Update README from hunarbatra/SpatialThinker-3B

README.md

@@ -1,7 +1,117 @@
 ---
-datasets:
-- OX-PIXL/STVQA-7K
-base_model:
-- Qwen/Qwen2.5-VL-3B-Instruct
+license: apache-2.0
+language:
+- en
+tags:
+- spatial-reasoning
+- multimodal
+- vision-language
+- scene-graph
+- reinforcement-learning
+base_model: Qwen/Qwen2.5-VL-3B-Instruct
+pipeline_tag: image-text-to-text
 ---
-Paper: https://arxiv.org/abs/2511.07403
+
+# SpatialThinker-3B
+
+<p align="center">
+  <a href="https://arxiv.org/abs/2511.07403">
+    <img src="https://img.shields.io/badge/arXiv-2511.07403-b31b1b.svg" alt="arXiv">
+  </a>
+  <a href="https://hunarbatra.com/SpatialThinker">
+    <img src="https://img.shields.io/badge/🌐%20Project%20Page-blue.svg" alt="Project Page">
+  </a>
+  <a href="https://github.com/hunarbatra/SpatialThinker">
+    <img src="https://img.shields.io/badge/GitHub-Repository-black.svg" alt="GitHub">
+  </a>
+</p>
+
+**SpatialThinker-3B** is a 3D-aware multimodal large language model (MLLM) trained with reinforcement learning to integrate structured spatial grounding with multi-step reasoning. The model simulates human-like spatial perception by constructing a scene graph of task-relevant objects and spatial relations, and reasoning towards an answer via dense spatial rewards.
+
+## Model Description
+
+- **Base Model**: Qwen2.5-VL-3B-Instruct
+- **Training**: GRPO (Group Relative Policy Optimization) with dense spatial rewards
+- **Training Data**: STVQA-7K (7,587 spatial VQA samples)
+- **Authors**: Hunar Batra, Haoqin Tu, Hardy Chen, Yuanze Lin, Cihang Xie, Ronald Clark
+- **Institutions**: University of Oxford, UC Santa Cruz
+
+## Key Features
+
+- **Structured Spatial Reasoning**: Constructs question-focused scene subgraphs with objects, bounding boxes, and relations
+- **Dense Spatial Rewards**: Multi-objective reward function enforcing format, count, accuracy, and spatial grounding
+- **9 Spatial Reasoning Categories**: Relations, reach, size, orientation, instance location, depth, distance, count, and existence
+- **Outperforms GPT-4o**: On spatial understanding benchmarks while using only 7K training samples
+
+## Inference Template
+
+Use the following template for inference:
+
+```
+You FIRST observe the image in <observe> </observe> tags, then visualise the relevant scene graph in <scene> </scene> tags, followed by thinking about the reasoning process as an internal monologue within <think> </think> tags and then provide the final answer. The final answer MUST BE put within <answer> </answer> tags, and only return the final choice including the correct option and answer within the answer tags, e.g., <answer> (A) cat </answer>.
+
+Image size: {Width} x {Height}
+```
+
+## Usage
+
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
+from PIL import Image
+
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "OX-PIXL/SpatialThinker-3B",
+    torch_dtype="auto",
+    device_map="auto"
+)
+processor = AutoProcessor.from_pretrained("OX-PIXL/SpatialThinker-3B")
+
+# Load image
+image = Image.open("your_image.jpg")
+width, height = image.size
+
+# Prepare prompt with template
+template = f"""You FIRST observe the image in <observe> </observe> tags, then visualise the relevant scene graph in <scene> </scene> tags, followed by thinking about the reasoning process as an internal monologue within <think> </think> tags and then provide the final answer. The final answer MUST BE put within <answer> </answer> tags, and only return the final choice including the correct option and answer within the answer tags, e.g., <answer> (A) cat </answer>.
+
+Image size: {width} x {height}"""
+
+question = "Where is the cat relative to the couch? (A) on top of (B) in front of (C) behind (D) beside"
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": template + "\n\n" + question},
+        ],
+    }
+]
+
+text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(**inputs, max_new_tokens=1024)
+output = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(output)
+```
+
+## Citation
+
+```bibtex
+@misc{batra2025spatialthinkerreinforcing3dreasoning,  
+  title={SpatialThinker: Reinforcing 3D Reasoning in Multimodal LLMs via Spatial Rewards},  
+  author={Hunar Batra and Haoqin Tu and Hardy Chen and Yuanze Lin and Cihang Xie and Ronald Clark},  
+  year={2025},  
+  eprint={2511.07403},  
+  archivePrefix={arXiv},  
+  primaryClass={cs.CV},  
+  url={https://arxiv.org/abs/2511.07403},  
+}
+```
+
+## Links
+
+- 📄 **Paper**: [arXiv:2511.07403](https://arxiv.org/abs/2511.07403)
+- 🌐 **Project Page**: [hunarbatra.com/SpatialThinker](https://hunarbatra.com/SpatialThinker)
+- 💻 **GitHub**: [github.com/hunarbatra/SpatialThinker](https://github.com/hunarbatra/SpatialThinker)
+- 🤗 **Dataset**: [OX-PIXL/STVQA-7K](https://huggingface.co/datasets/OX-PIXL/STVQA-7K)