Create README.md

README.md

@@ -0,0 +1,141 @@
+---
+license: apache-2.0
+datasets:
+- internlm/Spatial-SSRL-81k
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-VL-3B-Instruct
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- multimodal
+- spatial
+- sptial understanding
+- self-supervised learning
+---
+
+
+# Spatial-SSRL-3B
+
+📖<a href="https://arxiv.org/abs/2510.27606">Paper</a>| 🏠<a href="https://github.com/InternLM/Spatial-SSRL">Github</a> |🤗<a href="https://huggingface.co/internlm/Spatial-SSRL-7B">Spatial-SSRL-7B Model</a> | 
+🤗<a href="https://huggingface.co/internlm/Spatial-SSRL-Qwen3VL-4B">Spatial-SSRL-Qwen3VL-4B Model</a> |
+  🤗<a href="https://huggingface.co/datasets/internlm/Spatial-SSRL-81k">Spatial-SSRL-81k Dataset</a> | 📰<a href="https://huggingface.co/papers/2510.27606">Daily Paper</a> 
+
+Spatial-SSRL-7B is a large vision-language model targeting spatial understanding, built on the base of Qwen2.5-VL-7B. It's optimized by applying Spatial-SSRL, a lightweight self-supervised reinforcement learning
+paradigm which can scale RLVR efficiently. The model demonstrates strong spatial intelligence while preserving the original general visual capabilities of the base model. 
+
+## 📢 News
+- 🚀 [2025/11/24] We have released the [🤗Spatial-SSRL-Qwen3VL-4B Model](https://huggingface.co/internlm/Spatial-SSRL-Qwen3VL-4B), initialized from Qwen3-VL-4B-Instruct.
+- 🚀 [2025/11/03] Now you can try out Spatial-SSRL-7B on [🤗Spatial-SSRL Space](https://huggingface.co/spaces/yuhangzang/Spatial-SSRL).
+- 🚀 [2025/11/03] We have released the [🤗Spatial-SSRL-7B Model](https://huggingface.co/internlm/Spatial-SSRL-7B), and [🤗Spatial-SSRL-81k Dataset](https://huggingface.co/datasets/internlm/Spatial-SSRL-81k).
+- 🚀 [2025/11/02] We have released the [🏠Spatial-SSRL Repository](https://github.com/InternLM/Spatial-SSRL).
+
+## 🌈 Overview
+We are thrilled to introduce <strong>Spatial-SSRL</strong>, a novel self-supervised RL paradigm aimed at enhancing LVLM spatial understanding. 
+By optimizing Qwen2.5-VL-7B with Spatial-SSRL, the model exhibits stronger spatial intelligence across seven spatial understanding benchmarks in both image and video settings.
+</p>
+<p style="text-align: center;"> 
+  <img src="assets/teaser_1029final.png" alt="Teaser" width="100%"> 
+</p>
+Spatial-SSRL is a <strong>lightweight</strong> tool-free framework that is natually compatible with the RLVR training paradigm and easy to extend to a multitude of pretext tasks.
+Five tasks are currently formulated in the framework, requiring only ordinary RGB and RGB-D images. <strong>And we welcome you to join Spatial-SSRL with effective pretext tasks to further strengthen the capabilities of LVLMs!</strong>
+
+<p style="text-align: center;"> 
+  <img src="assets/pipeline_1029final.png" alt="Pipeline" width="100%"> 
+</p>
+
+## 💡 Highlights
+- 🔥 **Highly Scalable:** Spatial-SSRL uses ordinary raw RGB and RGB-D images instead of richly-annotated public datasets or manual labels for data curation, making it highly scalable.
+- 🔥 **Cost-effective:** Avoiding the need for human labels or API calls for general LVLMs throughout the entire pipeline endows Spatial-SSRL with cost-effectiveness.
+- 🔥 **Lightweight:** Prior approaches for spatial understanding heavily rely on annotation of external tools, incurring inherent errors in training data and additional cost. In constrast, Spatial-SSRL is completely tool-free and can easily be extended to more self-supervised tasks. 
+- 🔥 **Naturally Verifiable:** Intrinsic supervisory signals determined by pretext objectives are naturally verifiable, aligning Spatial-SSRL well with the RLVR paradigm.
+<p style="text-align: center;"> 
+  <img src="assets/comparison_v2.png" alt="Teaser" width="100%"> 
+</p>
+
+## 📊 Results
+We train Qwen2.5-VL-3B and Qwen2.5-VL-7B with our Spatial-SSRL paradigm and the experimental results across seven spatial understanding benchmarks are shown below.
+<p style="text-align: center;"> 
+  <img src="assets/exp_result.png" alt="Pipeline" width="100%"> 
+</p>
+
+## 🛠️ Usage
+To directly experience <strong>Spatial-SSRL-7B</strong>, you can try it out on [🤗Spatial-SSRL Space](https://huggingface.co/spaces/yuhangzang/Spatial-SSRL)!
+
+Here we provide a code snippet for you to start a simple trial of <strong>Spatial-SSRL-7B</strong> on your own device. You can download the model from 🤗<a href="https://huggingface.co/internlm/Spatial-SSRL-7B">Spatial-SSRL-7B Model</a > before your trial!
+</p>
+
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+model_path = "internlm/Spatial-SSRL-7B" #You can change it to your own local path if deployed already
+img_path = "examples/eg1.jpg"
+question = "Consider the real-world 3D locations of the objects. Which object has a higher location? A. yellow bear kite B. building"
+#We recommend using the format prompt to make the inference consistent with training
+format_prompt = "\n You FIRST think about the reasoning process as an internal monologue and then provide the final answer. The reasoning process MUST BE enclosed within <think> </think> tags. The final answer MUST BE put in \\boxed{}."
+
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    model_path, torch_dtype="auto", device_map="auto"
+)
+processor = AutoProcessor.from_pretrained(model_path)
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": img_path,
+            },
+            {"type": "text", "text": question + format_prompt},
+        ],
+    }
+]
+
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+
+generated_ids = model.generate(**inputs, max_new_tokens=4096, do_sample=False)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print("Model Response:", output_text)
+```
+
+## Cases
+<p style="text-align: center;"> 
+  <img src="assets/case1.jpg" alt="Teaser" width="100%"> 
+</p>
+<p style="text-align: center;"> 
+  <img src="assets/case2.jpg" alt="Teaser" width="100%"> 
+</p>
+
+## ✒️Citation
+If you find our model useful, please kindly cite:
+```
+@article{liu2025spatial,
+  title={Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised Reinforcement Learning},
+  author={Liu, Yuhong and Zhang, Beichen and Zang, Yuhang and Cao, Yuhang and Xing, Long and Dong, Xiaoyi and Duan, Haodong and Lin, Dahua and Wang, Jiaqi},
+  journal={arXiv preprint arXiv:2510.27606},
+  year={2025}
+}
+```
+
+## 📄 License
+![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg) ![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg) 
+
+**Usage and License Notices**: The data and code are intended and licensed for research use only.