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Model: remyxai/SpaceQwen2.5-VL-3B-Instruct Source: Original Platform
2026-09-02 15:36:14 +08:00
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+---
+base_model:
+- Qwen/Qwen2.5-VL-3B-Instruct
+datasets:
+- remyxai/OpenSpaces
+language:
+- en
+library_name: transformers
+license_name: qwen-research
+license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE
+pipeline_tag: image-text-to-text
+tags:
+- remyx
+- vqasynth
+- spatial-reasoning
+- multimodal
+- vlm
+- vision-language
+- robotics
+- distance-estimation
+- embodied-ai
+- quantitative-spatial-reasoning
+new_version: remyxai/SpaceThinker-Qwen2.5VL-3B
+model-index:
+  - name: SpaceQwen2.5-VL-3B-Instruct
+    results:
+      - task:
+          type: visual-question-answering
+          name: Spatial Reasoning
+        dataset:
+          name: 3DSRBench
+          type: benchmark
+        metrics:
+          - type: success_rate
+            name: Overall Success Rate
+            value: 0.515
+        results_by_subcategory:
+          - name: 3D Positional Relation / Orientation
+            success_rate: 0.4706
+          - name: Object Localization / 3D Localization
+            success_rate: 0.5629
+          - name: Object Properties / Size
+            success_rate: 0.5116
+      - task:
+          type: visual-question-answering
+          name: Spatial Reasoning
+        dataset:
+          name: BLINK
+          type: benchmark
+        metrics:
+          - type: success_rate
+            name: Overall Success Rate
+            value: 0.5
+        results_by_subcategory:
+          - name: 3D Positional Relation / Orientation
+            success_rate: 0.6503
+          - name: Counting / Object Counting
+            success_rate: 0.6083
+          - name: Depth and Distance / Relative
+            success_rate: 0.5161
+          - name: Object Localization / 2D Localization
+            success_rate: 0.4426
+          - name: Point and Object Tracking / Point Correspondence
+            success_rate: 0.2849
+      - task:
+          type: visual-question-answering
+          name: Spatial Reasoning
+        dataset:
+          name: MMIU
+          type: benchmark
+        metrics:
+          - type: success_rate
+            name: Overall Success Rate
+            value: 0.3045
+        results_by_subcategory:
+          - name: Camera and Image Transformation / 2D Transformation
+            success_rate: 0.245
+          - name: Camera and Image Transformation / 3D Camera Pose
+            success_rate: 0.215
+          - name: Camera and Image Transformation / Camera Motion
+            success_rate: 0.4436
+          - name: Depth and Distance / Absolute
+            success_rate: 0.265
+          - name: Object Localization / 3D Localization
+            success_rate: 0.48
+          - name: Point and Object Tracking / 3D Tracking
+            success_rate: 0.24
+          - name: Point and Object Tracking / Point Correspondence
+            success_rate: 0.28
+      - task:
+          type: visual-question-answering
+          name: Spatial Reasoning
+        dataset:
+          name: MMVP
+          type: benchmark
+        metrics:
+          - type: success_rate
+            name: Overall Success Rate
+            value: 0.5767
+        results_by_subcategory:
+          - name: Others / Miscellaneous
+            success_rate: 0.5767
+      - task:
+          type: visual-question-answering
+          name: Spatial Reasoning
+        dataset:
+          name: QSpatialBench-Plus
+          type: benchmark
+        metrics:
+          - type: success_rate
+            name: Overall Success Rate
+            value: 0.3663
+        results_by_subcategory:
+          - name: Depth and Distance / Absolute
+            success_rate: 0.3663
+      - task:
+          type: visual-question-answering
+          name: Spatial Reasoning
+        dataset:
+          name: QSpatialBench-ScanNet
+          type: benchmark
+        metrics:
+          - type: success_rate
+            name: Overall Success Rate
+            value: 0.33
+        results_by_subcategory:
+          - name: Depth and Distance / Absolute
+            success_rate: 0.216
+          - name: Object Properties / Size
+            success_rate: 0.4444
+      - task:
+          type: visual-question-answering
+          name: Spatial Reasoning
+        dataset:
+          name: RealWorldQA
+          type: benchmark
+        metrics:
+          - type: success_rate
+            name: Overall Success Rate
+            value: 0.4392
+        results_by_subcategory:
+          - name: Others / Miscellaneous
+            success_rate: 0.4392
+      - task:
+          type: visual-question-answering
+          name: Spatial Reasoning
+        dataset:
+          name: SpatialSense
+          type: benchmark
+        metrics:
+          - type: success_rate
+            name: Overall Success Rate
+            value: 0.6554
+        results_by_subcategory:
+          - name: 3D Positional Relation / Orientation
+            success_rate: 0.6554
+      - task:
+          type: visual-question-answering
+          name: Spatial Reasoning
+        dataset:
+          name: VGBench
+          type: benchmark
+        metrics:
+          - type: success_rate
+            name: Overall Success Rate
+            value: 0.2615
+        results_by_subcategory:
+          - name: Camera and Image Transformation / 2D Transformation
+            success_rate: 0.2277
+          - name: Camera and Image Transformation / 3D Camera Pose
+            success_rate: 0.2438
+          - name: Depth and Distance / Absolute
+            success_rate: 0.2696
+          - name: Depth and Distance / Relative
+            success_rate: 0.1945
+          - name: Object Localization / 3D Localization
+            success_rate: 0.3733
+          - name: Point and Object Tracking / 3D Tracking
+            success_rate: 0.2655
+      - task:
+          type: visual-question-answering
+          name: Spatial Reasoning
+        dataset:
+          name: VSI-Bench_8
+          type: benchmark
+        metrics:
+          - type: success_rate
+            name: Overall Success Rate
+            value: 0.2322
+        results_by_subcategory:
+          - name: 3D Positional Relation / Orientation
+            success_rate: 0.3843
+          - name: Counting / Object Counting
+            success_rate: 0.1715
+          - name: Depth and Distance / Absolute
+            success_rate: 0.0299
+          - name: Depth and Distance / Relative
+            success_rate: 0.3521
+          - name: Object Properties / Size
+            success_rate: 0.2323
+          - name: Others / Miscellaneous
+            success_rate: 0.2525
+      - task:
+          type: visual-question-answering
+          name: Spatial Reasoning
+        dataset:
+          name: VSR-ZeroShot
+          type: benchmark
+        metrics:
+          - type: success_rate
+            name: Overall Success Rate
+            value: 0.7373
+        results_by_subcategory:
+          - name: 3D Positional Relation / Orientation
+            success_rate: 0.7373
+      - task:
+          type: visual-question-answering
+          name: Spatial Reasoning
+        dataset:
+          name: cvbench
+          type: benchmark
+        metrics:
+          - type: success_rate
+            name: Overall Success Rate
+            value: 0.5179
+        results_by_subcategory:
+          - name: Counting / Object Counting
+            success_rate: 0.6168
+          - name: Depth and Distance / Relative
+            success_rate: 0.4925
+          - name: Object Localization / 3D Localization
+            success_rate: 0.4446
+      - task:
+          type: visual-question-answering
+          name: Spatial Reasoning
+        dataset:
+          name: spatialbench
+          type: benchmark
+        metrics:
+          - type: success_rate
+            name: Overall Success Rate
+            value: 0.4879
+        results_by_subcategory:
+          - name: 3D Positional Relation / Orientation
+            success_rate: 0.5294
+          - name: Counting / Object Counting
+            success_rate: 0.7
+          - name: Object Properties / Existence
+            success_rate: 0.45
+          - name: Object Properties / Reachability
+            success_rate: 0.5
+          - name: Object Properties / Size
+            success_rate: 0.25
+---
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/647777304ae93470ffc28913/v4edJliSy46xBA8g5ZXf8.png" width="500"/>
+
+# SpaceQwen2.5-VL-3B-Instruct
+
+The model was presented in the paper [OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models](https://huggingface.co/papers/2506.03135). More information can be found at the [project page](https://qizekun.github.io/omnispatial/).
+
+
+- **Model Type:** Multimodal, Vision-Language Model
+- **Architecture**: `Qwen2.5-VL-3B-Instruct`
+- **Model Size:** 3.75B parameters (FP16)
+- **Finetuned from:** Qwen/Qwen2.5-VL-3B-Instruct
+- **Finetune Strategy:** LoRA (Low-Rank Adaptation)
+- **License:** Apache-2.0
+
+### Model Overview
+
+This model uses data synthesis techniques and publicly available models to reproduce the work described in SpatialVLM to enhance the spatial reasoning of multimodal models.
+With a pipeline of expert models, we can infer spatial relationships between objects in a scene to create a VQA dataset for spatial reasoning.
+
+
+## Running SpaceQwen2.5-VL-3B-Instruct
+
+### Ollama
+To launch with ollama, run:
+```bash
+ollama run hf.co/remyxai/SpaceQwen2.5-VL-3B-Instruct:latest
+```
+
+### Transformers
+
+Install qwen dependencies:
+```
+pip install qwen-vl-utils[decord]==0.0.8
+```
+
+To run inference on a sample image:
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "remyxai/SpaceQwen2.5-VL-3B-Instruct", torch_dtype="auto", device_map="auto"
+)
+processor = AutoProcessor.from_pretrained("remyxai/SpaceQwen2.5-VL-3B-Instruct")
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://raw.githubusercontent.com/remyxai/VQASynth/refs/heads/main/assets/warehouse_sample_2.jpeg",
+            },
+            {"type": "text", "text": "What is the height of the man in the red hat in feet?"},
+        ],
+    }
+]
+
+# Preparation for inference
+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")
+
+# Inference: Generation of the output
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+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(output_text)
+```
+
+### GGUF
+
+Or run **SpaceQwen2.5-VL-3B-Instruct** using **llama.cpp**:
+```bash
+./llama-qwen2vl-cli -m /path/to/SpaceQwen2.5-VL-3B-Instruct/SpaceQwen2.5-VL-3B-Instruct-F16.gguf \
+                    --mmproj /path/to/SpaceQwen2.5-VL-3B-Instruct/spaceqwen2.5-vl-3b-instruct-vision.gguf \
+                    -p "What's the height of the man in the red hat?" \
+                    --image /path/to/warehouse_sample_2.jpeg --threads 24 -ngl 99
+```
+
+
+## Dataset & Training
+
+**SpaceQwen2.5-VL-3B-Instruct** uses LoRA to fine-tune [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) on the 
+[OpenSpaces](https://huggingface.co/datasets/salma-remyx/OpenSpaces) dataset.
+
+**Dataset Summary**:
+- ~10k synthetic spatial reasoning traces
+- Question types: spatial relations (distances (units), above, left-of, contains, closest to)
+- Format: image (RGB) + question + answer
+
+- **Dataset:** [OpenSpaces](https://huggingface.co/datasets/remyxai/OpenSpaces)
+- **Code:** [VQASynth](https://github.com/remyxai/VQASynth/tree/main)
+- **Reference:** [SpatialVLM](https://spatial-vlm.github.io/)
+
+Scripts for LoRA SFT available at [trl](https://github.com/huggingface/trl/blob/main/examples/scripts/sft_vlm.py)
+
+
+## Model Evaluation
+
+### SpatialScore
+**SpaceQwen** shines in the 3D positional relations categories of the SpatialScore-Hard comparison featured in the table below:
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/647777304ae93470ffc28913/sNei_Js6IjEKKHK717PeZ.png)
+
+Read more about the comprehensive spatial reasoning benchmark: [SpatialScore](https://haoningwu3639.github.io/SpatialScore/).
+
+The following chart compares performance between **SpaceQwen** and **SpaceThinker** on the **SpatialScore** benchmarks sources.
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/647777304ae93470ffc28913/M9DMOXkhef7-LBzGNxykm.png" alt="SpaceQwen_v_SpaceThinker" style="max-height: 250px;">
+
+### OmniSpatial
+
+**OmniSpatial** is another comprehensive spatial reasoning benchmark that assesses dynamic reasoning, complex spatial logic, spatial interaction, and perspective-taking capabilities.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/647777304ae93470ffc28913/EDHmFRztyTI-lhdgEYZzP.png)
+
+Learn more about [OmniSpatial](https://qizekun.github.io/omnispatial/).
+
+### SpaCE-10 
+
+
+
+| **Model**                | **Overall** | **EQ**   | **SQ**   | **SA**   | **OO**   | **OS**   | **EP**   | **FR**   | **SP**   | **Source**  |
+|--------------------------|-------------|----------|----------|----------|----------|----------|----------|----------|----------|-------------|
+| InternVL2.5-4B           | **36.01**   | **34.30**| 34.40    | 43.60    | 44.40    | 16.50    | **31.10**| **50.10**| **33.70**| Table       |
+| SpaceThinker             | 32.72       | 32.73    | 24.81    | 47.26    | 50.33    | 33.63    | 9.25     | 37.54    | 26.25    | GPT Eval    |
+| SpaceOm                  | 32.32       | 32.47    | 24.81    | **47.63**| 50.00    | 32.52    | 9.12     | 37.04    | 25.00    | GPT Eval    |
+| **SpaceQwen**            | 31.98       | 31.19    | 25.89    | 41.61    | **51.98**| **35.18**| 10.97    | 36.54    | 22.50    | GPT Eval    |
+| Qwen2.5-VL-3B-Instruct   | 30.00       | 31.70    | **45.50**| 39.00    | 43.00    | 25.30    | 11.50    | 22.80    | 21.20    | Table       |
+
+
+**Legend:**
+- EQ: Entity Quantification
+- SQ: Scene Quantification
+- SA: Size Assessment
+- OO: Object-Object spatial relations
+- OS: Object-Scene spatial relations
+- EP: Entity Presence
+- FR: Functional Reasoning
+- SP: Spatial Planning
+
+> ℹ️ Note: Scores for SpaceQwen, SpaceThinker, SpaceOm are generated via `gpt_eval_score` on single-choice (`*-single`) versions of the SpaCE-10 benchmark tasks. Other entries reflect leaderboard accuracy scores from the official SpaCE-10 evaluation table.
+
+Read more about the [SpaCE-10 benchmark](https://arxiv.org/pdf/2506.07966v1) or see [results here](https://huggingface.co/datasets/salma-remyx/SpaceQwen_SpaCE-10_Results/blob/main/20250612_013312_results.json)
+
+
+### SIRI-Bench
+
+[SIRI-Bench](https://arxiv.org/pdf/2506.14512v1) is a video-based benchmark designed to evaluate complex spatial reasoning capabilities
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/647777304ae93470ffc28913/r17vO_1vpwEoLpARo5F1t.png)
+
+
+### MindCube
+
+[MindCube](https://huggingface.co/datasets/MLL-Lab/MindCube) is a benchmark for assessing [Spatial Mental Modeling from Limited Views](https://arxiv.org/pdf/2506.21458)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/647777304ae93470ffc28913/t1lhP6_1B3H2A4PEubCyc.png)
+
+
+
+## ⚠️ Limitations & Ethical Considerations
+
+- Performance may degrade in cluttered environments or camera perspective.
+- This model was fine-tuned using synthetic reasoning over an internet image dataset.
+- Multimodal biases inherent to the base model (Qwen2.5-VL) may persist.
+- Not intended for use in safety-critical or legal decision-making.
+
+> Users are encouraged to evaluate outputs critically and consider fine-tuning for domain-specific safety and performance.
+
+## Citation
+```
+@article{chen2024spatialvlm,
+  title = {SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities},
+  author = {Chen, Boyuan and Xu, Zhuo and Kirmani, Sean and Ichter, Brian and Driess, Danny and Florence, Pete and Sadigh, Dorsa and Guibas, Leonidas and Xia, Fei},
+  journal = {arXiv preprint arXiv:2401.12168},
+  year = {2024},
+  url = {https://arxiv.org/abs/2401.12168},
+}
+
+@misc{qwen2.5-VL,
+    title = {Qwen2.5-VL},
+    url = {https://qwenlm.github.io/blog/qwen2.5-vl/},
+    author = {Qwen Team},
+    month = {January},
+    year = {2025}
+}
+
+@article{wu2025spatialscore,
+    author    = {Wu, Haoning and Huang, Xiao and Chen, Yaohui and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
+    title     = {SpatialScore: Towards Unified Evaluation for Multimodal Spatial Understanding},
+    journal   = {arXiv preprint arXiv:2505.17012},
+    year      = {2025},
+}
+
+@article{omnispatial25,
+  title   = {OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models},
+  author  = {Mengdi Jia and Zekun Qi and Shaochen Zhang and Wenyao Zhang and Xinqiang Yu and Jiawei He and He Wang and Li Yi},
+  journal = {arXiv preprint arXiv:2506.03135},
+  year = {2025}
+}
+
+@article{song2025siribench,
+  title   = {{SIRI-Bench}: Challenging VLMs’ Spatial Intelligence through Complex Reasoning Tasks},
+  author  = {Song, Zijian and Lin, Xiaoxin and Huang, Qiuming and Wang, Guangrun and Lin, Liang},
+  journal = {arXiv preprint arXiv:2506.14512},
+  year    = {2025},
+  url     = {https://arxiv.org/abs/2506.14512}
+}
+
+@misc{yin2025spatial,
+  title        = {Spatial Mental Modeling from Limited Views},
+  author       = {Baiqiao Yin and Qineng Wang and Pingyue Zhang and Jianshu Zhang
+                  and Kangrui Wang and Zihan Wang and Jieyu Zhang
+                  and Keshigeyan Chandrasegaran and Han Liu and Ranjay Krishna
+                  and Saining Xie and Manling Li and Jiajun Wu and Li Fei-Fei},
+  year         = {2025},
+  archivePrefix= {arXiv},
+  eprint       = {2506.21458},
+  primaryClass = {cs.AI},
+  url          = {https://arxiv.org/abs/2506.21458}
+}
+
+```