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Update constants.py
Browse files- constants.py +12 -5
constants.py
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LEADERBOARD_INTRODUCTION = """
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# DD-Ranking Leaderboard
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<p align="center">
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| <a href="https://nus-hpc-ai-lab.github.io/DD-Ranking/"><b>Documentation</b></a> | <a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking"><b>Github</b></a> | <b>Paper </b> (Coming Soon) | <a href=""><b>Twitter/X</b
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</p>
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🏆 Welcome to the leaderboard of the **DD-Ranking**!
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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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**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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"""
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LABEL_TYPE_INFO = """
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Hard labels are categorical, having the same format of the real dataset. Soft labels are
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"""
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DATASET_LIST = ["CIFAR-10", "CIFAR-100", "Tiny-ImageNet"]
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IPC_LIST = ["IPC-1", "IPC-10", "IPC-50"]
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LABEL_TYPE_LIST = ["Hard Label", "Soft Label"]
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DATASET_MAPPING = {
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"CIFAR-10": 0,
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LEADERBOARD_INTRODUCTION = """
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# DD-Ranking Leaderboard
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</h3> -->
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<p align="center">
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| <a href="https://nus-hpc-ai-lab.github.io/DD-Ranking/"><b>Documentation</b></a> | <a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking"><b>Github</b></a> | <a href=""><b>Paper </b> (Coming Soon)</a> | <a href=""><b>Twitter/X</b> (Coming Soon)</a> | <a href=""><b>Developer Slack</b> (Coming Soon)</a> |
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</p>
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🏆 Welcome to the leaderboard of the **DD-Ranking**!
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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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**Join Leaderboard**: Please see the [instructions](https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/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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"""
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LABEL_TYPE_INFO = """
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Hard labels are categorical, having the same format of the real dataset. Soft labels are generated by a teacher model pretrained on the target dataset
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"""
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WEIGHT_ADJUSTMENT_INTRODUCTION = """
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The score for ranking in the following table is computed by $score = \sum w_i score_i$, where $w_i$ is the weight for the $i$-th metric.
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**You can specify the weights for each metric below.**
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"""
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DATASET_LIST = ["CIFAR-10", "CIFAR-100", "Tiny-ImageNet"]
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IPC_LIST = ["IPC-1", "IPC-10", "IPC-50"]
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LABEL_TYPE_LIST = ["Hard Label", "Soft Label"]
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METRICS = ["HLR", "IOR"]
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COLUMN_NAMES = ["Ranking", "Method", "Verified", "Date", "Label Type", "HLR", "IOR", "Score"]
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DATA_TITLE_TYPE = ['number', 'markdown', 'markdown', 'markdown', 'markdown', 'number', 'number', 'number']
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DATASET_MAPPING = {
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"CIFAR-10": 0,
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