Improve dataset card: Add description, paper link, tags, and sample usage for AQUA20

This PR enhances the AQUA20 dataset card by:
- Adding a descriptive overview of the dataset based on the paper's abstract to the content section.
- Including a direct link to the associated paper on the Hugging Face Hub (https://huggingface.co/papers/2506.17455) for easy access.
- Providing a Python code snippet for sample usage, demonstrating how to load and interact with the dataset using the `datasets` library.
- Adding relevant tags (`underwater`, `marine-biology`, `species-classification`, `benchmark`) to the metadata for improved discoverability and categorization.

The existing comprehensive dataset information and the BibTeX citation are preserved.

README.md

@@ -1,4 +1,13 @@
 ---
+language:
+- en
+task_categories:
+- image-classification
+tags:
+- underwater
+- marine-biology
+- species-classification
+- benchmark
 dataset_info:
   features:
   - name: image
@@ -43,12 +52,46 @@ configs:
     path: data/train-*
   - split: test
     path: data/test-*
-task_categories:
-- image-classification
-language:
-- en
 ---
 
+# AQUA20 Dataset
+
+The AQUA20 dataset is a comprehensive benchmark dataset designed for **underwater species classification** under challenging real-world conditions. It comprises 8,171 underwater images across 20 distinct marine species, specifically curated to reflect environmental complexities such as turbidity, low illumination, and occlusion, which commonly degrade the performance of standard vision systems. This dataset provides a valuable resource for advancing robust visual recognition in aquatic environments.
+
+The dataset was presented in the paper [AQUA20: A Benchmark Dataset for Underwater Species Classification under Challenging Conditions](https://huggingface.co/papers/2506.17455).
+
+## Sample Usage
+
+You can easily load the AQUA20 dataset using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("AQUA20")
+
+# Access the training split
+train_dataset = dataset["train"]
+print(f"Number of examples in training set: {len(train_dataset)}")
+
+# Access the test split
+test_dataset = dataset["test"]
+print(f"Number of examples in test set: {len(test_dataset)}")
+
+# Example of accessing an image and its label
+example = train_dataset[0]
+image = example["image"]
+label = example["label"]
+print(f"Example label: {label} (Class Name: {train_dataset.features['label'].names[label]})")
+
+# You can optionally display the image if you have PIL and matplotlib installed
+# import matplotlib.pyplot as plt
+# plt.imshow(image)
+# plt.title(f"Label: {train_dataset.features['label'].names[label]}")
+# plt.axis('off')
+# plt.show()
+```
+
 ## Citation
 
 ```bibtex
@@ -60,4 +103,5 @@ language:
       archivePrefix={arXiv},
       primaryClass={cs.CV},
       url={https://arxiv.org/abs/2506.17455}, 
-}
+}
+```