constant.py

LEADERBOARD_INTRODUCTION = """
# DD-Ranking Leaderboard

🏆 Welcome to the leaderboard of the **DD-Ranking**! [![Code](https://img.shields.io/github/stars/NUS-HPC-AI-Lab/DD-Ranking.svg?style=social&label=Official)](https://github.com/NUS-HPC-AI-Lab/DD-Ranking) 

**Join Leaderboard**: Please see the [instructions]() to participate.
"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""

"""

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,
}