Improve dataset card: Add metadata, paper/code links, and sample usage

This PR significantly enhances the dataset card by:
- Adding the `image-to-image` task category and relevant tags (`image-enhancement`, `hdr`, `multi-exposure`) for better discoverability.
- Specifying the `cc-by-nc-4.0` license.
- Linking to the official paper page: https://huggingface.co/papers/2507.17157.
- Including a link to the GitHub repository.
- Providing a basic sample usage section to guide users.

README.md

@@ -1,10 +1,20 @@
+---
+task_categories:
+- image-to-image
+license: cc-by-nc-4.0
+tags:
+- image-enhancement
+- hdr
+- multi-exposure
+---
+
 # UNICE Dataset Description
 
-This is the dataset released with the paper:
+This is the dataset released with the paper: [UNICE: Training A Universal Image Contrast Enhancer](https://huggingface.co/papers/2507.17157).
 
-**UNICE: Training A Universal Image Contrast Enhancer**
+The UNICE dataset is crucial for training a universal and generalized model for various image contrast enhancement tasks, free of costly human labeling. It comprises HDR raw images used to render multi-exposure sequences (MES) and corresponding pseudo sRGB ground-truths via multi-exposure fusion.
 
-The dataset consists of two main components:
+**Code:** [https://github.com/RuodaiCui/UNICE](https://github.com/RuodaiCui/UNICE)
 
 ## 1. `UNICEdataset.zip`
 - **Type**: Multi-Exposure Sequences (MES)
@@ -16,3 +26,14 @@ The dataset consists of two main components:
 - **Type**: Pseudo Ground Truths
 - **Content**: High-quality sRGB images generated by fusing the MES using an ensemble of multi-exposure fusion (MEF) techniques.
 - **Purpose**: Used as the target output (pseudo-GT) for supervised training of enhancement models.
+
+## Sample Usage
+
+To download the dataset using Git LFS:
+
+```bash
+git lfs install
+git clone https://huggingface.co/datasets/lahaina/UNICE
+```
+
+After downloading, you will find `UNICEdataset.zip` and `pseudoGT.zip`. For model training (e.g., as described in the associated code repository), you would typically extract these files and configure your `dataset_folder` to point to the extracted data. For instance, you might place the extracted contents into a directory like `data/exposure` and use it with the training scripts.