FlyGCL: Lightweight Continual Learning Framework (FlyPrompt & ViT baselines)

FlyGCL is a practical framework for General Continual Learning (GCL) / online class-incremental learning on images, with a focus on the Si-Blurry setting. It includes multiple pretrained-based baselines built on Vision Transformers (ViT) and a lightweight runner for reproducing experiments. FlyPrompt is our proposed method, which uses random-expanded routing and temporal-ensemble experts to effectively tackle GCL problem, achieving significant gains on major benchmarks.

📦 What's included

• Methods: flyprompt (ours), l2p, dualprompt, codaprompt, mvp, misa, slca, sprompt, ranpac, hide (prompt/lora/adapter), norga, sdlora
• Backbones: ViT via timm and a local ViT implementation (models/vit.py) supporting multiple pretrained sources
• Setting: true online Si-Blurry with configurable disjoint/blurry ratios
• Outputs: logs and numpy/json artifacts under results/

🛠️ Installation (Linux)

Python environment

• Python: 3.10+
• PyTorch / CUDA: please follow the official PyTorch instructions for your CUDA version, and then install the remaining dependencies.

We recommend:

python -m venv .venv
source .venv/bin/activate
pip install -U pip
pip install -r requirements.txt

Notes:

• requirements.txt pins a full stack (including torch/torchvision). If you prefer installing PyTorch from a specific CUDA wheel index, install PyTorch first and then install the remaining packages accordingly.

🗂️ Datasets

FlyGCL uses --data_dir as the dataset root path. Different datasets expect different sub-structures (please see below).

Recommended directory layout

FlyGCL/
  data/                          # default ./data
    CIFAR/                       # for CIFAR-10/100 (torchvision)
    imagenet-r/                  # for ImageNet-R (see split requirement below)
      train/
        <class_name>/*.jpg
      test/
        <class_name>/*.jpg
    CUB_200_2011/                # for CUB-200-2011
      images/
        <class_name>/*.jpg

You may change the root path by:

• CLI: --data_dir /your/path
• Baseline scripts: set DATA_ROOT=/your/path (scripts use ${DATA_ROOT}/CIFAR, ${DATA_ROOT}/imagenet-r, ${DATA_ROOT}/CUB_200_2011 by default)

Download links (common benchmarks)

• CIFAR-100: torchvision can download automatically. Official page: https://www.cs.toronto.edu/~kriz/cifar.html
• ImageNet-R (Rendition):
  • Project page: https://github.com/hendrycks/imagenet-r
  • Tarball (as referenced in our dataset code comments): https://people.eecs.berkeley.edu/~hendrycks/imagenet-r.tar
  • Note: our active loader expects imagenet-r/train/ and imagenet-r/test/. If your download does not include this split, please create a split (e.g., 80/20 per class) and place images into train/ and test/ folders.
• CUB-200-2011:
  • Official page: https://www.vision.caltech.edu/datasets/cub_200_2011/
  • Direct tgz (images+annotations): https://data.caltech.edu/records/65de6-vp158/files/CUB_200_2011.tgz
  • After extraction, point --data_dir to the extracted CUB_200_2011/ folder (the one that contains images/).

Notes on download=True

The trainer currently calls dataset constructors with download=True, but most custom datasets in datasets/ have download/extract code commented out. In practice:

• CIFAR-10/100 (torchvision) can auto-download into --data_dir.
• Many others (e.g., ImageNet-R, CUB, Cars) require manual download + correct folder structure.

🧩 Checkpoints (pretrained backbones & prompt checkpoints)

Checkpoint download links:

• Hugging Face: Hugging Face Project
• Baidu Netdisk: Download Link (code: 39wb)

Where to put files

Please create a local folder:

FlyGCL/
  checkpoints/
    (files listed below)

Notes:

• checkpoints/ is ignored by git (.gitignore), so you should download and place files manually.
• models/vit.py will also look for some .npz weights under ~/.cache/torch/hub/checkpoints/ (torch hub cache, for vit_base_patch16_224).

Supported local backbone checkpoints (ViT-B/16)

When you set --backbone to one of the following, models/vit.py will try to load a local file when pretrained=True:

run.sh also uses vit_base_patch16_224 by default, which requires to put ViT-B_16.npz weights under ~/.cache/torch/hub/checkpoints/.

Model Name	--backbone value	Expected file name
Sup-21K	vit_base_patch16_224	ViT-B_16.npz
Sup-21K/1K	vit_base_patch16_224_mepo_21k_1k	vit_21k_1k_mepo_epoch_0.pth
iBOT-21K	vit_base_patch16_224_21k_ibot	checkpoint.pth (expects key teacher)
iBOT-1K	vit_base_patch16_224_ibot	ibot_vitbase16_pretrain.pth
DINO-1K	vit_base_patch16_224_dino	dino_vitbase16_pretrain.pth
MoCo v3-1K	vit_base_patch16_224_mocov3	mocov3-vit-base-300ep.pth (expects key model)

Prompt checkpoints (MISA)

MISA (based on DualPrompt) loads prompt tensors from local files when you pass --load_pt:

• ./checkpoints/g_prompt.pt
• ./checkpoints/e_prompt.pt

Prompt checkpoints are distributed via the same links above.

🚀 Quick start (FlyPrompt)

Single-GPU, CIFAR-100, Si-Blurry (n=50, m=10), 5 tasks:

python main.py \
  --method flyprompt \
  --dataset cifar100 \
  --data_dir ./data/CIFAR \
  --backbone vit_base_patch16_224 \
  --n_tasks 5 --n 50 --m 10 \
  --batchsize 64 --lr 0.005 \
  --online_iter 3 --num_epochs 1 \
  --use_amp --eval_period 1000 \
  --note flyprompt_cifar100

ImageNet-R example (requires ./data/imagenet-r/train and ./data/imagenet-r/test):

python main.py \
  --method flyprompt \
  --dataset imagenet-r \
  --data_dir ./data/imagenet-r \
  --backbone vit_base_patch16_224 \
  --n_tasks 5 --n 50 --m 10 \
  --batchsize 32 --lr 0.005 \
  --online_iter 3 --num_epochs 1 \
  --use_amp --eval_period 1000 \
  --note flyprompt_imagenet_r

🏃 Running baseline scripts (scripts/)

We provide ready-to-run bash scripts under scripts/:

• Set python interpreter with env var PYTHON (defaults to python)
• Set dataset root with env var DATA_ROOT (defaults to ./data)

Example:

export DATA_ROOT=./data
export PYTHON=python
bash scripts/run_baselines_flyprompt.sh 0 "1 2 3" cifar100 flyprompt_minimal

You can always override the dataset path per run by passing --data_dir ... as extra args:

bash scripts/run_baselines_flyprompt.sh 0 "1 2 3" cifar100 note --data_dir /mnt/datasets/CIFAR

run.sh (multi-session runner)

run.sh launches many baseline scripts via screen. If you plan to use it, please ensure screen is installed.

🔧 Key arguments

• Method/dataset: --method {flyprompt|l2p|dualprompt|codaprompt|mvp|slca|ranpac|...} --dataset {cifar100|imagenet-r|cub200|...} --data_dir /path
• Setting: --n_tasks 5 --n 50 (disjoint class ratio, %) --m 10 (blurry sample ratio, %)
• Training: --batchsize 64 --lr 0.005 --online_iter 3 --num_epochs 1 --use_amp --eval_period 1000
• Backbone: --backbone vit_base_patch16_224 (or one of the local-checkpoint variants)
• Repro: --seeds 1 2 3 --note my_experiment

📁 Outputs

Outputs are stored under results/:

results/
  logs/{dataset}/{note}/
    seed_{seed}.npy
    seed_{seed}_eval.npy                 # if eval_period is enabled
    seed_{seed}_eval_time.npy            # if eval_period is enabled

When running via scripts/*.sh, the console output is additionally captured by tee into:

• results/logs/{dataset}/{note}/seed_{SEEDS}_log.txt

🧱 Project layout

• main.py: entry point (loads args, builds trainer, runs)
• configuration/config.py: argument definitions
• methods/: trainers (e.g., methods/flyprompt.py)
• models/: model components (e.g., models/flyprompt.py, models/vit.py)
• datasets/: dataset wrappers
• scripts/: baseline launchers

🙏 Acknowledgements

We sincerely thank the authors and maintainers of the following open-source projects and resources. Parts of this codebase are adapted from, or inspired by, them:

• MISA
• l2p-pytorch
• RanPAC
• HiDe-Prompt
• MoE_PromptCL
• SD-Lora-CL

📝 Citation

If this repository helps your research, please cite our paper:

@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

If you have any questions, suggestions, please feel free to report issues or contact:

• Maintainer: Hongwei Yan (yanhw22@mails.tsinghua.edu.cn)

📄 License

MIT. See LICENSE.