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S2RL

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semantic-segmentation
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Initial release

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  1. .gitattributes +35 -0
  2. README.md +117 -0
  3. checkpoints/ADE20K/adapter_config.json +46 -0
  4. checkpoints/ADE20K/adapter_model.safetensors +3 -0
  5. checkpoints/COCO/adapter_config.json +50 -0
  6. checkpoints/COCO/adapter_model.safetensors +3 -0
  7. datasets/ADE20K/Herding.tar.gz +3 -0
  8. datasets/ADE20K/K-Center.tar.gz +3 -0
  9. datasets/ADE20K/Random.tar.gz +3 -0
  10. datasets/ADE20K/S2RL.tar.gz +3 -0
  11. datasets/ADE20K/Uniform.tar.gz +3 -0
  12. datasets/COCO/Herding.tar.gz +3 -0
  13. datasets/COCO/K-Center.tar.gz +3 -0
  14. datasets/COCO/Random.tar.gz +3 -0
  15. datasets/COCO/S2RL.tar.gz +3 -0
  16. datasets/COCO/Uniform.tar.gz +3 -0
  17. manifest.json +2334 -0
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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: stabilityai/stable-diffusion-3.5-medium
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+ library_name: peft
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+ tags:
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+ - semantic-segmentation
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+ - dataset-distillation
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+ - text-to-image
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+ - lora
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+ - s2rl
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+ ---
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+
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+ <p align="center">
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+ <img src="https://raw.githubusercontent.com/00why00/S2RL/main/assets/banner.svg" alt="S²-RL" width="100%">
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+ </p>
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+
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+ <h1 align="center">⚗️ S²-RL</h1>
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+
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+ <p align="center">
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+ <strong>Sample-Set Dual Reinforcement Learning<br>for Generative Semantic Segmentation Dataset Distillation</strong>
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+ </p>
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+
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+ <p align="center">
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+ <a href="https://haoyuwang.com">Haoyu Wang</a> · <a href="https://scholar.google.com/citations?hl=en&amp;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>
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+ </p>
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+
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+ <p align="center">
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+ <a href="https://00why00.github.io/S2RL/">🌐 Project Page</a> ·
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+ <a href="https://github.com/00why00/S2RL">💻 Code</a> ·
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+ <a href="#datasets">📂 Datasets</a> ·
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+ <a href="#training">🚀 Policy Training</a> ·
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+ <a href="#generation">🎨 Generation</a>
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+ </p>
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+
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+ 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.
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+
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+ <a id="datasets"></a>
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+
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+ ## 📂 Datasets
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+
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+ ### Selected real-data subsets
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+
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+ The selection baselines provide subsets of real images and their ground-truth masks. Uniform subsets also supply the category prompts for S²-RL.
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+
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+ | Method | ADE20K | COCO |
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+ | :--- | :---: | :---: |
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+ | 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) |
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+ | 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) |
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+ | 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) |
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+ | 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) |
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+
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+ All data archives include **three budgets and five sets per budget**, with paired JPEG images and PNG segmentation masks.
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+
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+ | Dataset | Ratios | Image–mask pairs per set |
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+ | :--- | :--- | :--- |
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+ | ADE20K | `0.0025`, `0.005`, `0.01` | 50, 101, 202 |
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+ | COCO | `0.000625`, `0.00125`, `0.0025` | 73, 147, 295 |
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+
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+ Extract archives into the data root used by the [code](https://github.com/00why00/S2RL#data):
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+
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+ ```bash
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+ tar -xzf <archive>.tar.gz -C "$S2RL_DATA_ROOT"
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+ ```
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+
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+ ```text
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+ ILDistillation/<dataset>/<method>/
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+ ├── images/time_<n>_ratio_<r>/*.jpg
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+ ├── annotations/time_<n>_ratio_<r>/*.png
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+ └── _manifest.json
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+ ```
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+
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+ 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).
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+
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+ <a id="training"></a>
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+
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+ ## 🚀 Policy Training
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+
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+ Policy training and generation are described in the [code repository](https://github.com/00why00/S2RL#training).
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+
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+ | Dataset | Policy weights | Adapter config |
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+ | :--- | :---: | :---: |
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+ | 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) |
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+ | 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) |
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+
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+ 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.
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+
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+ <a id="generation"></a>
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+
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+ ## 🎨 Generation and selection
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+
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+ ### Distilled datasets
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+
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+ S²-RL provides distilled datasets of generated images and pseudo-labels for downstream segmentation training.
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+
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+ | Method | ADE20K | COCO |
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+ | :--- | :---: | :---: |
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+ | **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) |
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+
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+ Extract into `$S2RL_DATA_ROOT`. Image–mask pairs are stored under `ILDistillation/<dataset>/S2RL/`; use `DATASET_NAME="S2RL"` in the downstream evaluation scripts.
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+
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+ ## 🖊️ Citation
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+
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+ ```bibtex
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+ @misc{wang2026s2rl,
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+ title={S$^2$-RL: Sample-Set Dual Reinforcement Learning for Generative Semantic Segmentation Dataset Distillation},
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+ author={Haoyu Wang and Fei Zhou and Qingqing Qiu and Lei Zhang and Wei Wei and Chen Ding},
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+ year={2026},
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+ eprint={ARXIV_ID},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CV},
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+ url={https://arxiv.org/abs/ARXIV_ID}
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+ }
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+ ```
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+
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+ ## 📄 License
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+
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+ 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.
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+ "Only the three main ratios and five constructed sets per ratio are included.",
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+ "Policy checkpoints are the original experimental artifacts; code corrections did not regenerate them."
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+ ],
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+ "code_revision": "8384db26834f759b4e882bc7860c4b04def813b5"
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+ }