How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers (2106.10270v2)

Abstract: 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.

Analyzing Training Components in Vision Transformers

The paper, "How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers," provides a detailed empirical paper focused on the Vision Transformer (ViT) performance. It examines the effects of data augmentation, regularization (AugReg), model size, and computational budget, especially when dealing with smaller datasets. The comprehensive analysis investigates the leverage of AugReg and compute, shedding light on how these factors interplay to replicate the performance of models trained on considerably larger datasets.

Methodology and Key Findings

This paper systematically evaluates the homogeneity of training setups by retraining over 50,000 ViT models under diverse conditions. One notable outcome is the equivalence of training a ViT with enhanced compute and AugReg to models trained with significantly more data. Specifically, ViTs trained with ImageNet-21k using AugReg match the performance of those trained on the larger, non-public JFT-300M dataset, illustrating the substantial impact of such augmentations.

The researchers conducted extensive transfer learning experiments that revealed the robustness of pre-trained ViTs across different applications. A crucial observation is that pre-trained ViT models yield better results and computational efficiency for practical task-specific models rather than training from scratch.

Experimental Setup

The experimental setup employs a unified JAX/Flax codebase using TPUs for pre-training and transfer learning processes. By utilizing both ImageNet-1k and ImageNet-21k datasets, the authors ensure consistency and reproducibility, making their results reliable references for further research.

ViTs of various configurations are tested, including hybrids with ResNets to assess differences in design choices. The augmentation schemes and regularization tactics implemented address overfitting, maintaining model performance across varied data scales.

Implications

The implications of these insights are manifold. For theoretical advances, the paper reinforces the importance of data augmentation and regularization as tools for model efficiency. Practically, it provides rationale for preferring transfer learning and strategically using public datasets rather than exclusive reliance on massive, inaccessible datasets.

Given these findings, future research could extend to other Transformer-based architectures, as the paper suggests broader applicability of the observed patterns. Additional exploration into the balance of augmentation and regularization versus inherent model capacity can further refine understanding of data-efficiency mechanisms.

Conclusion

This thorough exploration into the training of ViTs offers valuable insights into optimizing performance with limited data and compute resources. The methodology and findings here offer a pivotal reference for professionals engaged in fine-tuning transformer models for diverse applications in computer vision. Overall, this research highlights how effective design and optimization strategies can substitute expansive data requirements, paving the way for more efficient deployment of Vision Transformers.