---
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`)