update README

README.md

@@ -10,9 +10,9 @@ viewer: False
 
 <div align="center">
 
-![MULTI](./overview.png)
+![MULTI](./docs/static/images/overview.png)
 
-🌐 [Website](https://OpenDFM.github.io/MULTI-Benchmark/) | 📃 [Paper](https://arxiv.org/abs/2402.03173/) | 🤗 [Dataset](https://huggingface.co/datasets/OpenDFM/MULTI-Benchmark) | 🎯 [Leaderboard]() (Coming Soon)
+🌐 [Website](https://OpenDFM.github.io/MULTI-Benchmark/) | 📃 [Paper](https://arxiv.org/abs/2402.03173/) | 🤗 [Dataset](https://huggingface.co/datasets/OpenDFM/MULTI-Benchmark) | 📮 [Submit](https://opendfm.github.io/MULTI-Benchmark/static/pages/submit.html)
 
 [简体中文](./README_zh.md) | English
 
@@ -20,11 +20,11 @@ viewer: False
 
 ## 🔥 News
 
-- **[Cooming Soon]** We will release the official evaluation platform.
-- **[2024.2.19]** We release the [HuggingFace Page](https://huggingface.co/datasets/OpenDFM/MULTI-Benchmark/).
-- **[2024.2.6]** We publish our [paper](https://arxiv.org/abs/2402.03173/) on arXiv.
-- **[2023.12.7]** We release the [code](https://github.com/OpenDFM/MULTI-Benchmark/tree/main/eval) of our benchmark evaluation.
-- **[2023.12.5]** We release the [GitHub Page](https://OpenDFM.github.io/MULTI-Benchmark/).
+- **[2024.3.4]** We have released the [evaluation page](https://OpenDFM.github.io/MULTI-Benchmark/static/pages/submit.html).
+- **[2024.2.19]** We have released the [HuggingFace Page](https://huggingface.co/datasets/OpenDFM/MULTI-Benchmark/).
+- **[2024.2.6]** We have published our [paper](https://arxiv.org/abs/2402.03173/) on arXiv.
+- **[2023.12.7]** We have released the [code](https://github.com/OpenDFM/MULTI-Benchmark/tree/main/eval) of our benchmark evaluation.
+- **[2023.12.5]** We have released the [GitHub Page](https://OpenDFM.github.io/MULTI-Benchmark/).
 
 ## 📖 Overview
 
@@ -48,8 +48,6 @@ Rapid progress in multimodal large language models (MLLMs) highlights the need t
 |    🖼️    |   VisualGLM   | visualglm-6b               |  31.1   |    12.8     |
 |    🖼️    | Chinese-LLaVA | Chinese-LLaVA-Cllama2      |  28.5   |    12.3     |
 
-For more details, please visit our [leaderboard]() (Coming Soon).
-
 ## ⏬ Download
 
 You can simply download data using the following command:
@@ -70,7 +68,6 @@ The structure of `./data` should be something like:
 └── captions_v1.2.0_20231217.csv                 # image captions generated by BLIP-6.7b
 ```
 
-
 ## 📝 How to Evaluate
 
 We provide a unified evaluation framework in `eval`. Each file in `eval/models` contains an evaluator specified to one M/LLM, and implements a `generate_answer` method to receive a question as input and give out the answer of it.
@@ -185,13 +182,13 @@ You need to first prepare a UTF-8 encoded JSON file with the following format:
 }
 ```
 
-If you evaluate the model with our official code, you can simply zip the experiment result folder `results/EXPERIMENT_NAME`.
+If you evaluate the model with our official code, you can simply zip the prediction file `prediction.json` and the configuration file `args.json` in the experiment results folder `. /results/EXPERIMENT_NAME` in `.zip` format.
 
-Then, you can submit your result to our [evaluation platform](https://wj.sjtu.edu.cn/q/89UmRAJn) (Coming Soon).
+Then, you can submit your result to our [evaluation page](https://opendfm.github.io/MULTI-Benchmark/static/pages/submit.html).
 
-You are also welcome to pull a request and contribute your code to our evaluation code. We will be very grateful for your contribution!
+You are also welcomed to pull a request and contribute your code to our evaluation code. We will be very grateful for your contribution!
 
-**[Notice]** Thank you for being so interested in the **MULTI** dataset! As the automated evaluation platform is not yet online, please fill in [this questionnaire](https://wj.sjtu.edu.cn/q/89UmRAJn) to get the evaluation results, your information will be kept strictly confidential, so please feel free to fill it out. 🤗
+**[Notice]** Thank you for being so interested in the **MULTI** dataset! If you want to add your model in our leaderboard, please fill in [this questionnaire](https://wj.sjtu.edu.cn/q/89UmRAJn), your information will be kept strictly confidential, so please feel free to fill it out. 🤗
 
 
 ## 📑 Citation

README_zh.md

@@ -1,18 +1,10 @@
----
-license: mit
-language:
-- zh
-pretty_name: MULTI-Benchmark
-viewer: False
----
-
 # 🖼️ MULTI-Benchmark: Multimodal Understanding Leaderboard with Text and Images
 
 <div align="center">
 
-![MULTI](./overview.png)
+![MULTI](./docs/static/images/overview.png)
 
-🌐 [网站](https://OpenDFM.github.io/MULTI-Benchmark/) | 📃 [论文](https://arxiv.org/abs/2402.03173/) | 🤗 [数据](https://huggingface.co/datasets/OpenDFM/MULTI-Benchmark) | 🎯 [榜单]() (即将上线) 
+🌐 [网站](https://OpenDFM.github.io/MULTI-Benchmark/) | 📃 [论文](https://arxiv.org/abs/2402.03173/) | 🤗 [数据](https://huggingface.co/datasets/OpenDFM/MULTI-Benchmark) | 📮 [提交](https://opendfm.github.io/MULTI-Benchmark/static/pages/submit.html)
 
 简体中文 | [English](./README.md) 
 
@@ -20,7 +12,7 @@ viewer: False
 
 ## 🔥 新闻
 
-- **[即将上线]** 我们将发布官方评测平台。
+- **[2024.3.4]** 我们发布了[评测页面](https://opendfm.github.io/MULTI-Benchmark/static/pages/submit.html)。
 - **[2024.2.19]** 我们发布了[HuggingFace页面](https://huggingface.co/datasets/OpenDFM/MULTI-Benchmark/)。
 - **[2024.2.6]** 我们在arXiv上发布了我们的[论文](https://arxiv.org/abs/2402.03173/)。
 - **[2023.12.7]** 我们发布了我们的基准评测[代码](https://github.com/OpenDFM/MULTI-Benchmark/tree/main/eval)。
@@ -48,8 +40,6 @@ viewer: False
 |  🖼️  |   VisualGLM   | visualglm-6b               | 31.1 |    12.8     |
 |  🖼️  | Chinese-LLaVA | Chinese-LLaVA-Cllama2      | 28.5 |    12.3     |
 
-更多详情，请访问我们的[排行榜]()（即将推出）。
-
 ## ⏬ 下载
 
 您只需使用以下命令即可下载数据：
@@ -183,13 +173,13 @@ python model_tester.py <args> # args 类似于上面的默认设置
     ...
 }
 ```
-如果您使用我们的官方代码评测模型，可以直接压缩实验结果文件夹`./results/EXPERIMENT_NAME`。
+如果您使用我们的官方代码评测模型，可以直接压缩实验结果文件夹`./results/EXPERIMENT_NAME`中的预测文件`prediction.json`和配置文件`args.json`为`.zip`格式。
 
-然后，您可以将你的结果提交到我们的[评测平台]()（即将推出）。
+然后，您可以将你的结果提交到我们的[评测页面](https://opendfm.github.io/MULTI-Benchmark/static/pages/submit.html)。
 
 欢迎拉取请求（Pull Request）并贡献您的代码到我们的评测代码中。我们感激不尽！
 
-**[提示]** 感谢您对 MULTI 数据集的关注！由于自动评测平台尚未上线，请填写[此问卷](https://wj.sjtu.edu.cn/q/89UmRAJn)以获取评测结果，您的个人信息将被严格保密，请放心填写。🤗
+**[提示]** 感谢您对 MULTI 数据集的关注！如果您希望将您的模型结果添加至榜单，请填写[此问卷](https://wj.sjtu.edu.cn/q/89UmRAJn)，您的个人信息将被严格保密，请放心填写。🤗
 
 ## 📑 引用
 
@@ -208,4 +198,4 @@ python model_tester.py <args> # args 类似于上面的默认设置
 
 ## 📧 联系我们
 
-如果你有任何问题，请随时通过电子邮件联系我们 `JamesZhutheThird@sjtu.edu.cn` 和 `xuyang0112@sjtu.edu.cn`
+如果您有任何问题，请随时通过电子邮件与我们联系： `JamesZhutheThird@sjtu.edu.cn` 和 `xuyang0112@sjtu.edu.cn`