Merge branch 'master' of github.com:open-mmlab/OpenSelfSup
xiaohangzhan
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a465185fc9
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### ImageNet Linear Classification
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**NOte**
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**Note**
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* Config: `configs/benchmarks/linear_classification/imagenet/r50_multihead.py` for ImageNet (Multi) and `configs/benchmarks/linear_classification/imagenet/r50_moco.py` for ImageNet (Last).
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* For DeepCluster, use the corresponding one with `_sobel`.
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* ImageNet (Multi) evaluates features in around 9k dimensions from different layers. Top-1 result of the last epoch is reported.
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* ImageNet (Last) evaluates the last feature after global average pooling, e.g., 2048 dimensions for resnet50. The best top-1 result among all epochs is reported.
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<table><thead><tr><th rowspan="2">Method</th><th rowspan="2">Config</th><th rowspan="2">Remarks</th><th colspan="5">ImageNet (Multi)</th><th>ImageNet (Last)</th></tr><tr><td>feat1</td><td>feat2</td><td>feat3</td><td>feat4</td><td>feat5</td><td>avgpool</td></tr></thead><tbody><tr><td><a href="https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py" target="_blank" rel="noopener noreferrer">ImageNet</a></td><td>-</td><td>torchvision</td><td>15.18</td><td>33.96</td><td>47.86</td><td>67.56</td><td>76.17</td><td>74.12</td></tr><tr><td>Random</td><td>-</td><td>kaiming</td><td></td><td></td><td></td><td></td><td></td><td>4.35</td></tr><tr><td><a href="https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Doersch_Unsupervised_Visual_Representation_ICCV_2015_paper.pdf" target="_blank" rel="noopener noreferrer">Relative-Loc</a></td><td>selfsup/relative_loc/r50.py</td><td>default</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><a href="https://arxiv.org/abs/1803.07728" target="_blank" rel="noopener noreferrer">Rotation-Pred</a></td><td>selfsup/rotation_pred/r50.py</td><td>default</td><td>12.89</td><td>34.30</td><td>44.91</td><td>54.99</td><td>49.09</td><td>47.01</td></tr><tr><td><a href="https://arxiv.org/abs/1807.05520" target="_blank" rel="noopener noreferrer">DeepCluster</a></td><td>selfsup/deepcluster/r50.py</td><td>default</td><td></td><td></td><td></td><td></td><td></td><td>46.92</td></tr><tr><td><a href="https://arxiv.org/abs/1805.01978" target="_blank" rel="noopener noreferrer">NPID</a></td><td>selfsup/npid/r50.py</td><td>default</td><td>14.28</td><td>31.20</td><td>40.68</td><td>54.46</td><td>56.61</td><td>56.60</td></tr><tr><td><a href="http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhan_Online_Deep_Clustering_for_Unsupervised_Representation_Learning_CVPR_2020_paper.pdf" target="_blank" rel="noopener noreferrer">ODC</a></td><td>selfsup/odc/r50_v1.py</td><td>default</td><td>14.76</td><td>31.82</td><td>42.44</td><td>55.76</td><td>57.70</td><td>53.42</td></tr><tr><td><a href="https://arxiv.org/abs/1911.05722" target="_blank" rel="noopener noreferrer">MoCo</a></td><td>selfsup/moco/r50_v1.py</td><td>default</td><td>15.32</td><td>33.08</td><td>44.68</td><td>57.27</td><td>60.60</td><td>61.02</td></tr><tr><td><a href="https://arxiv.org/abs/2003.04297" target="_blank" rel="noopener noreferrer">MoCo v2</a></td><td>selfsup/moco/r50_v2.py</td><td>default</td><td>15.35</td><td>34.57</td><td>45.81</td><td>60.96</td><td>66.72</td><td>67.02</td></tr><tr><td></td><td>selfsup/moco/r50_v2_simclr_neck.py</td><td>-> SimCLR neck<br></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><a href="https://arxiv.org/abs/2002.05709" target="_blank" rel="noopener noreferrer">SimCLR</a></td><td>selfsup/simclr/r50_bs256_ep200.py</td><td>default</td><td>17.09</td><td>31.37</td><td>41.38</td><td>54.35</td><td>61.57</td><td>60.06</td></tr><tr><td></td><td>selfsup/simclr/r50_bs256_ep200_mocov2_neck.py</td><td>-> MoCo v2 neck</td><td>16.97</td><td>31.88</td><td>41.73</td><td>54.33</td><td>59.94</td><td>58.00</td></tr><tr><td><a href="https://arxiv.org/abs/2006.07733" target="_blank" rel="noopener noreferrer">BYOL</a></td><td>selfsup/byol/r50.py</td><td>default</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
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<table><thead><tr><th rowspan="2">Method</th><th rowspan="2">Config</th><th rowspan="2">Remarks</th><th colspan="5">ImageNet (Multi)</th><th>ImageNet (Last)</th></tr><tr><td>feat1</td><td>feat2</td><td>feat3</td><td>feat4</td><td>feat5</td><td>avgpool</td></tr></thead><tbody><tr><td><a href="https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py" target="_blank" rel="noopener noreferrer">ImageNet</a></td><td>-</td><td>torchvision</td><td>15.18</td><td>33.96</td><td>47.86</td><td>67.56</td><td>76.17</td><td>74.12</td></tr><tr><td>Random</td><td>-</td><td>kaiming</td><td></td><td></td><td></td><td></td><td></td><td>4.35</td></tr><tr><td><a href="https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Doersch_Unsupervised_Visual_Representation_ICCV_2015_paper.pdf" target="_blank" rel="noopener noreferrer">Relative-Loc</a></td><td>selfsup/relative_loc/r50.py</td><td>default</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><a href="https://arxiv.org/abs/1803.07728" target="_blank" rel="noopener noreferrer">Rotation-Pred</a></td><td>selfsup/rotation_pred/r50.py</td><td>default</td><td>12.89</td><td>34.30</td><td>44.91</td><td>54.99</td><td>49.09</td><td>47.01</td></tr><tr><td><a href="https://arxiv.org/abs/1807.05520" target="_blank" rel="noopener noreferrer">DeepCluster</a></td><td>selfsup/deepcluster/r50.py</td><td>default</td><td>12.78</td><td>30.81</td><td>43.88</td><td>57.71</td><td>51.68</td><td>46.92</td></tr><tr><td><a href="https://arxiv.org/abs/1805.01978" target="_blank" rel="noopener noreferrer">NPID</a></td><td>selfsup/npid/r50.py</td><td>default</td><td>14.28</td><td>31.20</td><td>40.68</td><td>54.46</td><td>56.61</td><td>56.60</td></tr><tr><td><a href="http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhan_Online_Deep_Clustering_for_Unsupervised_Representation_Learning_CVPR_2020_paper.pdf" target="_blank" rel="noopener noreferrer">ODC</a></td><td>selfsup/odc/r50_v1.py</td><td>default</td><td>14.76</td><td>31.82</td><td>42.44</td><td>55.76</td><td>57.70</td><td>53.42</td></tr><tr><td><a href="https://arxiv.org/abs/1911.05722" target="_blank" rel="noopener noreferrer">MoCo</a></td><td>selfsup/moco/r50_v1.py</td><td>default</td><td>15.32</td><td>33.08</td><td>44.68</td><td>57.27</td><td>60.60</td><td>61.02</td></tr><tr><td><a href="https://arxiv.org/abs/2003.04297" target="_blank" rel="noopener noreferrer">MoCo v2</a></td><td>selfsup/moco/r50_v2.py</td><td>default</td><td>15.35</td><td>34.57</td><td>45.81</td><td>60.96</td><td>66.72</td><td>67.02</td></tr><tr><td></td><td>selfsup/moco/r50_v2_simclr_neck.py</td><td>-> SimCLR neck<br></td><td>15.19</td><td>32.54</td><td>43.12</td><td>60.36</td><td>67.08</td><td></td></tr><tr><td><a href="https://arxiv.org/abs/2002.05709" target="_blank" rel="noopener noreferrer">SimCLR</a></td><td>selfsup/simclr/r50_bs256_ep200.py</td><td>default</td><td>17.09</td><td>31.37</td><td>41.38</td><td>54.35</td><td>61.57</td><td>60.06</td></tr><tr><td></td><td>selfsup/simclr/r50_bs256_ep200_mocov2_neck.py</td><td>-> MoCo v2 neck</td><td>16.97</td><td>31.88</td><td>41.73</td><td>54.33</td><td>59.94</td><td>58.00</td></tr><tr><td><a href="https://arxiv.org/abs/2006.07733" target="_blank" rel="noopener noreferrer">BYOL</a></td><td>selfsup/byol/r50.py</td><td>default</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
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### Place Linear Classification
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### Places205 Linear Classification
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Coming soon.
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@ -127,7 +127,7 @@ Coming soon.
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* 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.
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* Please use `--deterministic` in this benchmark.
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<table><thead><tr><th rowspan="2">Method</th><th rowspan="2">Config</th><th rowspan="2">Remarks</th><th rowspan="2">Optimal setting for ImageNet 1%</th><th colspan="2">ImageNet 1%</th></tr><tr><td>top-1</td><td>top-5</td></tr></thead><tbody><tr><td><a href="https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py" target="_blank" rel="noopener noreferrer">ImageNet</a></td><td>-</td><td>torchvision</td><td>r50_lr0_001_head100.py</td><td>68.68</td><td>88.87</td></tr><tr><td>Random</td><td>-</td><td>kaiming</td><td>r50_lr0_01_head1.py</td><td>1.56</td><td>4.99</td></tr><tr><td><a href="https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Doersch_Unsupervised_Visual_Representation_ICCV_2015_paper.pdf" target="_blank" rel="noopener noreferrer">Relative-Loc</a></td><td>selfsup/relative_loc/r50.py</td><td>default</td><td></td><td></td><td></td></tr><tr><td><a href="https://arxiv.org/abs/1803.07728" target="_blank" rel="noopener noreferrer">Rotation-Pred</a></td><td>selfsup/rotation_pred/r50.py</td><td>default</td><td>r50_lr0_01_head100.py</td><td>18.98</td><td>44.05</td></tr><tr><td><a href="https://arxiv.org/abs/1807.05520" target="_blank" rel="noopener noreferrer">DeepCluster</a></td><td>selfsup/deepcluster/r50.py</td><td>default</td><td>r50_lr0_01_head1_sobel.py</td><td>33.44</td><td>58.62</td></tr><tr><td><a href="https://arxiv.org/abs/1805.01978" target="_blank" rel="noopener noreferrer">NPID</a></td><td>selfsup/npid/r50.py</td><td>default</td><td>r50_lr0_01_head100.py</td><td>27.95</td><td>54.37</td></tr><tr><td><a href="http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhan_Online_Deep_Clustering_for_Unsupervised_Representation_Learning_CVPR_2020_paper.pdf" target="_blank" rel="noopener noreferrer">ODC</a></td><td>selfsup/odc/r50_v1.py</td><td>default</td><td>r50_lr0_1_head100.py</td><td>32.39</td><td>61.02</td></tr><tr><td><a href="https://arxiv.org/abs/1911.05722" target="_blank" rel="noopener noreferrer">MoCo</a></td><td>selfsup/moco/r50_v1.py</td><td>default</td><td>r50_lr0_01_head100.py</td><td>33.15</td><td>61.30</td></tr><tr><td><a href="https://arxiv.org/abs/2003.04297" target="_blank" rel="noopener noreferrer">MoCo v2</a></td><td>selfsup/moco/r50_v2.py</td><td>default</td><td>r50_lr0_01_head100.py</td><td>38.71</td><td>67.90</td></tr><tr><td></td><td>selfsup/moco/r50_v2_simclr_neck.py</td><td>-> SimCLR neck<br></td><td></td><td></td><td></td></tr><tr><td><a href="https://arxiv.org/abs/2002.05709" target="_blank" rel="noopener noreferrer">SimCLR</a></td><td>selfsup/simclr/r50_bs256_ep200.py</td><td>default</td><td>r50_lr0_01_head100.py</td><td>36.09</td><td>64.50</td></tr><tr><td></td><td>selfsup/simclr/r50_bs256_ep200_mocov2_neck.py</td><td>-> MoCo v2 neck</td><td></td><td></td><td></td></tr><tr><td><a href="https://arxiv.org/abs/2006.07733" target="_blank" rel="noopener noreferrer">BYOL</a></td><td>selfsup/byol/r50.py</td><td>default</td><td></td><td></td><td></td></tr></tbody></table>
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<table><thead><tr><th rowspan="2">Method</th><th rowspan="2">Config</th><th rowspan="2">Remarks</th><th rowspan="2">Optimal setting for ImageNet 1%</th><th colspan="2">ImageNet 1%</th></tr><tr><td>top-1</td><td>top-5</td></tr></thead><tbody><tr><td><a href="https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py" target="_blank" rel="noopener noreferrer">ImageNet</a></td><td>-</td><td>torchvision</td><td>r50_lr0_001_head100.py</td><td>68.68</td><td>88.87</td></tr><tr><td>Random</td><td>-</td><td>kaiming</td><td>r50_lr0_01_head1.py</td><td>1.56</td><td>4.99</td></tr><tr><td><a href="https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Doersch_Unsupervised_Visual_Representation_ICCV_2015_paper.pdf" target="_blank" rel="noopener noreferrer">Relative-Loc</a></td><td>selfsup/relative_loc/r50.py</td><td>default</td><td></td><td></td><td></td></tr><tr><td><a href="https://arxiv.org/abs/1803.07728" target="_blank" rel="noopener noreferrer">Rotation-Pred</a></td><td>selfsup/rotation_pred/r50.py</td><td>default</td><td>r50_lr0_01_head100.py</td><td>18.98</td><td>44.05</td></tr><tr><td><a href="https://arxiv.org/abs/1807.05520" target="_blank" rel="noopener noreferrer">DeepCluster</a></td><td>selfsup/deepcluster/r50.py</td><td>default</td><td>r50_lr0_01_head1_sobel.py</td><td>33.44</td><td>58.62</td></tr><tr><td><a href="https://arxiv.org/abs/1805.01978" target="_blank" rel="noopener noreferrer">NPID</a></td><td>selfsup/npid/r50.py</td><td>default</td><td>r50_lr0_01_head100.py</td><td>27.95</td><td>54.37</td></tr><tr><td><a href="http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhan_Online_Deep_Clustering_for_Unsupervised_Representation_Learning_CVPR_2020_paper.pdf" target="_blank" rel="noopener noreferrer">ODC</a></td><td>selfsup/odc/r50_v1.py</td><td>default</td><td>r50_lr0_1_head100.py</td><td>32.39</td><td>61.02</td></tr><tr><td><a href="https://arxiv.org/abs/1911.05722" target="_blank" rel="noopener noreferrer">MoCo</a></td><td>selfsup/moco/r50_v1.py</td><td>default</td><td>r50_lr0_01_head100.py</td><td>33.15</td><td>61.30</td></tr><tr><td><a href="https://arxiv.org/abs/2003.04297" target="_blank" rel="noopener noreferrer">MoCo v2</a></td><td>selfsup/moco/r50_v2.py</td><td>default</td><td>r50_lr0_01_head100.py</td><td>38.71</td><td>67.90</td></tr><tr><td></td><td>selfsup/moco/r50_v2_simclr_neck.py</td><td>-> SimCLR neck<br></td><td>r50_lr0_01_head100.py</td><td>31.37<br></td><td>59.65</td></tr><tr><td><a href="https://arxiv.org/abs/2002.05709" target="_blank" rel="noopener noreferrer">SimCLR</a></td><td>selfsup/simclr/r50_bs256_ep200.py</td><td>default</td><td>r50_lr0_01_head100.py</td><td>36.09</td><td>64.50</td></tr><tr><td></td><td>selfsup/simclr/r50_bs256_ep200_mocov2_neck.py</td><td>-> MoCo v2 neck</td><td>r50_lr0_01_head100.py</td><td>36.31</td><td>64.68</td></tr><tr><td><a href="https://arxiv.org/abs/2006.07733" target="_blank" rel="noopener noreferrer">BYOL</a></td><td>selfsup/byol/r50.py</td><td>default</td><td></td><td></td><td></td></tr></tbody></table>
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<table><thead><tr><th rowspan="2">Method</th><th rowspan="2">Config</th><th rowspan="2">Remarks</th><th rowspan="2">Optimal setting for ImageNet 1%</th><th colspan="2">ImageNet 1%</th></tr><tr><td>top-1</td><td>top-5</td></tr></thead><tbody><tr><td><a href="https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py" target="_blank" rel="noopener noreferrer">ImageNet</a></td><td>-</td><td>torchvision</td><td>r50_lr0_01_head1.py</td><td>63.10</td><td>85.73</td></tr><tr><td>Random</td><td>-</td><td>kaiming</td><td>r50_lr0_01_head1.py</td><td>1.56</td><td>4.99</td></tr><tr><td><a href="https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Doersch_Unsupervised_Visual_Representation_ICCV_2015_paper.pdf" target="_blank" rel="noopener noreferrer">Relative-Loc</a></td><td>selfsup/relative_loc/r50.py</td><td>default</td><td></td><td></td><td></td></tr><tr><td><a href="https://arxiv.org/abs/1803.07728" target="_blank" rel="noopener noreferrer">Rotation-Pred</a></td><td>selfsup/rotation_pred/r50.py</td><td>default</td><td>r50_lr0_01_head100.py</td><td>18.98</td><td>44.05</td></tr><tr><td><a href="https://arxiv.org/abs/1807.05520" target="_blank" rel="noopener noreferrer">DeepCluster</a></td><td>selfsup/deepcluster/r50.py</td><td>default</td><td>r50_lr0_01_head1_sobel.py</td><td>33.44</td><td>58.62</td></tr><tr><td><a href="https://arxiv.org/abs/1805.01978" target="_blank" rel="noopener noreferrer">NPID</a></td><td>selfsup/npid/r50.py</td><td>default</td><td>r50_lr0_01_head100.py</td><td>27.95</td><td>54.37</td></tr><tr><td><a href="http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhan_Online_Deep_Clustering_for_Unsupervised_Representation_Learning_CVPR_2020_paper.pdf" target="_blank" rel="noopener noreferrer">ODC</a></td><td>selfsup/odc/r50_v1.py</td><td>default</td><td>r50_lr0_1_head100.py</td><td>32.39</td><td>61.02</td></tr><tr><td><a href="https://arxiv.org/abs/1911.05722" target="_blank" rel="noopener noreferrer">MoCo</a></td><td>selfsup/moco/r50_v1.py</td><td>default</td><td>r50_lr0_01_head100.py</td><td>33.15</td><td>61.30</td></tr><tr><td><a href="https://arxiv.org/abs/2003.04297" target="_blank" rel="noopener noreferrer">MoCo v2</a></td><td>selfsup/moco/r50_v2.py</td><td>default</td><td>r50_lr0_01_head100.py</td><td>38.71</td><td>67.90</td></tr><tr><td></td><td>selfsup/moco/r50_v2_simclr_neck.py</td><td>-> SimCLR neck<br></td><td></td><td></td><td></td></tr><tr><td><a href="https://arxiv.org/abs/2002.05709" target="_blank" rel="noopener noreferrer">SimCLR</a></td><td>selfsup/simclr/r50_bs256_ep200.py</td><td>default</td><td>r50_lr0_01_head100.py</td><td>36.09</td><td>64.50</td></tr><tr><td></td><td>selfsup/simclr/r50_bs256_ep200_mocov2_neck.py</td><td>-> MoCo v2 neck</td><td></td><td></td><td></td></tr><tr><td><a href="https://arxiv.org/abs/2006.07733" target="_blank" rel="noopener noreferrer">BYOL</a></td><td>selfsup/byol/r50.py</td><td>default</td><td></td><td></td><td></td></tr></tbody></table>
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