Update README.md

README.md

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----
-license: mit
----
+---
+language:
+  - en
+license: mit
+tags:
+  - continual-learning
+  - general-continual-learning
+  - online-learning
+  - vision
+  - image-classification
+  - vit
+  - prompt-tuning
+library_name: pytorch
+pipeline_tag: image-classification
+inference: false
+---
+
+# FlyGCL Checkpoints (FlyPrompt & ViT Baselines)
+
+This repository provides **research checkpoints** for **FlyGCL**, a lightweight framework for **General Continual Learning (GCL) / online class-incremental learning** in the **Si-Blurry** setting.
+
+It is designed to be used together with the FlyGCL codebase:
+
+- Code: `https://github.com/AnAppleCore/FlyGCL`
+- Paper (arXiv): `https://www.arxiv.org/abs/2602.01976`
+- OpenReview: `https://openreview.net/forum?id=8pi1rP71qv`
+
+## What is included
+
+This model repo may contain:
+
+- **Backbone checkpoints** (ViT-B/16 variants) referenced by FlyGCL via `--backbone`.
+- **Prompt checkpoints** (optional) for DualPrompt/MISA-style prompts:
+  - `g_prompt.pt`
+  - `e_prompt.pt`
+
+For the exact filename mapping and where to place these files in FlyGCL, see the code repository README:
+
+- `https://github.com/AnAppleCore/FlyGCL/blob/main/README.md`
+
+## Model details
+
+- **Architecture family**: Vision Transformer (ViT-B/16) backbones + prompt-based continual learning heads.
+- **Framework**: PyTorch.
+- **Training setting**: online GCL / Si-Blurry (see paper and code for details).
+
+## Intended use
+
+These checkpoints are released for:
+
+- **Research / reproducibility** of the FlyGCL paper and baselines
+- **Benchmarking** continual learning methods in comparable settings
+
+Not intended for:
+
+- Safety-critical or medical/diagnostic use
+- Deployment without careful evaluation in your target environment
+
+## Limitations and biases
+
+- Continual learning performance depends on data ordering, hyperparameters, and backbone initialization.
+- Backbones pretrained on large-scale datasets may encode biases from their pretraining data.
+- Prompt checkpoints may not transfer to datasets/settings different from those used during training.
+
+## License
+
+- Code license: MIT (see FlyGCL `LICENSE`).
+- **Checkpoint licensing** may depend on upstream sources (e.g., DINO/iBOT/MoCo pretrained backbones). If you redistribute upstream-derived weights here, ensure the redistribution terms are compatible and include required notices.
+
+## Citation
+
+If you use FlyGCL or these checkpoints in your research, please cite:
+
+```bibtex
+@inproceedings{flyprompt2026,
+  title={FlyPrompt: Brain-Inspired Random-Expanded Routing with Temporal-Ensemble Experts for General Continual Learning},
+  author={Yan, Hongwei and Sun, Guanglong and Zhou, Kanglei and Li, Qian and Wang, Liyuan and Zhong, Yi},
+  booktitle={ICLR},
+  year={2026}
+}
+```
+
+## Contact
+
+- Maintainer: `Hongwei Yan` (`yanhw22@mails.tsinghua.edu.cn`)