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@ -20,8 +20,8 @@ Below is the relations among Unsupervised Learning, Self-Supervised Learning and
- **All methods in one repository**
<em>For comprehensive comparison in all benchmarks, refer to [MODEL_ZOO.md](docs/MODEL_ZOO.md).</em>
<table><thead><tr><th>Method</th><th>VOC07 SVM (best layer)</th><th>ImageNet (best layer)</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>87.17</td><td>76.17</td></tr><tr><td>Random</td><td>30.22</td><td>13.70</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>65.06</td><td>49.31</td></tr><tr><td><a href="https://arxiv.org/abs/1803.07728" target="_blank" rel="noopener noreferrer">Rotation-Pred</a></td><td>67.38</td><td>54.99</td></tr><tr><td><a href="https://arxiv.org/abs/1807.05520" target="_blank" rel="noopener noreferrer">DeepCluster</a></td><td>74.26</td><td>57.71</td></tr><tr><td><a href="https://arxiv.org/abs/1805.01978" target="_blank" rel="noopener noreferrer">NPID</a></td><td>74.50</td><td>56.61</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>78.42</td><td>57.70</td></tr><tr><td><a href="https://arxiv.org/abs/1911.05722" target="_blank" rel="noopener noreferrer">MoCo</a></td><td>79.18</td><td>60.60</td></tr><tr><td><a href="https://arxiv.org/abs/2003.04297" target="_blank" rel="noopener noreferrer">MoCo v2</a></td><td>84.05</td><td>66.72</td></tr><tr><td><a href="https://arxiv.org/abs/2002.05709" target="_blank" rel="noopener noreferrer">SimCLR</a></td><td>78.95</td><td>61.57</td></tr><tr><td><a href="https://arxiv.org/abs/2006.07733" target="_blank" rel="noopener noreferrer">BYOL</a></td><td></td><td></td></tr></tbody></table>
<em>For comprehensive comparison in all benchmarks, refer to [MODEL_ZOO.md](docs/MODEL_ZOO.md). Most of the methods are under the `batch_size=256, epochs=200` setting.</em>
<table><thead><tr><th>Method</th><th>VOC07 SVM (best layer)</th><th>ImageNet (best layer)</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>87.17</td><td>76.17</td></tr><tr><td>Random</td><td>30.22</td><td>13.70</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>65.06</td><td>49.31</td></tr><tr><td><a href="https://arxiv.org/abs/1803.07728" target="_blank" rel="noopener noreferrer">Rotation-Pred</a></td><td>67.38</td><td>54.99</td></tr><tr><td><a href="https://arxiv.org/abs/1807.05520" target="_blank" rel="noopener noreferrer">DeepCluster</a></td><td>74.26</td><td>57.71</td></tr><tr><td><a href="https://arxiv.org/abs/1805.01978" target="_blank" rel="noopener noreferrer">NPID</a></td><td>74.50</td><td>56.61</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>78.42</td><td>57.70</td></tr><tr><td><a href="https://arxiv.org/abs/1911.05722" target="_blank" rel="noopener noreferrer">MoCo</a></td><td>79.18</td><td>60.60</td></tr><tr><td><a href="https://arxiv.org/abs/2003.04297" target="_blank" rel="noopener noreferrer">MoCo v2</a></td><td>84.05</td><td>66.72</td></tr><tr><td><a href="https://arxiv.org/abs/2002.05709" target="_blank" rel="noopener noreferrer">SimCLR</a></td><td>78.95</td><td>61.57</td></tr><tr><td><a href="https://arxiv.org/abs/2006.07733" target="_blank" rel="noopener noreferrer">BYOL (bs4096)</a></td><td>85.10</td><td>67.10</td></tr></tbody></table>
- **Flexibility & Extensibility**