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LEADERBOARD_INTRODUCTION = """
# DD-Ranking Leaderboard
🏆 Welcome to the leaderboard of the **DD-Ranking**! [](https://github.com/NUS-HPC-AI-Lab/DD-Ranking)
> DD-Ranking (DD, i.e., Dataset Distillation) is an integrated and easy-to-use benchmark for dataset distillation. It aims to provide a fair evaluation scheme for DD methods that can decouple the impacts from knowledge distillation and data augmentation to reflect the real informativeness of the distilled data.
- **Fair Evaluation**: DD-Ranking provides a fair evaluation scheme for DD methods that can decouple the impacts from knowledge distillation and data augmentation to reflect the real informativeness of the distilled data.
- **Easy-to-use**: DD-Ranking provides a unified interface for dataset distillation evaluation.
- **Extensible**: DD-Ranking supports various datasets and models.
- **Customizable**: DD-Ranking supports various data augmentations and soft label strategies.
**Join Leaderboard**: Please see the [instructions](https://github.com/NUS-HPC-AI-Lab/DD-Ranking/CONTRIBUTING.md) to participate.
"""
CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
COMING SOON
"""
IPC_INFO = """
Images Per Class
"""
LABEL_TYPE_INFO = """
Hard labels are categorical, having the same format of the real dataset. Soft labels are distributions, typically generated by a pre-trained teacher model.
"""
DATASET_LIST = ["CIFAR-10", "CIFAR-100", "Tiny-ImageNet"]
IPC_LIST = ["IPC-1", "IPC-10", "IPC-50"]
LABEL_TYPE_LIST = ["Hard Label", "Soft Label"]
COLUMN_NAMES = ["Method", "Verified", "Date", "Recovery", "Improvement", "Score"]
DATA_TITLE_TYPE = ['markdown', 'markdown', 'markdown', 'number', 'number', 'number']
DATASET_MAPPING = {
"CIFAR-10": 0,
"CIFAR-100": 1,
"Tiny-ImageNet": 2,
}
IPC_MAPPING = {
"IPC-1": 0,
"IPC-10": 1,
"IPC-50": 2,
}
LABEL_MAPPING = {
"Hard Label": 0,
"Soft Label": 1,
}
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