Training Techniques | RotNet, Weight Decay, SGD with Momentum |
---|---|
Architecture | Convolution, Dropout, Dense Connections, ReLU, Max Pooling, Softmax |
ID | alexnet_in1k_oss_rotnet |
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Training Techniques | RotNet, 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_rotnet |
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Training Techniques | RotNet, 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_rotnet |
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RotNet is a self-supervision approach that relies on predicting image rotations as the pretext task in order to learn image representations.
Get started with VISSL by trying one of the Colab tutorial notebooks.
@article{DBLP:journals/corr/abs-1803-07728,
author = {Spyros Gidaris and
Praveer Singh and
Nikos Komodakis},
title = {Unsupervised Representation Learning by Predicting Image Rotations},
journal = {CoRR},
volume = {abs/1803.07728},
year = {2018},
url = {http://arxiv.org/abs/1803.07728},
archivePrefix = {arXiv},
eprint = {1803.07728},
timestamp = {Mon, 13 Aug 2018 16:46:04 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1803-07728.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}
}
BENCHMARK | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK |
---|---|---|---|---|
ImageNet | RotNet ResNet-50 (Goyal19, ImageNet-22K) | Top 1 Accuracy | 54.89% | # 308 |
ImageNet | RotNet ResNet-50 (Goyal19, ImageNet-1K) | Top 1 Accuracy | 48.2% | # 315 |
ImageNet | RotNet AlexNet (ImageNet-1K) | Top 1 Accuracy | 39.51% | # 322 |