README.md

metadata

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:

@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)