132 lines
9.3 KiB
Markdown
132 lines
9.3 KiB
Markdown
# MoCo v2
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> [Improved Baselines with Momentum Contrastive Learning](https://arxiv.org/abs/2003.04297)
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<!-- [ALGORITHM] -->
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## Abstract
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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.
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<div align="center">
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<img src="https://user-images.githubusercontent.com/36138628/149720067-b65e5736-d425-45b3-93ed-6f2427fc6217.png" width="500" />
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</div>
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## Models and Benchmarks
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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.
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### Classification
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The classification benchmarks includes 4 downstream task datasets, **VOC**, **ImageNet**, **iNaturalist2018** and **Places205**. If not specified, the results are Top-1 (%).
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#### VOC SVM / Low-shot SVM
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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).
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Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.
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| Self-Supervised Config | Best Layer | SVM | k=1 | k=2 | k=4 | k=8 | k=16 | k=32 | k=64 | k=96 |
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| --------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- |
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| [resnet50_8xb32-coslr-200e](https://github.com/open-mmlab/mmselfsup/blob/1.x/configs/selfsup/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k.py) | feature5 | 84.04 | 43.14 | 53.29 | 65.34 | 71.03 | 75.42 | 78.48 | 80.88 | 82.23 |
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#### ImageNet Linear Evaluation
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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](https://github.com/open-mmlab/mmselfsup/blob/1.x/configs/benchmarks/classification/imagenet/resnet50_mhead_linear-8xb32-steplr-90e_in1k.py) for details of config.
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| Self-Supervised Config | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 |
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| --------------------------------------------------------------------------------------------------------------------------------------------- | -------- | -------- | -------- | -------- | -------- |
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| [resnet50_8xb32-coslr-200e](https://github.com/open-mmlab/mmselfsup/blob/1.x/configs/selfsup/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k.py) | 15.96 | 34.22 | 45.78 | 61.11 | 66.24 |
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<table class="docutils">
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<thead>
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<tr>
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<th rowspan="2">Algorithm</th>
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<th rowspan="2">Backbone</th>
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<th rowspan="2">Epoch</th>
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<th rowspan="2">Batch Size</th>
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<th colspan="2" align="center">Results (Top-1 %)</th>
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<th colspan="3" align="center">Links</th>
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</tr>
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<tr>
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<th>Linear Eval</th>
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<th>Fine-tuning</th>
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<th>Pretrain</th>
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<th>Linear Eval</th>
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<th>Fine-tuning</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>MoCo v2</td>
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<td>ResNet50</td>
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<td>200</td>
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<td>256</td>
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<td>67.5</td>
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<td>/</td>
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<td><a href='https://github.com/open-mmlab/mmselfsup/blob/dev-1.x/configs/selfsup/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k.py'>config</a> | <a href='https://download.openmmlab.com/mmselfsup/1.x/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k/mocov2_resnet50_8xb32-coslr-200e_in1k_20220825-b6d23c86.pth'>model</a> | <a href='https://download.openmmlab.com/mmselfsup/1.x/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k/mocov2_resnet50_8xb32-coslr-200e_in1k_20220721_215805.json'>log</a></td>
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<td><a href='https://github.com/open-mmlab/mmselfsup/blob/dev-1.x/configs/benchmarks/classification/imagenet/resnet50_linear-8xb32-steplr-100e_in1k.py'>config</a> | <a href='https://download.openmmlab.com/mmselfsup/1.x/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k/resnet50_linear-8xb32-steplr-100e_in1k/resnet50_linear-8xb32-steplr-100e_in1k_20220825-994c4128.pth'>model</a> | <a href='https://download.openmmlab.com/mmselfsup/1.x/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k/resnet50_linear-8xb32-steplr-100e_in1k/resnet50_linear-8xb32-steplr-100e_in1k_20220724_172046.json'>log</a></td>
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<td>/</td>
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</tr>
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</tbody>
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</table>
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#### Places205 Linear Evaluation
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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](https://github.com/open-mmlab/mmselfsup/blob/1.x/configs/benchmarks/classification/places205/resnet50_mhead_8xb32-steplr-28e_places205.py) for details of config.
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| Self-Supervised Config | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 |
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| --------------------------------------------------------------------------------------------------------------------------------------------- | -------- | -------- | -------- | -------- | -------- |
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| [resnet50_8xb32-coslr-200e](https://github.com/open-mmlab/mmselfsup/blob/1.x/configs/selfsup/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k.py) | 20.92 | 35.72 | 42.62 | 49.79 | 52.25 |
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#### ImageNet Nearest-Neighbor Classification
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The results are obtained from the features after GlobalAveragePooling. Here, k=10 to 200 indicates different number of nearest neighbors.
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| Self-Supervised Config | k=10 | k=20 | k=100 | k=200 |
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| --------------------------------------------------------------------------------------------------------------------------------------------- | ---- | ---- | ----- | ----- |
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| [resnet50_8xb32-coslr-200e](https://github.com/open-mmlab/mmselfsup/blob/1.x/configs/selfsup/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k.py) | 55.6 | 55.7 | 53.7 | 52.5 |
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### Detection
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The detection benchmarks includes 2 downstream task datasets, **Pascal VOC 2007 + 2012** and **COCO2017**. This benchmark follows the evluation protocols set up by MoCo.
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#### Pascal VOC 2007 + 2012
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Please refer to [config](https://github.com/open-mmlab/mmselfsup/blob/1.x/configs/benchmarks/mmdetection/voc0712/faster-rcnn_r50-c4_ms-24k_voc0712.py) for details.
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| Self-Supervised Config | AP50 |
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| --------------------------------------------------------------------------------------------------------------------------------------------- | ----- |
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| [resnet50_8xb32-coslr-200e](https://github.com/open-mmlab/mmselfsup/blob/1.x/configs/selfsup/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k.py) | 81.06 |
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#### COCO2017
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Please refer to [config](https://github.com/open-mmlab/mmselfsup/blob/1.x/configs/benchmarks/mmdetection/coco/mask-rcnn_r50_fpn_ms-1x_coco.py) for details.
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| Self-Supervised Config | mAP(Box) | AP50(Box) | AP75(Box) | mAP(Mask) | AP50(Mask) | AP75(Mask) |
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| --------------------------------------------------------------------------------------------------------------------------------------------- | -------- | --------- | --------- | --------- | ---------- | ---------- |
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| [resnet50_8xb32-coslr-200e](https://github.com/open-mmlab/mmselfsup/blob/1.x/configs/selfsup/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k.py) | 40.2 | 59.7 | 44.2 | 36.1 | 56.7 | 38.8 |
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### Segmentation
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The segmentation benchmarks includes 2 downstream task datasets, **Cityscapes** and **Pascal VOC 2012 + Aug**. It follows the evluation protocols set up by MMSegmentation.
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#### Pascal VOC 2012 + Aug
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Please refer to [config](https://github.com/open-mmlab/mmselfsup/blob/1.x/configs/benchmarks/mmsegmentation/voc12aug/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py) for details.
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| Self-Supervised Config | mIOU |
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| --------------------------------------------------------------------------------------------------------------------------------------------- | ----- |
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| [resnet50_8xb32-coslr-200e](https://github.com/open-mmlab/mmselfsup/blob/1.x/configs/selfsup/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k.py) | 67.55 |
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## Citation
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```bibtex
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@article{chen2020improved,
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title={Improved baselines with momentum contrastive learning},
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author={Chen, Xinlei and Fan, Haoqi and Girshick, Ross and He, Kaiming},
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journal={arXiv preprint arXiv:2003.04297},
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year={2020}
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}
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```
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