| --- |
| license: apache-2.0 |
| task_categories: |
| - image-classification |
| - object-detection |
| - image-to-text |
| - question-answering |
| - image-text-to-text |
| language: en |
| tags: |
| - underwater |
| - multimodal |
| - LMM |
| - instruction-following |
| - scene-understanding |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
| # NautData |
|
|
| [Paper](https://huggingface.co/papers/2510.27481) | [Project Page](https://h-embodvis.github.io/NAUTILUS/) | [Code](https://github.com/H-EmbodVis/NAUTILUS) |
|
|
| **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). |
|
|
| 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. |
|
|
| 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. |
|
|
| ## Supported Tasks |
|
|
| NautData supports eight underwater scene understanding tasks across image, region, and object levels, facilitating comprehensive analysis: |
|
|
| * **Classification:** Coarse-grained and fine-grained image classification. |
| * **Captioning:** Image-level and region-level description generation. |
| * **Grounding:** Referring expression comprehension and localization. |
| * **Detection:** Object detection within underwater scenes. |
| * **Visual Question Answering (VQA):** Answering questions about images. |
| * **Counting:** Counting specific objects or entities. |
|
|
| ## Sample Usage |
|
|
| 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. |
|
|
| ### NAUTILUS(LLaVA) Inference |
|
|
| ```bash |
| cd LLaVA |
| 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" |
| # prompt default is "Describe the image" |
| ``` |
|
|
| ### NAUTILUS(Qwen) Inference |
|
|
| ```bash |
| cd qwen-vl-finetune |
| CUDA_VISIBLE_DEVICES=0 python scripts/inference.py --checkpoint "path to checkpoint" --image "path to image" --prompt "question" |
| # prompt default is "Describe the image" |
| ``` |
|
|
| For more detailed usage, including dataset preparation, training, and evaluation, please refer to the [official GitHub repository](https://github.com/H-EmbodVis/NAUTILUS). |
|
|
| ## Citation |
|
|
| If you find NautData or the NAUTILUS project useful in your research, please consider citing the associated paper: |
|
|
| ```bibtex |
| @inproceedings{xu2025nautilus, |
| title={NAUTILUS: A Large Multimodal Model for Underwater Scene Understanding}, |
| author={Xu, Wei and Wang, Cheng and Liang, Dingkang and Zhao, Zongchuang and Jiang, Xingyu and Zhang, Peng and Bai, Xiang}, |
| booktitle={Advances in Neural Information Processing Systems}, |
| year={2025} |
| } |
| ``` |
