ClusterFit ResNet-50 (ImageNet-1K, 16K RotNet clusters) achieves 81.5% Top 1 Accuracy on ImageNet
Training Techniques | Rotnet, ClusterFit, 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_clusterfit |
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ClusterFit is a self-supervision approach for learning image representations. Given a dataset, we (a) cluster its features extracted from a pre-trained network using k-means and (b) re-train a new network from scratch on this dataset using cluster assignments as pseudo-labels.
Get started with VISSL by trying one of the Colab tutorial notebooks.
@article{DBLP:journals/corr/abs-1912-03330,
author = {Xueting Yan and
Ishan Misra and
Abhinav Gupta and
Deepti Ghadiyaram and
Dhruv Mahajan},
title = {ClusterFit: Improving Generalization of Visual Representations},
journal = {CoRR},
volume = {abs/1912.03330},
year = {2019},
url = {http://arxiv.org/abs/1912.03330},
archivePrefix = {arXiv},
eprint = {1912.03330},
timestamp = {Mon, 28 Sep 2020 08:19:37 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1912-03330.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 | ClusterFit ResNet-50 (ImageNet-1K, 16K RotNet clusters) | Top 1 Accuracy | 53.63% | # 309 |