mmselfsup/configs/selfsup/mocov2/README.md

MoCo v2

Improved Baselines with Momentum Contrastive Learning

Abstract

Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR’s design improvements by implementing them in the MoCo framework. With simple modifications to MoCo—namely, using an MLP projection head and more data augmentation—we establish stronger baselines that outperform SimCLR and do not require large training batches. We hope this will make state-of-the-art unsupervised learning research more accessible.

Models and Benchmarks

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	84.04	43.14	53.29	65.34	71.03	75.42	78.48	80.88	82.23

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.

Self-Supervised Config	Feature1	Feature2	Feature3	Feature4	Feature5
resnet50_8xb32-coslr-200e	15.96	34.22	45.78	61.11	66.24

Algorithm	Backbone	Epoch	Batch Size	Results (Top-1 %)		Links
				Linear Eval	Fine-tuning	Pretrain	Linear Eval	Fine-tuning
MoCo v2	ResNet50	200	256	67.5	/	config | model | log	config | model | log	/

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	20.92	35.72	42.62	49.79	52.25

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	55.6	55.7	53.7	52.5

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 config for details.

Self-Supervised Config	AP50
resnet50_8xb32-coslr-200e	81.06

COCO2017

Please refer to config for details.

Self-Supervised Config	mAP(Box)	AP50(Box)	AP75(Box)	mAP(Mask)	AP50(Mask)	AP75(Mask)
resnet50_8xb32-coslr-200e	40.2	59.7	44.2	36.1	56.7	38.8

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 config for details.

Self-Supervised Config	mIOU
resnet50_8xb32-coslr-200e	67.55

Citation

@article{chen2020improved,
  title={Improved baselines with momentum contrastive learning},
  author={Chen, Xinlei and Fan, Haoqi and Girshick, Ross and He, Kaiming},
  journal={arXiv preprint arXiv:2003.04297},
  year={2020}
}