Update MODEL_ZOO.md

update VOC07 SVM & SVM Low-shot results

docs/MODEL_ZOO.md

@@ -8,7 +8,7 @@
 
 ### 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>30.22</td><td></td><td></td><td></td><td></td><td></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></td><td>default</td><td>feat4</td><td>65.06</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/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></td><td></td><td></td><td></td><td></td><td></td><td></td><td></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></td><td></td><td></td><td></td><td></td><td></td><td></td><td></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></td><td></td><td></td><td></td><td></td><td></td><td></td><td></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></td><td></td><td></td><td></td><td></td><td></td><td></td><td></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></td><td></td><td></td><td></td><td></td><td></td><td></td><td></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>84.05</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>selfsup/moco/r50_v2_simclr_neck.py</td><td>-&gt; SimCLR neck<br></td><td>feat5</td><td>84.00</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></td><td></td><td></td><td></td><td></td><td></td><td></td><td></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></td><td></td><td></td><td></td><td></td><td></td><td></td><td></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">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>84.05</td><td>42.33</td><td>50.57</td><td>62.59</td><td>70.47</td><td>75.82</td><td>78.53</td><td>80.9</td><td>81.99</td></tr><tr><td></td><td>selfsup/moco/r50_v2_simclr_neck.py</td><td>-&gt; SimCLR neck</td><td>feat5</td><td>84.00</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