Introduce DAD with the paper abstract and teaser

README.md

@@ -20,10 +20,23 @@ size_categories:
 - 1M<n<10M
 ---
 
-# Layered Design Dataset
+# Detect Anything in Graphic Design: Element-Level Rewards for Autoregressive Detection
 
-Two layered-graphic-design subsets (`element_replaced`: ~1M samples,
-`chartgalaxy`: ~100k samples), sharing the same file layout and annotation schema.
+**NeurIPS 2026**
+
+![DAD detects layered design elements, recovers occluded regions, and supports image-to-layer decomposition.](https://huggingface.co/datasets/dad887/DAD/resolve/main/assets/teaser.png)
+
+*DAD improves element localization and recall, recovers occluded regions, and supports image-to-layer decomposition. Qualitative examples and benchmark results are from the paper; radar-chart metrics are normalized to [0.1, 1].*
+
+## Abstract
+
+Graphic designs, such as posters, advertisements, and infographics, are an important medium for communicating information and shaping understanding. Unlike natural images, they consist of layered elements with explicit compositional order. However, existing object detection models treat these elements as an unordered set, leaving compositional order unexploited. To address this limitation, we present Detect Anything in Graphic Design (DAD), a model that formulates graphic design detection as compositional deconstruction. It decodes elements in compositional order, using lower-layer elements to better detect higher-layer ones. The key feature of DAD is amodal detection, which predicts the full bounding box of each element, including regions occluded by elements placed above it. Building on this formulation, we propose Element Relative Policy Optimization (EleRPO), which extends GRPO from sequence-level supervision to element-level optimization. EleRPO provides fine-grained training signals that capture how each detected element contributes to overall detection quality, and works synergistically with compositional order to improve detection performance. To support training and evaluation, we build a dataset of 10 million graphic designs. Experiments show that DAD outperforms all baselines and achieves human-level performance in amodal detection, supporting effective image-to-layer decomposition. EleRPO consistently improves over GRPO across nine detection benchmarks.
+
+## Dataset overview
+
+This repository provides the public layered-design dataset accompanying DAD. It contains approximately one million designs constructed by replacing elements with stylistically and semantically similar alternatives, together with approximately 100,000 ChartGalaxy designs. Composited images, individual layer assets, and aligned annotations support graphic-design detection and image-to-layer decomposition.
+
+The paper's DAD-10M training and evaluation collection contains 10 million designs. The two subsets distributed here are listed below and share the same file layout and annotation schema.
 
 | subset | source | samples | layers / sample | size |
 |---|---|---:|---:|---:|
@@ -86,9 +99,12 @@ layers = [Image.open(ROOT / "samples" / r["primary_key"] / lf)
 boxes  = r["boxes"]
 types  = r["types"]
 ```
-## Public experiment
 
-The fixed public experiment [SFT, RL, and test splits](https://huggingface.co/datasets/dad887/DAD-Public-Repro), [SFT/GRPO/EleRPO models](https://huggingface.co/dad887/DAD-Public-Models), and [inference and data tools](https://github.com/dad887/DAD) are available separately.
+## Code and additional resources
+
+The [DAD code repository](https://github.com/dad887/DAD) provides the EleRPO implementation, annotation and filtering tools, and inference/evaluation scripts.
+
+For the paper's public-data experiment, separate repositories provide the [SFT, RL, and test splits](https://huggingface.co/datasets/dad887/DAD-Public-Repro) and [SFT, GRPO, and EleRPO models](https://huggingface.co/dad887/DAD-Public-Models). Those checkpoints are public-data experiment models, separate from the DAD model used for the paper's main experiments.
 
 ## License