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# Model Zoo
## Pre-trained model download links and speed test.
**Note**
* The testing GPUs are NVIDIA Tesla V100.
* Experiments with the same batch size are directly comparable in speed.
<table><thead><tr><th>Method</th><th>Config</th><th>Remarks</th><th>Download link</th><th>Batch size</th><th>Epochs</th><th>Time per epoch</th></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><a href="https://drive.google.com/file/d/11xA3TOcbD0qOrwpBfYonEDeseE1wMfBh/view?usp=sharing" target="_blank" rel="noopener noreferrer">imagenet_r50-21352794.pth</a></td><td>-</td><td>-</td><td>-</td></tr>
<tr><td>Random</td><td>-</td><td>kaiming</td><td><a href="https://drive.google.com/file/d/1UaFTjd6sbKkZEE-f58Zv30bnx7C1qJBb/view?usp=sharing" target="_blank" rel="noopener noreferrer">random_r50-5d0fa71b.pth</a></td><td>-</td><td>-</td><td>-</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><a href="https://drive.google.com/file/d/1ibk1BI3PFQxZqcxuDfHs3n7JnWKCgl8x/view?usp=sharing" target="_blank" rel="noopener noreferrer">relative_loc_r50-342c9097.pth</a></td><td>512</td><td>70</td><td>21min17s</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><a href="https://drive.google.com/file/d/1t3oClmIvQ0p8RZ0V5yvQFltzjqBO823Y/view?usp=sharing" target="_blank" rel="noopener noreferrer">rotation_r50-cfab8ebb.pth</a></td><td>128</td><td>70</td><td>49min58s</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><a href="https://drive.google.com/file/d/1GxgP7pI18JtFxDIC0hnHOanvUYajoLlg/view?usp=sharing" target="_blank" rel="noopener noreferrer">deepcluster_r50-bb8681e2.pth</a></td><td>512</td><td>200</td><td>41min57s</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><a href="https://drive.google.com/file/d/1sm6I3Y5XnCWdbmeLSF4YupUtPe5nRQMI/view?usp=sharing" target="_blank" rel="noopener noreferrer">npid_r50-dec3df0c.pth</a></td><td>256</td><td>200</td><td>20min5s</td></tr>
<tr><td></td><td>selfsup/npid/r50_ensure_neg.py</td><td>ensure_neg=True</td><td><a href="https://drive.google.com/file/d/1FldDrb6kzF3CZ7737mwCXVI6HE2aCSaF/view?usp=sharing" target="_blank" rel="noopener noreferrer">npid_r50_ensure_neg-ce09b7ae.pth</a></td><td></td><td></td><td></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><a href="https://drive.google.com/file/d/1EdhJeZAyMsD_pEW7uMhLzos5xZLdariN/view?usp=sharing" target="_blank" rel="noopener noreferrer">odc_r50_v1-5af5dd0c.pth</a></td><td>512</td><td>440</td><td>28min22s</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><a href="https://drive.google.com/file/d/1ANXfnoT8yBQQBBqR_kQLQorK20l65KMy/view?usp=sharing" target="_blank" rel="noopener noreferrer">moco_r50_v1-4ad89b5c.pth</a></td><td>256</td><td>200</td><td>22min58s</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><a href="https://drive.google.com/file/d/1ImO8A3uWbrTx21D1IqBDMUQvpN6wmv0d/view?usp=sharing" target="_blank" rel="noopener noreferrer">moco_r50_v2-e3b0c442.pth</a></td><td>256</td><td>200</td><td>55min43s</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><a href="https://drive.google.com/file/d/1aZ43nSdivdNxHbM9DKVoZYVhZ8TNnmPp/view?usp=sharing" target="_blank" rel="noopener noreferrer">simclr_r50_bs256_ep200-4577e9a6.pth</a></td><td>256</td><td>200</td><td>1h1min7s</td></tr>
<tr><td></td><td>selfsup/simclr/r50_bs256_ep200_mocov2_neck.py</td><td>-&gt; MoCo v2 neck</td><td><a href="https://drive.google.com/file/d/1AXpSKqgWfnj6jCgN65BXSTCKFfuIVELa/view?usp=sharing" target="_blank" rel="noopener noreferrer">simclr_r50_bs256_ep200_mocov2_neck-0d6e5ff2.pth</a></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_bs4096_ep200.py</td><td>default</td><td><a href="https://drive.google.com/file/d/1Whj3j5E3ShQj_VufjrJSzWiq1xcZZCXN/view?usp=sharing" target="_blank" rel="noopener noreferrer">byol_r50-e3b0c442.pth</a></td><td>4096</td><td>200</td><td>14min40s</td></tr>
</tbody></table>
## Benchmarks
### VOC07 SVM & SVM Low-shot
<table><thead><tr><th rowspan="2">Method</th><th rowspan="2">Config</th><th rowspan="2">Remarks</th><th rowspan="2">Best layer</th><th rowspan="2">VOC07 SVM</th><th colspan="8">VOC07 SVM Low-shot</th></tr>
<tr><td>1</td><td>2</td><td>4</td><td>8</td><td>16</td><td>32</td><td>64</td><td>96</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>feat5</td><td>87.17</td><td>52.99</td><td>63.55</td><td>73.7</td><td>78.79</td><td>81.76</td><td>83.75</td><td>85.18</td><td>85.97</td></tr>
<tr><td>Random</td><td>-</td><td>kaiming</td><td>feat2</td><td>30.54</td><td>9.15</td><td>9.39</td><td>11.09</td><td>12.3</td><td>14.3</td><td>17.41</td><td>21.32</td><td>23.77</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>feat4</td><td>64.78</td><td>18.17</td><td>22.08</td><td>29.37</td><td>35.58</td><td>41.8</td><td>48.73</td><td>55.55</td><td>58.33</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>feat4</td><td>67.38</td><td>18.91</td><td>23.33</td><td>30.57</td><td>38.22</td><td>45.83</td><td>52.23</td><td>58.08</td><td>61.11</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>feat5</td><td>74.26</td><td>29.73</td><td>37.66</td><td>45.85</td><td>55.57</td><td>62.48</td><td>66.15</td><td>70.0</td><td>71.37</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>feat5</td><td>74.50</td><td>24.19</td><td>31.24</td><td>39.69</td><td>50.99</td><td>59.03</td><td>64.4</td><td>68.69</td><td>70.84</td></tr>
<tr><td></td><td>selfsup/npid/r50_ensure_neg.py</td><td>ensure_neg=True</td><td>feat5</td><td>75.70</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></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>feat5</td><td>78.42</td><td>32.42</td><td>40.27</td><td>49.95</td><td>59.96</td><td>65.71</td><td>69.99</td><td>73.64</td><td>75.13</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>feat5</td><td>79.18</td><td>30.03</td><td>37.73</td><td>47.64</td><td>58.78</td><td>66.0</td><td>70.6</td><td>74.6</td><td>76.07</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>feat5</td><td>84.26</td><td>-</td><td>-</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>feat5</td><td>78.95</td><td>32.45</td><td>40.76</td><td>50.4</td><td>59.01</td><td>65.45</td><td>70.13</td><td>73.58</td><td>75.35</td></tr>
<tr><td></td><td>selfsup/simclr/r50_bs256_ep200_mocov2_neck.py</td><td>-&gt; MoCo v2 neck</td><td>feat5</td><td>77.65</td><td></td><td></td><td></td><td></td><td></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_bs4096_ep200.py</td><td>default</td><td>feat5</td><td>85.10</td><td>44.48</td><td>52.09</td><td>62.88</td><td>70.87</td><td>76.18</td><td>79.45</td><td>81.88</td><td>83.08</td></tr>
</tbody></table>
### ImageNet Linear Classification
**Note**
* Config: `configs/benchmarks/linear_classification/imagenet/r50_multihead.py` for ImageNet (Multi) and `configs/benchmarks/linear_classification/imagenet/r50_last.py` for ImageNet (Last).
* For DeepCluster, use the corresponding one with `_sobel`.
* ImageNet (Multi) evaluates features in around 9k dimensions from different layers. Top-1 result of the last epoch is reported.
* 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.
<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>11.37</td><td>16.21</td><td>13.47</td><td>9.07</td><td>6.54</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>14.76</td><td>31.29</td><td>45.77</td><td>49.31</td><td>40.20</td><td>38.83</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>14.74</td><td>32.81</td><td>44.95</td><td>61.61</td><td>66.73</td><td>67.69</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>-&gt; 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_bs4096_ep200.py</td><td>default</td><td>16.70</td><td>34.22</td><td>46.61</td><td>60.78</td><td>69.14</td><td>67.10</td></tr>
</tbody></table>
### Places205 Linear Classification
**Note**
* Config: `configs/benchmarks/linear_classification/places205/r50_multihead.py`.
* For DeepCluster, use the corresponding one with `_sobel`.
* Places205 evaluates features in around 9k dimensions from different layers. Top-1 result of the last epoch is reported.
<table><thead><tr><th rowspan="2">Method</th><th rowspan="2">Config</th><th rowspan="2">Remarks</th><th colspan="5">Places205</th></tr>
<tr><td>feat1</td><td>feat2</td><td>feat3</td><td>feat4</td><td>feat5</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>21.27</td><td>36.10</td><td>43.03</td><td>51.38</td><td>53.05</td></tr>
<tr><td>Random</td><td>-</td><td>kaiming</td><td>17.19</td><td>21.70</td><td>19.23</td><td>14.59</td><td>11.73</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>21.07</td><td>34.86</td><td>42.84</td><td>45.71</td><td>41.45</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>18.65</td><td>35.71</td><td>42.28</td><td>45.98</td><td>43.72</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>18.80</td><td>33.93</td><td>41.44</td><td>47.22</td><td>42.61</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>20.53</td><td>34.03</td><td>40.48</td><td>47.13</td><td>47.73</td></tr>
<tr><td><a href="https://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>20.94</td><td>34.78</td><td>41.19</td><td>47.45</td><td>49.18</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>21.13</td><td>35.19</td><td>42.40</td><td>48.78</td><td>50.70</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>20.86</td><td>35.63</td><td>42.57</td><td>49.93</td><td>52.05</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>22.55</td><td>34.14</td><td>40.35</td><td>47.15</td><td>51.64</td></tr>
<tr><td></td><td>selfsup/simclr/r50_bs256_ep200_mocov2_neck.py</td><td>-&gt; MoCo v2 neck</td><td></td><td></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_bs4096_ep200.py</td><td>default</td><td>22.28</td><td>35.95</td><td>43.03</td><td>49.79</td><td>52.75</td></tr>
</tbody></table>
### ImageNet Semi-Supervised Classification
**Note**
* 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.
* Config: under `configs/benchmarks/semi_classification/imagenet_1percent/` for 1% data, and `configs/benchmarks/semi_classification/imagenet_10percent/` for 10% data.
* 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.
* Please use `--deterministic` in this benchmark.
<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>r50_lr0_01_head100.py</td><td>16.48</td><td>40.37</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><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>-&gt; 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_bs4096_ep200.py</td><td>default</td><td>r50_lr0_01_head10.py</td><td>49.37</td><td>76.75</td></tr>
</tbody></table>
<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 10%</th><th colspan="2">ImageNet 10%</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_head10.py</td><td>74.53</td><td>92.19</td></tr>
<tr><td>Random</td><td>-</td><td>kaiming</td><td>r50_lr0_01_head1.py</td><td>21.78</td><td>44.24</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>r50_lr0_01_head100.py</td><td>53.86</td><td>79.62</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>54.75</td><td>80.21</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>52.94</td><td>77.96</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>57.22</td><td>81.39</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_head10.py</td><td>58.15</td><td>82.55</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>60.08</td><td>84.02</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>r50_lr0_01_head100.py</td><td>61.64</td><td>84.90</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>58.46</td><td>82.60</td></tr>
<tr><td></td><td>selfsup/simclr/r50_bs256_ep200_mocov2_neck.py</td><td>-&gt; MoCo v2 neck</td><td></td><td>58.38</td><td>82.53</td></tr>
<tr><td><a href="https://arxiv.org/abs/2006.07733" target="_blank" rel="noopener noreferrer">BYOL</a></td><td>selfsup/byol/r50_bs4096_ep200.py</td><td>default</td><td>r50_lr0_01_head100.py</td><td>65.94</td><td>87.81</td></tr>
</tbody></table>
### PASCAL VOC07+12 Object Detection
**Note**
* This benchmark follows the evluation protocols set up by MoCo.
* Config: `benchmarks/detection/configs/pascal_voc_R_50_C4_24k_moco.yaml`.
* Please follow [here](GETTING_STARTED.md#voc0712--coco17-object-detection) to run the evaluation.
<table><thead><tr><th rowspan="2">Method</th><th rowspan="2">Config</th><th rowspan="2">Remarks</th><th colspan="3">VOC07+12</th></tr>
<tr><td>AP50</td><td>AP</td><td>AP75</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>81.58</td><td>54.19</td><td>59.80</td></tr>
<tr><td>Random</td><td>-</td><td>kaiming</td><td>59.02</td><td>32.83</td><td>31.60</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>80.36</td><td>55.13</td><td>61.18</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>80.91</td><td>55.52</td><td>61.39</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>80.03</td><td>54.11</td><td>59.50</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>81.38</td><td>55.95</td><td>62.23</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>81.96</td><td>56.63</td><td>62.90</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>79.41</td><td>51.54</td><td>55.63</td></tr>
<tr><td><a href="https://arxiv.org/abs/2006.07733" target="_blank" rel="noopener noreferrer">BYOL</a></td><td>selfsup/byol/r50_bs4096_ep200.py</td><td>default</td><td>80.95</td><td>51.87</td><td>56.53</td></tr>
</tbody></table>
### COCO2017 Object Detection
**Note**
* This benchmark follows the evluation protocols set up by MoCo.
* Config: `benchmarks/detection/configs/coco_R_50_C4_2x_moco.yaml`.
* Please follow [here](GETTING_STARTED.md#voc0712--coco17-object-detection) to run the evaluation.
<table><thead><tr><th rowspan="2">Method</th><th rowspan="2">Config</th><th rowspan="2">Remarks</th><th colspan="6">COCO2017</th></tr>
<tr><td>AP50(Box)</td><td>AP(Box)</td><td>AP75(Box)</td><td>AP50(Mask)</td><td>AP(Mask)</td><td>AP75(Mask)</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>59.9</td><td>40.0</td><td>43.1</td><td>56.5</td><td>34.7</td><td>36.9</td></tr>
<tr><td>Random</td><td>-</td><td>kaiming</td><td>54.6</td><td>35.6</td><td>38.2</td><td>51.5</td><td>31.4</td><td>33.5</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>59.6</td><td>40.0</td><td>43.5</td><td>56.5</td><td>35.0</td><td>37.3</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>59.3</td><td>40.0</td><td>43.6</td><td>56.0</td><td>34.9</td><td>37.4</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>59.0</td><td>39.4</td><td>42.8</td><td>55.9</td><td>34.5</td><td>36.6</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>60.5</td><td>40.9</td><td>44.2</td><td>57.1</td><td>35.5</td><td>37.7</td></tr>
<tr><td><a href="https://arxiv.org/abs/2003.04297">MoCo v2</a></td><td>selfsup/moco/r50_v2.py</td><td>default</td><td>60.7</td><td>40.9</td><td>44.2</td><td>57.2</td><td>35.5</td><td>37.9</td></tr>
<tr><td><a href="https://arxiv.org/abs/2002.05709">SimCLR</a></td><td>selfsup/simclr/r50_bs256_ep200.py</td><td>default</td><td>59.1</td><td>39.6</td><td>42.9</td><td>55.9</td><td>34.6</td><td>37.1</td></tr>
<tr><td><a href="https://arxiv.org/abs/2006.07733">BYOL</a></td><td>selfsup/byol/r50.py</td><td>default</td><td>60.5</td><td>40.3</td><td>43.9</td><td>56.8</td><td>35.1</td><td>37.3</td></tr>
</tbody></table>
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