SimCLR

A Simple Framework for Contrastive Learning of Visual Representations

Abstract

This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50.

Results and Models

This page is based on documents in MMSelfSup.

Classification

The classification benchmarks includes 4 downstream task datasets, VOC, ImageNet, iNaturalist2018 and Places205. If not specified, the results are Top-1 (%). We also provide configs on CIFAR-10, CIFAR-100, and ImageNet-100 datasets according to the setting on ImageNet.

VOC SVM / Low-shot SVM

The Best Layer indicates that the best results are obtained from which layers feature map. For example, if the Best Layer is feature3, its best result is obtained from the second stage of ResNet (1 for stem layer, 2-5 for 4 stage layers).

Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.

Self-Supervised Config	Best Layer	SVM	k=1	k=2	k=4	k=8	k=16	k=32	k=64	k=96
r50_8xb64_cos_lr0_6_fp16_ep200	feature5	79.98	35.02	42.79	54.87	61.91	67.38	71.88	75.56	77.4

ImageNet Linear Evaluation

The Feature1 - Feature5 don't have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to r50_mhead_sz224_4xb64_step_ep90.py for details of config.

The AvgPool result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to r50_linear_sz224_4xb64_step_ep100.py for details of config.

Self-Supervised Config	Feature1	Feature2	Feature3	Feature4	Feature5	AvgPool
r50_8xb64_cos_lr0_6_fp16_ep200	16.29	31.11	39.99	55.06	62.91	62.56
r50_16xb256_cos_lr4_8_fp16_ep200	15.44	31.47	41.83	59.44	66.41	66.66

Places205 Linear Evaluation

The Feature1 - Feature5 don't have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to r50_mhead_sz224_4xb64_step_ep28 for details of config.

Self-Supervised Config	Feature1	Feature2	Feature3	Feature4	Feature5
r50_8xb64_cos_lr0_6_fp16_ep200	20.60	33.62	38.86	45.25	50.91

Detection

The detection benchmarks includes 2 downstream task datasets, Pascal VOC 2007 + 2012 and COCO2017. This benchmark follows the evluation protocols set up by MoCo.

Pascal VOC 2007 + 2012

Please refer to faster_rcnn_r50_c4_mstrain_24k_voc0712.py for details of config.

Self-Supervised Config	AP50
r50_8xb64_cos_lr0_6_fp16_ep200	79.38

COCO2017

Please refer to mask_rcnn_r50_fpn_mstrain_1x_coco.py for details of config.

Self-Supervised Config	mAP(Box)	AP50(Box)	AP75(Box)	mAP(Mask)	AP50(Mask)	AP75(Mask)
r50_8xb64_cos_lr0_6_fp16_ep200	38.7	58.1	42.4	34.9	55.3	37.5

Segmentation

The segmentation benchmarks includes 2 downstream task datasets, Cityscapes and Pascal VOC 2012 + Aug. It follows the evluation protocols set up by MMSegmentation.

Pascal VOC 2012 + Aug

Please refer to fcn_r50-d8_512x512_20k_voc12aug.py for details of config.

Self-Supervised Config	mIOU
r50_8xb64_cos_lr0_6_fp16_ep200	64.03

Citation

@inproceedings{chen2020simple,
  title={A simple framework for contrastive learning of visual representations},
  author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  booktitle={ICML},
  year={2020},
}