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Update constants.py

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  1. constants.py +12 -5
constants.py CHANGED
@@ -1,8 +1,9 @@
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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></a> | <a href=""><b>Developer Slack</b></a> |
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  </p>
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  🏆 Welcome to the leaderboard of the **DD-Ranking**!
@@ -14,7 +15,7 @@ LEADERBOARD_INTRODUCTION = """
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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"
@@ -27,15 +28,21 @@ Images Per Class
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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 distributions, typically generated by a pre-trained teacher model.
 
 
 
 
 
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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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- COLUMN_NAMES = ["Method", "Verified", "Date", "Recovery", "Improvement", "Score"]
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- DATA_TITLE_TYPE = ['markdown', 'markdown', 'markdown', 'number', 'number', 'number']
 
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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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+
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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,