Update README.md

Author: BDeMo

README.md

@@ -13,7 +13,7 @@
 Fair and benchmark for dataset distillation.
 </h3> -->
 <p align="center">
-| <a href="https://nus-hpc-ai-lab.github.io/DD-Ranking/"><b>Documentation</b></a> | <a href="https://huggingface.co/spaces/Soptq/DD-Ranking"><b>Leaderboard</b></a> | <b>Paper </b> (Coming Soon) | <a href=""><b>Twitter/X</b></a> | <a href=""><b>Developer Slack</b></a> |
+| <a href="https://nus-hpc-ai-lab.github.io/DD-Ranking/"><b>Documentation</b></a> | <a href="https://huggingface.co/spaces/Soptq/DD-Ranking"><b>Leaderboard</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>
 
 
@@ -35,7 +35,7 @@ Fair and benchmark for dataset distillation.
 
 <details>
 <summary>Unfold to see more details.</summary>
-
+<br>
 Dataset Distillation (DD) aims to condense a large dataset into a much smaller one, which allows a model to achieve comparable performance after training on it. DD has gained extensive attention since it was proposed. With some foundational methods such as DC, DM, and MTT, various works have further pushed this area to a new standard with their novel designs.
 
 ![history](./static/history.png)
@@ -58,13 +58,16 @@ Motivated by this, we propose DD-Ranking, a new benchmark for DD evaluation. DD-
 
 ## About
 
+<details>
+<summary>Unfold to see more details.</summary>
+<br>
 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.
 
 <!-- Hard label is tested -->
 <!-- Keep the same compression ratio, comparing with random selection -->
 **Performance benchmark**
 
-<span style="color: #ffff00;">[To Verify]：</span>Revisit the original goal of dataset distillation: 
+Revisit the original goal of dataset distillation: 
 > The idea is to synthesize a small number of data points that do not need to come from the correct data distribution, but will, when given to the learning algorithm as training data, approximate the model trained on the original data.
 >
 
@@ -85,21 +88,33 @@ $$\text{IOR}/\text{HLR} = \frac{(\text{Acc.}{\text{syn-any}}-\text{Acc.}{\text{r
 DD-Ranking is integrated with:
 <!-- Uniform Fair Labels: loss on soft label -->
 <!-- Data Aug. -->
-- <span style="color: #ffff00;">[To Verify]：</span>Multiple [strategies](https://github.com/NUS-HPC-AI-Lab/DD-Ranking/tree/main/dd_ranking/loss) of using soft labels;
-- <span style="color: #ffff00;">[To Verify]：</span>Data augmentation, reconsidered as [optional tricks](https://github.com/NUS-HPC-AI-Lab/DD-Ranking/tree/main/dd_ranking/aug) in DD;
-- <span style="color: #ffff00;">[To Verify]：</span>Commonly used [model architectures](https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/dd_ranking/utils/networks.py) in DD.
-<span style="color: #ffff00;">[To Verify]：</span> A new ranking on representative DD methods.
+- Multiple [strategies](https://github.com/NUS-HPC-AI-Lab/DD-Ranking/tree/main/dd_ranking/loss) of using soft labels;
+- Data augmentation, reconsidered as [optional tricks](https://github.com/NUS-HPC-AI-Lab/DD-Ranking/tree/main/dd_ranking/aug) in DD;
+- Commonly used [model architectures](https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/dd_ranking/utils/networks.py) in DD.
+ A new ranking on representative DD methods.
 
 DD-Ranking is flexible and easy to use, supported by:
 <!-- Defualt configs: Customized configs -->
 <!-- Integrated classes: 1) Optimizer and etc.; 2) random selection tests (additionally, w/ or w/o hard labels)-->
-- <span style="color: #ffff00;">[To Verify]：</span>Extensive configs provided;
-- <span style="color: #ffff00;">[To Verify]：</span>Cutomized configs;
-- <span style="color: #ffff00;">[To Verify]：</span>Testing and training framework with integrated metrics.
+- Extensive configs provided;
+- Cutomized configs;
+- Testing and training framework with integrated metrics.
+
+</details>
+
+## Overview
+: DC DSA DM MTT
+: DATM SRe2L RDED D4M
+|Dataset|Hard Label|Soft Label|
+|:-|:-|:-|
+|CIFAR10|DC|DATM|
+|CIFAR100|DSA|SRe2L|
+|TinyImageNet|DM|RDED|
+||MTT|D4M|
 
 ## Coming Soon
-<span style="color: #ffff00;">[To Verify]：</span>Rank on different data augmentation methods.
-<span style="color: #ffff00;">[To Verify]：</span>Rank on different data augmentation methods.
+Rank on different data augmentation methods.
+Rank on different data augmentation methods.
 ## Tutorial
 
 Install DD-Ranking with `pip` or from [source](https://github.com/NUS-HPC-AI-Lab/DD-Ranking/tree/main):
@@ -208,7 +223,7 @@ The following results will be returned to you:
 - [Supported Models]() -->
 
 ## Contributing
-ß
+
 <!-- Only PR for the 1st version of DD-Ranking -->
 Feel free to submit grades to update the DD-Ranking list. We welcome and value any contributions and collaborations.
 Please check out [CONTRIBUTING.md](./CONTRIBUTING.md) for how to get involved.