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README.md

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-license: unknown
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+---
+license: unknown
+---
+
+# ViT-5
+
+**ViT-5: Vision Transformers for the Mid-2020s**  
+Official checkpoint release.
+
+📄 Paper: https://arxiv.org/abs/2602.08071  
+💻 Code: https://github.com/wangf3014/ViT-5  
+
+---
+
+## Overview
+
+ViT-5 is a modernized Vision Transformer backbone that preserves the canonical Attention–FFN block structure while systematically upgrading its internal components using best practices from recent large-scale vision modeling research.
+
+Rather than proposing a new paradigm, ViT-5 focuses on refining and consolidating improvements that have emerged over the past few years into a clean, scalable, and reproducible ViT design suitable for mid-2020s workloads.
+
+This repository provides pretrained ViT-5 checkpoints for image recognition and as a general-purpose vision backbone.
+
+---
+
+## Model Architecture
+
+ViT-5 retains the standard Transformer encoder structure:
+
+Patch Embedding → [Attention → FFN] × L → Classification Head
+
+but modernizes key components, including:
+
+- Improved normalization strategy  
+- Updated positional encoding  
+- Refined activation design  
+- Architectural stabilization techniques  
+- Training refinements  
+
+Full architectural details are described in the paper.
+
+---
+
+## Available Checkpoints
+
+| Model | Input Resolution | Params | Top-1 (ImageNet-1K) | Notes |
+|-------|------------------|--------|---------------------|-------|
+| ViT-5-Small | 224 | 22M | 82.2% |  |
+| ViT-5-Base  | 224 | 87M | 84.2% |  |
+| ViT-5-Base  | 384 | 87M | 85.4% |  |
+| ViT-5-Large | 224 | 304M | 84.9% |  |
+| ViT-5-Large | 384 | 304M | 86.0% | Available soon |
+
+Please refer to the paper for detailed training configuration.
+
+---
+
+## Intended Use
+
+ViT-5 is designed as a general-purpose vision backbone and can be used for:
+
+- Image classification (fine-tuning or linear probing)  
+- Transfer learning to detection and segmentation  
+- Vision-language modeling  
+- Generative modeling backbones (e.g., diffusion transformers)  
+- Research on Transformer scaling and representation learning  
+
+---
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@article{wang2026vit5,
+  title={ViT-5: Vision Transformers for The Mid-2020s},
+  author={Wang, Feng and Ren, Sucheng and Zhang, Tiezheng and Neskovic, Predrag and Bhattad, Anand and Xie, Cihang and Yuille, Alan},
+  journal={arXiv preprint arXiv:2602.08071},
+  year={2026}
+}
+```
+
+---
+
+## Acknowledgements
+
+This work builds on the foundation of Vision Transformers and recent advances in scalable Transformer design.