 200 256 2x224 + 6x96 73.5 pretrained linear

4 Citation

@InProceedings{wang2021NeurIPS,
  title={Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration},
  author={Wang, Yu and Lin, Jingyang and Zou, Jingjing and Pan, Yingwei and Yao, Ting and Mei, Tao},
  booktitle={NeurIPS},
  year={2021},
}
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