senayyakut/semi-supervised-imagenet1k-models

Semi-supervised ImageNet1K models

Semi-Supervised and Semi-Weakly Supervised ImageNet Models

This project includes the semi-supervised and semi-weakly supervised ImageNet models introduced in "Billion-scale Semi-Supervised Learning for Image Classification" https://arxiv.org/abs/1905.00546.

"Semi-supervised" (SSL) ImageNet models are pre-trained on a subset of unlabeled YFCC100M public image dataset and fine-tuned with the ImageNet1K training dataset, as described by the semi-supervised training framework in the paper mentioned above. In this case, the high capacity teacher model was trained only with labeled examples.

"Semi-weakly" supervised (SWSL) ImageNet models are pre-trained on 940 million public images with 1.5K hashtags matching with 1000 ImageNet1K synsets, followed by fine-tuning on ImageNet1K dataset. In this case, the associated hashtags are only used for building a better teacher model. During training the student model, those hashtags are ingored and the student model is pretrained with a subset of 64M images selected by the teacher model from the same 940 million public image dataset.

We are providing the following semi-supervised and semi-weakly supervised ImageNet models. The teacher models used for training these models have the ResNet-101-32x48 model architecture.

Semi-weakly supervised ResNet and ResNext models provided in the table below significantly improve the top-1 accuracy on the ImageNet validation set compared to training from scratch or other training mechanisms introduced in the literature as of September 2019. For example, We achieve state-of-the-art accuracy of 81.2% on ImageNet for the widely used/adopted ResNet-50 model architecture.

Architecture	Supervision	#Parameters	FLOPS	Top-1 Acc.	Top-5 Acc.
ResNet-18	semi-supervised	14M	2B	72.8	91.5
ResNet-50	semi-supervised	25M	4B	79.3	94.9
ResNeXt-50 32x4d	semi-supervised	25M	4B	80.3	95.4
ResNeXt-101 32x4d	semi-supervised	42M	8B	81.0	95.7
ResNeXt-101 32x8d	semi-supervised	88M	16B	81.7	96.1
ResNeXt-101 32x16d	semi-supervised	193M	36B	81.9	96.2
ResNet-18	semi-weakly supervised	14M	2B	73.4	91.9
ResNet-50	semi-weakly supervised	25M	4B	81.2	96.0
ResNeXt-50 32x4d	semi-weakly supervised	25M	4B	82.2	96.3
ResNeXt-101 32x4d	semi-weakly supervised	42M	8B	83.4	96.8
ResNeXt-101 32x8d	semi-weakly supervised	88M	16B	84.3	97.2
ResNeXt-101 32x16d	semi-weakly supervised	193M	36B	84.8	97.4

Loading models with torch.hub

The models are available with torch.hub. As an example, to load the semi-weakly trained ResNet-50 model, simply run:

model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnet50_swsl')

Please refer to torch.hub to see a full example of using the model to classify an image.

Citation

If you use the models released in this repository, please cite the following publication.

@misc{yalniz2019billionscale,
    title={Billion-scale semi-supervised learning for image classification},
    author={I. Zeki Yalniz and Hervé Jégou and Kan Chen and Manohar Paluri and Dhruv Mahajan},
    year={2019},
    eprint={1905.00546},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

License

The models in this repository are released under the CC-BY-NC 4.0 license. See LICENSE for additional details.