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| LEADERBOARD_HEADER = """ | |
| <style> | |
| .header-gradient { | |
| top: 40%; | |
| bottom: 40%; | |
| padding: 10px 0px; | |
| font-weight: bold; | |
| font-size: 40px; | |
| font-family: Inter, Arial, Helvetica, sans-serif; | |
| background: linear-gradient(to right, #5EA2EF, #0072F5); | |
| -webkit-text-fill-color: transparent; | |
| -webkit-background-clip: text; | |
| } | |
| .header-normal { | |
| top: 40%; | |
| bottom: 40%; | |
| padding: 10px 0px; | |
| font-weight: bold; | |
| font-size: 40px; | |
| font-family: Inter, Arial, Helvetica, sans-serif; | |
| } | |
| </style> | |
| <div align="center"> | |
| <span class="header-gradient"> DD-Ranking </span> | |
| <span class="header-normal"> Leaderboard </span> | |
| </div> | |
| <p align="center"> | |
| | <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> | | |
| </p>""" | |
| LEADERBOARD_INTRODUCTION = """ | |
| # DD-Ranking Leaderboard | |
| 🏆 Welcome to the leaderboard of the **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/blob/main/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 generated by a teacher model pretrained on the target dataset | |
| """ | |
| WEIGHT_ADJUSTMENT_INTRODUCTION = """ | |
| The score for ranking in the following table is computed by $score = w score_{HLR} - (1 - w) score_{IOR}$, where $w$ is the weight for the HLR metric. | |
| **You can specify the weights for each metric below.** | |
| """ | |
| DATASET_LIST = ["CIFAR-10", "CIFAR-100", "Tiny-ImageNet"] | |
| IPC_LIST = ["IPC-1", "IPC-10", "IPC-50"] | |
| DATASET_IPC_LIST = { | |
| "CIFAR-10": ["IPC-1", "IPC-10", "IPC-50"], | |
| "CIFAR-100": ["IPC-1", "IPC-10", "IPC-50"], | |
| "Tiny-ImageNet": ["IPC-10", "IPC-50"], | |
| } | |
| LABEL_TYPE_LIST = ["Hard Label", "Soft Label"] | |
| METRICS = ["HLR", "IOR"] | |
| METRICS_SIGN = [1.0, -1.0] | |
| COLUMN_NAMES = ["Ranking", "Method", "Verified", "Date", "Label Type", "HLR", "IOR", "Score"] | |
| DATA_TITLE_TYPE = ['number', 'markdown', '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, | |
| } | |