text: 454,66,1406918712058433540,1091042849561473031,Alexander Kolesnikov 🇺🇦,"New paper <LINK> 🧵 : How to train your ViT? It is common to train vision transformers on ImageNet-1k (~1.3m images) for 300 epochs. We show that you are better off investing the same compute budget for training on ImageNet-21k (~13m images) for 30 epochs. <LINK> But this is just one out of many highlights. We also conduct extremely thorough study on the interplay of model size, dataset size, regularizations and augmentations. Plus extensive transfer learning experiments. Check out our paper if you want to learn how to train ViT models. <LINK> We are sure there are lots of interesting insights to be drawn from our collection of trained ViTs. So we release them all: &gt;50'000 models! JAX repo: <LINK>. JAX colab: <LINK> Or also available in pytorch through timm: <LINK>. Also check out this great thread with the summary of our insights: <LINK> Work done with stellar Andreas Steiner, @XiaohuaZhai, @wightmanr, @kyosu and @giffmana.",http://arxiv.org/abs/2106.10270,"Vision Transformers (ViT) have been shown to attain highly competitive performance for a wide range of vision applications, such as image classification, object detection and semantic image segmentation. In comparison to convolutional neural networks, the Vision Transformer's weaker inductive bias is generally found to cause an increased reliance on model regularization or data augmentation (``AugReg'' for short) when training on smaller training datasets. We conduct a systematic empirical study in order to better understand the interplay between the amount of training data, AugReg, model size and compute budget. As one result of this study we find that the combination of increased compute and AugReg can yield models with the same performance as models trained on an order of magnitude more training data: we train ViT models of various sizes on the public ImageNet-21k dataset which either match or outperform their counterparts trained on the larger, but not publicly available JFT-300M dataset. ","How to train your ViT? Data, Augmentation, and Regularization in Vision