update readme

README.md

@@ -1,22 +1,20 @@
 ---
 license: apache-2.0
 task_categories:
-- visual-question-answering
+  - visual-question-answering
 language:
-- en
+  - en
 tags:
-- spatial-reasoning
-- multimodal
+  - spatial-reasoning
+  - multimodal
 pretty_name: Spatial457
 size_categories:
-- 10K<n<100K
+  - 10K<n<100K
 ---
-<!-- # Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Models -->
 
-<p align="center">
-  <img src="https://xingruiwang.github.io/projects/Spatial457/static/images/icon.png" width="48" alt="icon"/>
-  <strong><span style="font-size: 28px;">&nbsp;Spatial457</span></strong>
-</p>
+<div align="center">
+  <img src="https://xingruiwang.github.io/projects/Spatial457/static/images/icon_name.png" alt="Spatial457 Logo" width="240"/>
+</div>
 
 <h1 align="center">
   <a href="https://arxiv.org/abs/2502.08636">
@@ -24,63 +22,111 @@ size_categories:
   </a>
 </h1>
 
-
 <p align="center">
-  <a href=".">Xingrui Wang</a><sup>1</sup>,
-  <a href=".">Wufei Ma</a><sup>1</sup>,
-  <a href=".">Tiezheng Zhang</a><sup>1</sup>,
-  <a href=".">Celso M de Melo</a><sup>2</sup>,
-  <a href=".">Jieneng Chen</a><sup>1</sup>,
-  <a href=".">Alan Yuille</a><sup>1</sup>
+  <a href="https://xingruiwang.github.io/">Xingrui Wang</a><sup>1</sup>,
+  <a href="#">Wufei Ma</a><sup>1</sup>,
+  <a href="#">Tiezheng Zhang</a><sup>1</sup>,
+  <a href="#">Celso M. de Melo</a><sup>2</sup>,
+  <a href="#">Jieneng Chen</a><sup>1</sup>,
+  <a href="#">Alan Yuille</a><sup>1</sup>
 </p>
 
 <p align="center">
-  <sup>1</sup> Johns Hopkins University &nbsp;&nbsp;
+  <sup>1</sup> Johns Hopkins University &nbsp;&nbsp;&nbsp;&nbsp;
   <sup>2</sup> DEVCOM Army Research Laboratory
 </p>
 
-
 <p align="center">
-  <a href="https://xingruiwang.github.io/projects/Spatial457/">Project Page</a> /
-  <a href="https://arxiv.org/abs/2502.08636">Paper</a> /
-  <a href="https://huggingface.co/datasets/RyanWW/Spatial457">🤗 Huggingface</a> /
-  <a href="https://github.com/XingruiWang/Spatial457">Code</a>
+  <a href="https://xingruiwang.github.io/projects/Spatial457/">🌐 Project Page</a> •
+  <a href="https://arxiv.org/abs/2502.08636">📄 Paper</a> •
+  <a href="https://huggingface.co/datasets/RyanWW/Spatial457">🤗 Dataset</a> •
+  <a href="https://github.com/XingruiWang/Spatial457">💻 Code</a>
 </p>
 
 <p align="center">
-  <img src="https://xingruiwang.github.io/projects/Spatial457/static/images/teaser.png" alt="Spatial457 Teaser" width="80%">
+  <img src="https://xingruiwang.github.io/projects/Spatial457/static/images/teaser.png" alt="Spatial457 Teaser" width="80%"/>
 </p>
 
-<!-- <p align="center"><i>
-  Official implementation of the CVPR 2025 (Highlight) paper:  
-  <strong>Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Models</strong>
-</i></p> -->
-
 ---
 
 ## 🧠 Introduction
 
-Spatial457 is a diagnostic benchmark designed to evaluate the 6D spatial reasoning capabilities of large multimodal models (LMMs). It systematically introduces four key capabilities—multi-object understanding, 2D and 3D localization, and 3D orientation—across five difficulty levels and seven question types, progressing from basic recognition to complex physical interaction.
+**Spatial457** is a diagnostic benchmark designed to evaluate **6D spatial reasoning** in large multimodal models (LMMs). It systematically introduces four core spatial capabilities:
+
+- 🧱 Multi-object understanding  
+- 🧭 2D spatial localization  
+- 📦 3D spatial localization  
+- 🔄 3D orientation estimation  
+
+These are assessed across **five difficulty levels** and **seven diverse question types**, ranging from simple object queries to complex reasoning about physical interactions.
 
 ---
 
-## 📦 Download
+## 📂 Dataset Structure
+
+The dataset is organized as follows:
+
+```
+Spatial457/
+├── images/                     # RGB images used in VQA tasks
+├── questions/                  # JSONs for each subtask
+│   ├── L1_single.json
+│   ├── L2_objects.json
+│   ├── L3_2d_spatial.json
+│   ├── L4_occ.json
+│   └── ...
+├── Spatial457.py               # Hugging Face dataset loader script
+├── README.md                   # Documentation
+```
 
-You can access the full dataset and evaluation toolkit:
+Each JSON file contains a list of VQA examples, where each item includes:
 
-- **Dataset**: [Hugging Face](https://huggingface.co/datasets/RyanWW/Spatial457)  
-- **Code**: [GitHub Repository](https://github.com/XingruiWang/Spatial457)  
-- **Paper**: [arXiv 2502.08636](https://arxiv.org/abs/2502.08636)
+- "image_filename": image file name used in the question  
+- "question": natural language question  
+- "answer": boolean, string, or number  
+- "program": symbolic program (optional)  
+- "question_index": unique identifier  
+
+This modular structure supports scalable multi-task evaluation across levels and reasoning types.
 
 ---
 
-## 📊 Benchmark
+## 🛠️ Dataset Usage
+
+You can load the dataset directly using the Hugging Face 🤗 `datasets` library:
+
+### ��� Load a specific subtask (e.g., L1_single)
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("RyanWW/Spatial457", name="L1_single", split="train")
+```
+
+Each example is a dictionary like:
+
+```python
+{
+  'image': <PIL.Image.Image>,
+  'image_filename': 'superCLEVR_new_000001.png',
+  'question': 'Is the large red object in front of the yellow car?',
+  'answer': 'True',
+  'program': [...],
+  'question_index': 100001
+}
+```
+
+### 🔹 Other available configurations
 
-We benchmarked a wide range of state-of-the-art models—including GPT-4o, Gemini, Claude, and several open-source LMMs—on all subsets. Performance consistently drops as task difficulty increases. PO3D-VQA and humans remain most robust across all levels.
+```python
+[
+  "L1_single", "L2_objects", "L3_2d_spatial",
+  "L4_occ", "L4_pose", "L5_6d_spatial", "L5_collision"
+]
+```
 
-The table below summarizes model performance across 7 subsets:
+You can swap `name="..."` in `load_dataset(...)` to evaluate different spatial reasoning capabilities.
 
-<!-- Include table image or markdown table here if needed -->
 
 ---
 
@@ -94,5 +140,6 @@ The table below summarizes model performance across 7 subsets:
   year      = {2025},
   url       = {https://arxiv.org/abs/2502.08636}
 }
+```