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Update constants.py

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  1. constants.py +9 -2
constants.py CHANGED
@@ -3,12 +3,19 @@ LEADERBOARD_INTRODUCTION = """
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  🏆 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)
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- **Join Leaderboard**: Please see the [instructions]() to participate.
 
 
 
 
 
 
 
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  """
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  CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
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  CITATION_BUTTON_TEXT = r"""
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-
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  """
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  IPC_INFO = """
 
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  🏆 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)
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+ > 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.
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+
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+ - **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.
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+ - **Easy-to-use**: DD-Ranking provides a unified interface for dataset distillation evaluation.
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+ - **Extensible**: DD-Ranking supports various datasets and models.
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+ - **Customizable**: DD-Ranking supports various data augmentations and soft label strategies.
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+
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+ **Join Leaderboard**: Please see the [instructions](https://github.com/NUS-HPC-AI-Lab/DD-Ranking/CONTRIBUTING.md) to participate.
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  """
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  CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
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  CITATION_BUTTON_TEXT = r"""
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+ COMING SOON
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  """
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  IPC_INFO = """