Training Techniques | Jigsaw, Weight Decay, SGD with Momentum |
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Architecture | Convolution, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
ID | alexnet_in1k_jigsaw_goyal |
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Training Techniques | Jigsaw, Weight Decay, SGD with Momentum |
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Architecture | Convolution, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
ID | alexnet_in22k_jigsaw_goyal |
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Training Techniques | Jigsaw, Weight Decay, SGD with Momentum |
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Architecture | Convolution, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
ID | alexnet_yfcc100m_jigsaw_goyal |
Jigsaw ResNet-50 - 100 permutations achieves 83.3% Top 1 Accuracy on ImageNet
Training Techniques | Jigsaw, Weight Decay, SGD with Momentum |
---|---|
Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
ID | rn50_in1k_perm100_jigsaw |
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Jigsaw ResNet-50 - 10K permutations achieves 81.9% Top 1 Accuracy on ImageNet
Training Techniques | Jigsaw, Weight Decay, SGD with Momentum |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
ID | rn50_in1k_perm10k_jigsaw |
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Training Techniques | Jigsaw, Weight Decay, SGD with Momentum |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
ID | rn50_in1k_jigsaw_goyal |
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Training Techniques | Jigsaw, Weight Decay, SGD with Momentum |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
ID | rn50_in22k_jigsaw_goyal |
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Training Techniques | Jigsaw, Weight Decay, SGD with Momentum |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
ID | rn50_yfcc100m_jigsaw_goyal |
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Jigsaw ResNet-50 (ImageNet-1K, 2K permutations) achieves 82% Top 1 Accuracy on ImageNet
Training Techniques | Jigsaw, Weight Decay, SGD with Momentum |
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Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
ID | rn50_in1k_perm2k_jigsaw |
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Jigsaw ResNet-50 (ImageNet-22K, 2K permutations) achieves 82.9% Top 1 Accuracy on ImageNet
Training Techniques | Jigsaw, Weight Decay, SGD with Momentum |
---|---|
Architecture | 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax |
ID | rn50_in22k_perm2k_jigsaw |
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Jigsaw is a self-supervision approach that relies on jigsaw-like puzzles as the pretext task in order to learn image representations. This particular set of models includes improved models for Jigsaw that employ:
Get started with VISSL by trying one of the Colab tutorial notebooks.
@article{DBLP:journals/corr/abs-1905-01235,
author = {Priya Goyal and
Dhruv Mahajan and
Abhinav Gupta and
Ishan Misra},
title = {Scaling and Benchmarking Self-Supervised Visual Representation Learning},
journal = {CoRR},
volume = {abs/1905.01235},
year = {2019},
url = {http://arxiv.org/abs/1905.01235},
archivePrefix = {arXiv},
eprint = {1905.01235},
timestamp = {Mon, 28 Sep 2020 08:19:37 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1905-01235.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{DBLP:journals/corr/NorooziF16,
author = {Mehdi Noroozi and
Paolo Favaro},
title = {Unsupervised Learning of Visual Representations by Solving Jigsaw
Puzzles},
journal = {CoRR},
volume = {abs/1603.09246},
year = {2016},
url = {http://arxiv.org/abs/1603.09246},
archivePrefix = {arXiv},
eprint = {1603.09246},
timestamp = {Mon, 13 Aug 2018 16:49:09 +0200},
biburl = {https://dblp.org/rec/journals/corr/NorooziF16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@misc{goyal2021vissl,
author = {Priya Goyal and Benjamin Lefaudeux and Mannat Singh and Jeremy Reizenstein and Vinicius Reis and
Min Xu and and Matthew Leavitt and Mathilde Caron and Piotr Bojanowski and Armand Joulin and
Ishan Misra},
title = {VISSL},
howpublished = {\url{https://github.com/facebookresearch/vissl}},
year = {2021}
}
MODEL | TOP 1 ACCURACY |
---|---|
Jigsaw ResNet-50 (Goyal19, ImageNet-22K) | 53.09% |
Jigsaw ResNet-50 (Goyal19, YFCC100M) | 51.37% |
Jigsaw ResNet-50 - 100 permutations | 48.57% |
Jigsaw ResNet-50 - 10K permutations | 48.11% |
Jigsaw ResNet-50 (ImageNet-1K, 2K permutations) | 46.73% |
Jigsaw ResNet-50 (Goyal19, ImageNet-1K) | 46.58% |
Jigsaw ResNet-50 (ImageNet-22K, 2K permutations) | 44.84% |
Jigsaw AlexNet (Goyal19, ImageNet-22K) | 37.5% |
Jigsaw AlexNet (Goyal19, YFCC100M) | 37.01% |
Jigsaw AlexNet (Goyal19, ImageNet-1K) | 34.82% |