Update README.md

README.md

@@ -1,43 +1,111 @@
-
 ---
 license: cc-by-nc-4.0
+task_categories:
+  - question-answering
+language:
+  - en
+size_categories:
+  - 10M<n<100M
+pretty_name: SpatialForge-10M
+---
+
+# SpatialForge-10M
+
+**SpatialForge: Bootstrapping 3D-Aware Spatial Reasoning from Open-World 2D Images**
+
+🤗 Hugging Face | 📑 Paper 
+
+**Zishan Liu, Ruoxi Zang, Yanglin Zhang, Wei Liu, Yin Zhang, Jian Yao, Jiayin Zheng, Zhengzhe Liu**
+
+Lingnan University · XPENG Robotics
+
 ---
-# SpatialForge: Bootstrapping 3D-Aware Spatial Reasoning from Open-World 2D Images
 
-🤗 [Hugging Face](https://huggingface.co/datasets/shana643/SpatialForge) | 
-📑 [Paper](https://arxiv.org/html/2605.11462v1) | 
+# 📦 SpatialForge-10M
+
+A large-scale vision-language dataset designed for **3D-aware spatial perception and reasoning from open-world 2D images**.
 
-Zishan Liu, Ruoxi Zang, Yanglin Zhang, Wei Liu, Yin Zhang, Jian Yao, Jiayin Zheng, Zhengzhe Liu
+SpatialForge-10M contains over **10 million QA pairs** generated from **2.8 million curated real-world images**, covering both low-level spatial perception and high-level spatial reasoning tasks. The dataset is constructed from diverse open-world image sources including **Objects365**, **Pixmo** and **OpenImages**, enabling broad visual diversity across indoor, outdoor, egocentric, and internet-scale scenes.
 
-*Lingnan University · XPENG Robotics*
+SpatialForge-10M is designed for:
 
-The official data release for **SpatialForge-10M**: A Large-Scale Spatial Reasoning Dataset Bootstrapped from Open-World 2D Images.
+- Spatial reasoning pretraining
+- Multitask VLM supervision
+- 3D-aware perception learning
+- Grounding and referring research
+- Camera-centric and human-centric reasoning
+- Spatial instruction tuning
+
+The dataset supports a unified QA format suitable for training modern multimodal large language models such as Qwen-VL, InternVL, LLaVA, and related architectures.
 
 ---
 
-## 📌 Important Notice
+# 📌 Important Notice
 
-This is the **full release** of SpatialForge-10M, containing **10 million** spatial QA pairs derived from **2.8 million** curated open-world images across six spatial tasks.
+This release contains the **full SpatialForge-10M annotations**, including all question-answer pairs and task splits. You will need to obtain the corresponding images directly from the source dataset.
 
-**Key Features:**
-- ✅ **10M spatial QA pairs** spanning perception (grounding, referring, counting) and relation (near-far, left-right, perspective)
-- ✅ **Open-world diversity** — 2.8M images from Objects365, OpenImages, Pixmo
-- ✅ **CC BY-NC 4.0 license** — free for academic and non-commercial use
+## Key Features
 
-> **Note**: All bounding boxes are normalized to `[0, 1000]` to align with Qwen3-VL's pre-training format.
+- ✅ **10M+ spatial QA pairs** spanning perception and reasoning tasks
+- ✅ **2.8M curated open-world images** from diverse visual domains
+- ✅ Covers both **object-centric** and **human-centric** spatial reasoning
+- ✅ Includes grounding, referring, counting, depth reasoning, and perspective understanding
+- ✅ Designed for scalable VLM pretraining and instruction tuning
+- ✅ Bounding boxes are normalized to **[0, 1000]**, following the format used in Qwen3-VL pretraining
 
 ---
 
-## 🎯 Task Overview
+# 🧠 Task Overview
+
+SpatialForge-10M contains six major spatial tasks divided into two categories: **Perception** and **Relation Reasoning**.
 
 | Level | Task | Description | Count |
-|-------|------|-------------|-------|
-| **Perception** | Grounding | Localize objects from descriptions → bbox | 3.6M |
-| | Referring | Describe objects within a bbox | 3.6M |
-| | Counting | Count objects satisfying a condition | 495k |
-| **Relation** | Near-Far | Determine relative depth ordering | 2.6M |
-| | Left-Right | Identify horizontal relations (camera-centric) | 93k |
-| | Perspective | Reason from human-centric viewpoints | 8k |
-| **Total** | | | **10.2M** |
-
----
+|---|---|---|---|
+| Perception | Grounding | Localize objects from textual descriptions → bbox prediction | 3.6M |
+| Perception | Referring | Generate object descriptions from regions/bboxes | 3.6M |
+| Perception | Counting | Count objects satisfying semantic conditions | 495k |
+| Relation | Near-Far | Determine relative depth comapring between objects | 2.6M |
+| Relation | Left-Right | Infer camera-centric horizontal spatial relations | 93k |
+| Relation | Perspective | Human-centric viewpoint and perspective reasoning | 8k |
+|  | **Total** |  | **10.2M** |
+
+---
+
+# 🌍 Open-World Data Sources
+
+SpatialForge-10M is bootstrapped from large-scale public image datasets:
+
+- **Objects365**
+- **OpenImages**
+- **Pixmo**
+
+These datasets provide rich scene diversity across:
+
+- Indoor environments
+- Outdoor scenes
+- Human-object interactions
+- Crowded object layouts
+- Real-world internet imagery
+
+Our pipeline automatically constructs spatial supervision signals from 2D images while preserving geometric consistency and viewpoint awareness.
+
+---
+
+<!-- # 🚀 Sample Usage
+
+This section provides guidance on how to download the SpatialForge-10M annotations and the corresponding images.
+
+## 1. Download Annotations from Hugging Face
+
+First, download the annotation package from Hugging Face Hub:
+
+```bash
+# Install huggingface hub if not already
+pip install huggingface-hub
+
+# Download the full annotations (QA pairs + task splits)
+huggingface-cli download SpatialForge/SpatialForge-10M \
+  --repo-type dataset \
+  --local-dir ./SpatialForge-10M \
+  --resume-download
+ -->