| --- |
| license: mit |
| base_model: stabilityai/stable-diffusion-3.5-medium |
| library_name: peft |
| tags: |
| - semantic-segmentation |
| - dataset-distillation |
| - text-to-image |
| - lora |
| - s2rl |
| - arxiv:2610.35491 |
| --- |
| |
| <p align="center"> |
| <img src="https://raw.githubusercontent.com/00why00/S2RL/main/assets/banner.svg" alt="S²-RL" width="100%"> |
| </p> |
|
|
| <h1 align="center">⚗️ S²-RL</h1> |
|
|
| <p align="center"> |
| <strong>Sample-Set Dual Reinforcement Learning<br>for Generative Semantic Segmentation Dataset Distillation</strong> |
| </p> |
|
|
| <p align="center"> |
| <a href="https://haoyuwang.com">Haoyu Wang</a> · <a href="https://scholar.google.com/citations?hl=en&user=Sd2wRM4AAAAJ">Fei Zhou</a> · <a href="https://openreview.net/profile?id=~Qingqing_Qiu1">Qingqing Qiu</a> · <a href="https://teacher.nwpu.edu.cn/nwpuzhanglei.html">Lei Zhang</a> · <a href="https://teacher.nwpu.edu.cn/weiwei.html">Wei Wei</a> · <a href="https://gr.xupt.edu.cn/info/1213/7446.htm">Chen Ding</a> |
| </p> |
|
|
| <p align="center"> |
| <a href="https://arxiv.org/abs/2610.35491">📄 arXiv</a> · |
| <a href="https://00why00.github.io/S2RL/">🌐 Project Page</a> · |
| <a href="https://github.com/00why00/S2RL">💻 Code</a> · |
| <a href="#datasets">📂 Datasets</a> · |
| <a href="#training">🚀 Policy Training</a> · |
| <a href="#generation">🎨 Generation</a> |
| </p> |
|
|
| This repository contains the **ADE20K and COCO datasets and LoRA adapters** used in the S²-RL main experiments: 10 data archives, 21,700 image–mask pairs, and two dataset-specific adapters. |
|
|
| <a id="datasets"></a> |
|
|
| ## 📂 Datasets |
|
|
| ### Selected real-data subsets |
|
|
| The selection baselines provide subsets of real images and their ground-truth masks. Uniform subsets also supply the category prompts for S²-RL. |
|
|
| | Method | ADE20K | COCO | |
| | :--- | :---: | :---: | |
| | Random | [Download](https://huggingface.co/0why0/S2RL/resolve/main/datasets/ADE20K/Random.tar.gz?download=true) | [Download](https://huggingface.co/0why0/S2RL/resolve/main/datasets/COCO/Random.tar.gz?download=true) | |
| | Uniform | [Download](https://huggingface.co/0why0/S2RL/resolve/main/datasets/ADE20K/Uniform.tar.gz?download=true) | [Download](https://huggingface.co/0why0/S2RL/resolve/main/datasets/COCO/Uniform.tar.gz?download=true) | |
| | Herding | [Download](https://huggingface.co/0why0/S2RL/resolve/main/datasets/ADE20K/Herding.tar.gz?download=true) | [Download](https://huggingface.co/0why0/S2RL/resolve/main/datasets/COCO/Herding.tar.gz?download=true) | |
| | K-Center | [Download](https://huggingface.co/0why0/S2RL/resolve/main/datasets/ADE20K/K-Center.tar.gz?download=true) | [Download](https://huggingface.co/0why0/S2RL/resolve/main/datasets/COCO/K-Center.tar.gz?download=true) | |
|
|
| All data archives include **three budgets and five sets per budget**, with paired JPEG images and PNG segmentation masks. |
|
|
| | Dataset | Ratios | Image–mask pairs per set | |
| | :--- | :--- | :--- | |
| | ADE20K | `0.0025`, `0.005`, `0.01` | 50, 101, 202 | |
| | COCO | `0.000625`, `0.00125`, `0.0025` | 73, 147, 295 | |
|
|
| Extract archives into the data root used by the [code](https://github.com/00why00/S2RL#data): |
|
|
| ```bash |
| tar -xzf <archive>.tar.gz -C "$S2RL_DATA_ROOT" |
| ``` |
|
|
| ```text |
| ILDistillation/<dataset>/<method>/ |
| ├── images/time_<n>_ratio_<r>/*.jpg |
| ├── annotations/time_<n>_ratio_<r>/*.png |
| └── _manifest.json |
| ``` |
|
|
| ADE20K masks use labels `1..150`, with `0` ignored. COCO masks use `0..132`, with `255` ignored; class order follows [dataset/coco.py](https://github.com/00why00/S2RL/blob/main/dataset/coco.py). |
|
|
| <a id="training"></a> |
|
|
| ## 🚀 Policy Training |
|
|
| Policy training and generation are described in the [code repository](https://github.com/00why00/S2RL#training). |
|
|
| | Dataset | Policy weights | Adapter config | |
| | :--- | :---: | :---: | |
| | ADE20K | [Download](https://huggingface.co/0why0/S2RL/resolve/main/checkpoints/ADE20K/adapter_model.safetensors?download=true) | [Download](https://huggingface.co/0why0/S2RL/resolve/main/checkpoints/ADE20K/adapter_config.json?download=true) | |
| | COCO | [Download](https://huggingface.co/0why0/S2RL/resolve/main/checkpoints/COCO/adapter_model.safetensors?download=true) | [Download](https://huggingface.co/0why0/S2RL/resolve/main/checkpoints/COCO/adapter_config.json?download=true) | |
|
|
| These LoRA adapters use [Stable Diffusion 3.5 Medium](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium) as the base model. Save both files in the same directory and set `POLICY_DIR` in the generation script to that directory. |
|
|
| <a id="generation"></a> |
|
|
| ## 🎨 Generation and selection |
|
|
| ### Distilled datasets |
|
|
| S²-RL provides distilled datasets of generated images and pseudo-labels for downstream segmentation training. |
|
|
| | Method | ADE20K | COCO | |
| | :--- | :---: | :---: | |
| | **S²-RL** | [Download](https://huggingface.co/0why0/S2RL/resolve/main/datasets/ADE20K/S2RL.tar.gz?download=true) | [Download](https://huggingface.co/0why0/S2RL/resolve/main/datasets/COCO/S2RL.tar.gz?download=true) | |
|
|
| Extract into `$S2RL_DATA_ROOT`. Image–mask pairs are stored under `ILDistillation/<dataset>/S2RL/`; use `DATASET_NAME="S2RL"` in the downstream evaluation scripts. |
|
|
| ## 🖊️ Citation |
|
|
| ```bibtex |
| @misc{wang2026s2rl, |
| title={S$^2$-RL: Sample-Set Dual Reinforcement Learning for Generative Semantic Segmentation Dataset Distillation}, |
| author={Haoyu Wang and Fei Zhou and Qingqing Qiu and Lei Zhang and Wei Wei and Chen Ding}, |
| year={2026}, |
| eprint={2610.35491}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2610.35491} |
| } |
| ``` |
|
|
| ## 📄 License |
|
|
| S²-RL contributions are licensed under the [MIT License](https://github.com/00why00/S2RL/blob/main/LICENSE). Third-party components and assets follow their original licenses. |
|
|
