| --- |
| 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`) |
|
|
