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
⚗️ S²-RL
Sample-Set Dual Reinforcement Learning
for Generative Semantic Segmentation Dataset Distillation
Haoyu Wang · Fei Zhou · Qingqing Qiu · Lei Zhang · Wei Wei · Chen Ding
📄 arXiv · 🌐 Project Page · 💻 Code · 📂 Datasets · 🚀 Policy Training · 🎨 Generation
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.
📂 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 | Download |
| Uniform | Download | Download |
| Herding | Download | Download |
| K-Center | Download | Download |
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:
tar -xzf <archive>.tar.gz -C "$S2RL_DATA_ROOT"
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.
🚀 Policy Training
Policy training and generation are described in the code repository.
These LoRA adapters use 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.
🎨 Generation and selection
Distilled datasets
S²-RL provides distilled datasets of generated images and pseudo-labels for downstream segmentation training.
Extract into $S2RL_DATA_ROOT. Image–mask pairs are stored under ILDistillation/<dataset>/S2RL/; use DATASET_NAME="S2RL" in the downstream evaluation scripts.
🖊️ Citation
@misc{wang2026s2rl,
title={S$^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. Third-party components and assets follow their original licenses.