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--- |
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license: apache-2.0 |
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task_categories: |
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- image-classification |
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- object-detection |
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- image-to-text |
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- question-answering |
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- image-text-to-text |
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language: en |
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tags: |
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- underwater |
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- multimodal |
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- LMM |
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- instruction-following |
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- scene-understanding |
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size_categories: |
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- 1M<n<10M |
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--- |
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# NautData: A Large Multimodal Dataset for Underwater Scene Understanding |
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[Paper](https://huggingface.co/papers/2510.27481) | [Project Page](https://h-embodvis.github.io/NAUTILUS/) | [Code](https://github.com/H-EmbodVis/NAUTILUS) |
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**NautData** is a large-scale underwater instruction-following dataset containing 1.45 million image-text pairs. It was constructed to bridge the gap in large-scale underwater multi-task instruction-tuning datasets, which are crucial for advancing underwater scene understanding methods. The dataset enables the development and thorough evaluation of underwater Large Multimodal Models (LMMs). |
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This dataset was introduced in the paper [NAUTILUS: A Large Multimodal Model for Underwater Scene Understanding](https://huggingface.co/papers/2510.27481). The paper also proposes the NAUTILUS model, which incorporates a Vision Feature Enhancement (VFE) module to explicitly restore clear underwater information and improve robustness against image degradation. |
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This Hugging Face repository (`Wang017/NautData`) specifically contains the processed images that form part of the NautData dataset. For the corresponding instruction-tuning annotation files, please refer to the [Wang017/NautData-Instruct](https://huggingface.co/datasets/Wang017/NautData-Instruct) dataset on the Hugging Face Hub. |
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## Supported Tasks |
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NautData supports eight underwater scene understanding tasks across image, region, and object levels, facilitating comprehensive analysis: |
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* **Classification:** Coarse-grained and fine-grained image classification. |
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* **Captioning:** Image-level and region-level description generation. |
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* **Grounding:** Referring expression comprehension and localization. |
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* **Detection:** Object detection within underwater scenes. |
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* **Visual Question Answering (VQA):** Answering questions about images. |
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* **Counting:** Counting specific objects or entities. |
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## Sample Usage |
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The following snippets, adapted from the project's GitHub repository, demonstrate how to perform single-sample inference using models trained on NautData (NAUTILUS variants). These examples illustrate how the dataset can be utilized for various underwater scene understanding tasks. |
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### NAUTILUS(LLaVA) Inference |
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```bash |
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cd LLaVA |
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CUDA_VISIBLE_DEVICES=0 python scripts/inference/inference.py --model-path "path to checkpoint" --model-base "models--liuhaotian--llava-v1.5-7b" --dinov2-weight "path to dinov2" --image "path to image" --prompt "question" |
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# prompt default is "Describe the image" |
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``` |
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### NAUTILUS(Qwen) Inference |
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```bash |
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cd qwen-vl-finetune |
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CUDA_VISIBLE_DEVICES=0 python scripts/inference.py --checkpoint "path to checkpoint" --image "path to image" --prompt "question" |
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# prompt default is "Describe the image" |
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``` |
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For more detailed usage, including dataset preparation, training, and evaluation, please refer to the [official GitHub repository](https://github.com/H-EmbodVis/NAUTILUS). |
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## Citation |
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If you find NautData or the NAUTILUS project useful in your research, please consider citing the associated paper: |
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```bibtex |
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@inproceedings{xu2025nautilus, |
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title={NAUTILUS: A Large Multimodal Model for Underwater Scene Understanding}, |
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author={Xu, Wei and Wang, Cheng and Liang, Dingkang and Zhao, Zongchuang and Jiang, Xingyu and Zhang, Peng and Bai, Xiang}, |
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booktitle={Advances in Neural Information Processing Systems}, |
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year={2025} |
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} |
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``` |
