---
license: apache-2.0
---


![image/png](https://cdn-uploads.huggingface.co/production/uploads/647777304ae93470ffc28913/iVKgqK6vTzCpCLVnWxmjA.png)

# Model Card for SpaceLLaVA

**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/)

## Model Details

### Model Description

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.
With a pipeline of expert models, we can infer spatial relationships between objects in a scene to create VQA dataset for spatial reasoning.


- **Developed by:** remyx.ai
- **Model type:** MultiModal Model, Vision Language Model, LLaVA
- **License:** Apache-2.0
- **Finetuned from model:** LLaVA

### Model Sources

- **Repository:** [VQASynth](https://github.com/remyxai/VQASynth/tree/main)
- **Paper:** [SpatialVLM](https://arxiv.org/abs/2401.12168)

## Uses

Use this model to query spatial relationships between objects in a scene.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1WPE7Br5A5ERSij8BL1M22EoEMLVkD8EP?usp=sharing)


Try it on Discord: http://discord.gg/b2yGuCNpuC


![image/png](https://cdn-uploads.huggingface.co/production/uploads/647777304ae93470ffc28913/Rsu5VpDgdZh9jemw97w8T.png)

## Deployment

`docker build -f Dockerfile -t spacellava-server:latest`

`docker run -it --rm --gpus all -p8000:8000 -p8001:8001 -p8002:8002 --shm-size 12G spacellava-server:latest`

`python3 client.py --image_path "https://remyx.ai/assets/spatialvlm/warehouse_rgb.jpg" --prompt "What is the distance between the man in the red hat and the pallet of boxes?"`

## 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{liu2023llava,
      title={Visual Instruction Tuning},
      author={Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae},
      publisher={NeurIPS},
      year={2023},
}
```