FlexiViT: One Model for All Patch Sizes
The paper presents FlexiViT, a model designed to address the limitations of Vision Transformers (ViTs) when it comes to varying patch sizes without requiring retraining. FlexiViT introduces a method where patch sizes are randomized during training, resulting in a single model that can perform well across a range of patch sizes. This adaptability allows for flexible computation budgets at deployment. This paper evaluates FlexiViT on various tasks including classification and segmentation, often achieving comparable or superior results to fixed patch size models.
Key Contributions
- Patch Randomization Strategy: By randomizing patch sizes during training, FlexiViT sidesteps the necessity for retraining models for different patch sizes, providing compute-adaptive capabilities inherently.
- Adaptive Resizing: The model employs adaptive resizing for patch embedding weights and positional embeddings, enabling it to handle diverse sequence lengths efficiently without architectural changes.
- Compatibility with Pretrained Models: FlexiViT maintains compatibility with existing pretrained ViT models. This is achieved through resizing operations such as PI-resize, which preserves the integrity and effectiveness of patch embeddings across different patch sizes.
- Superior Flexibility in Practical Applications: Through extensive evaluations on ImageNet-1k and various downstream tasks including open-world detection and semantic segmentation, FlexiViT demonstrates flexibility without sacrificing performance.
- Resource-Efficient Transfer Learning: The ability of FlexiViT to retain flexibility at different patch sizes allows for significant savings in training resources. The model can be pretrained effectively with large patches and used with smaller patches during inference to optimize performance.
Strong Numerical Results
FlexiViT shows robust performance across tasks compared to standard ViT models trained on single patch sizes. It potentially matches or exceeds the performance of these models, particularly when evaluated with patch sizes different from those used during their training. In the discussed experiments, FlexiViT visibly outperforms fixed-size models in tasks such as semantic segmentation (e.g., Cityscapes mIoU), indicating enhanced adaptability and practical application potential.
Implications and Future Directions
The introduction of FlexiViT provides valuable insights into the flexibility of neural architectures, particularly in handling variable computational demands and model scalability. From a theoretical perspective, it demonstrates a significant shift in the traditional paradigm of model training and deployment, potentially influencing future research on model adaptation to dynamic environments.
Practically, FlexiViT's adaptability makes it a viable candidate for real-world scenarios where computational resources can be unpredictable or constrained. The principles introduced could inspire future explorations in automated adaptability, extending beyond vision tasks to broader AI applications. Moreover, this research may prompt further investigations into adaptive training methodologies, fostering deeper integration of flexibility within model architectures.
Overall, FlexiViT presents a significant step toward bridging the gap between performance optimization and resource efficiency in the context of neural networks dealing with vision tasks. By simplifying the adaptation to different computational settings, FlexiViT sets a foundation for more resilient and versatile AI systems in practical applications.