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WSL-Images

This project provides models pre-trained in weakly-supervised fashion on 940 million public images with 1.5K hashtags matching with 1000 ImageNet1K synsets, followed by fine-tuning on ImageNet1K dataset. Please refer to "Exploring the Limits of Weakly Supervised Pretraining" (https://arxiv.org/abs/1805.00932) presented at ECCV 2018 for the details of model training.

We are providing 4 models with different capacities.

Model #Parameters FLOPS Top-1 Acc. Top-5 Acc.
ResNeXt-101 32x8d 88M 16B 82.2 96.4
ResNeXt-101 32x16d 193M 36B 84.2 97.2
ResNeXt-101 32x32d 466M 87B 85.1 97.5
ResNeXt-101 32x48d 829M 153B 85.4 97.6

Our models significantly improve the training accuracy on ImageNet compared to training from scratch. We achieve state-of-the-art accuracy of 85.4% on ImageNet with our ResNext-101 32x48d model.

Loading models with torch.hub

The models are available with torch.hub. As an example, to load the ResNext-101 32x16d model, simply run:

model = torch.hub.load('facebookresearch/WSL-Images', 'resnext101_32x16d_wsl')

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

Citing WSL-Images

If you use the WSL-Images models, please cite the following publication.

@inproceedings{wslimageseccv2018,
  title={Exploring the Limits of Weakly Supervised Pretraining},
  author={Dhruv Kumar Mahajan and Ross B. Girshick and Vignesh Ramanathan and Kaiming He and Manohar Paluri and Yixuan Li and Ashwin Bharambe and Laurens van der Maaten},
  booktitle={ECCV},
  year={2018}
}

License

WSL-Images models are released under the CC-BY-NC 4.0 license. See LICENSE for additional details.

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Weakly Supervised Learning On Images

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