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