华为2019创新大赛的工程文件+各种模型的backbone和tricks

## 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](https://pytorch.org/docs/stable/hub.html). 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](https://pytorch.org/docs/stable/hub.html) 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](LICENSE) for additional details.