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SimCLR¶
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¶
Back to model_zoo.md to download models.
In this page, we provide benchmarks as much as possible to evaluate our pre-trained models. If not mentioned, all models are pre-trained on ImageNet-1k dataset.
Classification¶
The classification benchmarks includes 4 downstream task datasets, VOC, ImageNet, iNaturalist2018 and Places205. If not specified, the results are Top-1 (%).
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 |
|---|---|---|---|---|---|---|---|---|---|---|
| resnet50_8xb32-coslr-200e | 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 resnet50_mhead_linear-8xb32-steplr-90e_in1k for details of config.
The AvgPool result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to resnet50_linear-8xb512-coslr-90e_in1k for details of config.
| Self-Supervised Config | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
|---|---|---|---|---|---|---|
| resnet50_8xb32-coslr-200e | 16.29 | 31.11 | 39.99 | 55.06 | 62.91 | 62.56 |
| resnet50_16xb256-coslr-200e | 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 resnet50_mhead_8xb32-steplr-28e_places205.py for details of config.
| Self-Supervised Config | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 |
|---|---|---|---|---|---|
| resnet50_8xb32-coslr-200e | 20.60 | 33.62 | 38.86 | 45.25 | 50.91 |
ImageNet Nearest-Neighbor Classification¶
The results are obtained from the features after GlobalAveragePooling. Here, k=10 to 200 indicates different number of nearest neighbors.
| Self-Supervised Config | k=10 | k=20 | k=100 | k=200 |
|---|---|---|---|---|
| resnet50_8xb32-coslr-200e | 47.8 | 48.4 | 46.7 | 45.2 |
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 |
|---|---|
| resnet50_8xb32-coslr-200e | 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) |
|---|---|---|---|---|---|---|
| resnet50_8xb32-coslr-200e | 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 |
|---|---|
| resnet50_8xb32-coslr-200e | 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},
}