--- 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}, } ```