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- ---
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- license: apache-2.0
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- ---
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-
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-
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- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/647777304ae93470ffc28913/iVKgqK6vTzCpCLVnWxmjA.png)
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-
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- # Model Card for SpaceLLaVA
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-
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- **SpaceLLaVA** uses LoRA to fine-tune [LLaVA](https://github.com/haotian-liu/LLaVA/tree/main) on a dataset designed with [VQASynth](https://github.com/remyxai/VQASynth/tree/main) to enhance spatial reasoning as in [SpatialVLM](https://spatial-vlm.github.io/)
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-
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- ## Model Details
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-
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- ### Model Description
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-
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- This model uses data synthesis techniques and publically available models to reproduce the work described in SpatialVLM to enhance the spatial reasoning of multimodal models.
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- With a pipeline of expert models, we can infer spatial relationships between objects in a scene to create VQA dataset for spatial reasoning.
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-
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-
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- - **Developed by:** remyx.ai
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- - **Model type:** MultiModal Model, Vision Language Model, LLaVA
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- - **License:** Apache-2.0
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- - **Finetuned from model:** LLaVA
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-
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- ### Model Sources
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-
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- - **Repository:** [VQASynth](https://github.com/remyxai/VQASynth/tree/main)
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- - **Paper:** [SpatialVLM](https://arxiv.org/abs/2401.12168)
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-
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- ## Uses
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-
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- Use this model to query spatial relationships between objects in a scene.
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-
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- ## Citation
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- ```
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- @article{chen2024spatialvlm,
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- title = {SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities},
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- 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},
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- journal = {arXiv preprint arXiv:2401.12168},
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- year = {2024},
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- url = {https://arxiv.org/abs/2401.12168},
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- }
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-
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- @misc{liu2023llava,
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- title={Visual Instruction Tuning},
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- author={Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae},
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- publisher={NeurIPS},
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- year={2023},
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- }
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- ```
 
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