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--- |
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task_categories: |
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- image-to-image |
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license: cc-by-nc-4.0 |
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tags: |
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- image-enhancement |
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- hdr |
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- multi-exposure |
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--- |
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# UNICE Dataset Description |
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This is the dataset released with the paper: [UNICE: Training A Universal Image Contrast Enhancer](https://huggingface.co/papers/2507.17157). |
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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. |
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**Code:** [https://github.com/BeyondHeaven/UNICE](https://github.com/BeyondHeaven/UNICE) |
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## 1. `UNICEdataset.zip` |
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- **Type**: Multi-Exposure Sequences (MES) |
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- **Content**: sRGB images rendered from HDR raw images using an emulated ISP pipeline. |
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- **Structure**: Each sequence contains multiple images of the same scene with varying exposure values (EVs), from -3EV to +3EV. |
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- **Purpose**: Serves as input data for training and evaluating exposure and contrast enhancement models. |
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## 2. `pseudoGT.zip` |
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- **Type**: Pseudo Ground Truths |
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- **Content**: High-quality sRGB images generated by fusing the MES using an ensemble of multi-exposure fusion (MEF) techniques. |
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- **Purpose**: Used as the target output (pseudo-GT) for supervised training of enhancement models. |
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## 3. `pseudoGT_arniqa.csv` |
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- **Type**: Pseudo Ground Truth Quality Scores |
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- **Content**: ARNIQA scores for each pseudoGT image, indicating perceptual quality. |
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- **Purpose**: Enables quality-aware selection of pseudoGTs. Low-quality samples (e.g., score < 0.5) can be filtered out to improve training. |
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## Sample Usage |
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To download the dataset using Git LFS: |
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```bash |
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git lfs install |
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git clone https://huggingface.co/datasets/lahaina/UNICE |
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``` |
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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. |
