MoCo v1 / v2¶

Momentum Contrast for Unsupervised Visual Representation Learning (MoCo v1)¶

We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks.

Citation¶

@inproceedings{he2020momentum,
  title={Momentum contrast for unsupervised visual representation learning},
  author={He, Kaiming and Fan, Haoqi and Wu, Yuxin and Xie, Saining and Girshick, Ross},
  booktitle={CVPR},
  year={2020}
}

Improved Baselines with Momentum Contrastive Learning (MoCo v2)¶

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.

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}
}

Models and Benchmarks¶

Back to model_zoo.md

In this page, we provide benchmarks as much as possible to evaluate our pre-trained models. If not mentioned, all models were trained on ImageNet1k dataset.

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.

Model	Config	Best Layer	SVM	k=1	k=2	k=4	k=8	k=16	k=32	k=64	k=96
model	mocov2_resnet50_8xb32-coslr-200e	feature5	84.04

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_8xb32-steplr-90e.py for details of config.

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

Model	Config	Feature1	Feature2	Feature3	Feature4	Feature5	AvgPool
model	mocov2_resnet50_8xb32-coslr-200e	15.96	34.22	45.78	61.11	66.24	67.56

iNaturalist2018 Linear Evaluation¶

Please refer to resnet50_mhead_8xb32-steplr-84e_inat18.py and file name for details of config.

Model	Config	Feature1	Feature2	Feature3	Feature4	Feature5	AvgPool
model	mocov2_resnet50_8xb32-coslr-200e

Places205 Linear Evaluation¶

Please refer to resnet50_mhead_8xb32-steplr-28e_places205.py and file name for details of config.

Model	Config	Feature1	Feature2	Feature3	Feature4	Feature5	AvgPool
model	mocov2_resnet50_8xb32-coslr-200e

Semi-Supervised Classification¶

In this benchmark, the necks or heads are removed and only the backbone CNN is evaluated by appending a linear classification head. All parameters are fine-tuned.
When training with 1% ImageNet, we find hyper-parameters especially the learning rate greatly influence the performance. Hence, we prepare a list of settings with the base learning rate from {0.001, 0.01, 0.1} and the learning rate multiplier for the head from {1, 10, 100}. We choose the best performing setting for each method. The setting of parameters are indicated in the file name. The learning rate is indicated like 1e-1, 1e-2, 1e-3 and the learning rate multiplier is indicated like head1, head10, head100.
Please use –deterministic in this benchmark.

Please refer to the directories configs/benchmarks/classification/imagenet/imagenet_1percent/ of 1% data and configs/benchmarks/classification/imagenet/imagenet_10percent/ 10% data for details.

Model	Pretrain Config	Fine-tuned Config	Top-1 (%)	Top-5 (%)
model	mocov2_resnet50_8xb32-coslr-200e	resnet50_head100_4xb64-steplr1e-2-20e_in1k-1pct.py	38.97	67.69

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.py for details of config.

Model	Config	mAP	AP50
model	mocov2_resnet50_8xb32-coslr-200e

COCO2017¶

Please refer to mask_rcnn_r50_fpn_mstrain_1x.py for details of config.

Model	Config	mAP(Box)	AP50(Box)	AP75(Box)	mAP(Mask)	AP50(Mask)	AP75(Mask)
model	mocov2_resnet50_8xb32-coslr-200e

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 file for details of config.

Model	Config	mIOU
model	mocov2_resnet50_8xb32-coslr-200e

Cityscapes¶

Please refer to file for details of config.

Model	Config	mIOU
model	mocov2_resnet50_8xb32-coslr-200e