Enhance dataset card for NautData: Add metadata, links, description, tasks, and sample usage

This pull request significantly enhances the dataset card for `NautData` by:
- Adding relevant `task_categories`: `image-classification`, `object-detection`, `image-to-text`, `question-answering`, `image-text-to-text`.
- Including `language: en` and descriptive `tags`: `underwater`, `multimodal`, `LMM`, `instruction-following`, `scene-understanding`.
- Adding `size_categories: 1M<n<10M` to reflect the dataset's scale.
- Providing a comprehensive description of the dataset, its purpose, and the tasks it supports, clarifying that this repository (`Wang017/NautData`) holds the images and linking to `Wang017/NautData-Instruct` for annotations.
- Including direct links to the associated paper ([NAUTILUS: A Large Multimodal Model for Underwater Scene Understanding](https://huggingface.co/papers/2510.27481)), the project page (https://h-embodvis.github.io/NAUTILUS/), and the GitHub repository (https://github.com/H-EmbodVis/NAUTILUS).
- Adding a "Sample Usage" section with practical Python code snippets for local inference, directly extracted from the project's GitHub README.
- Including the official BibTeX citation for proper attribution.

This makes the dataset much more informative and easier to discover and use for researchers.

README.md

@@ -1,3 +1,74 @@
----
-license: apache-2.0
----
+---
+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: A Large Multimodal Dataset for Underwater Scene Understanding
+
+[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}
+  }
+```