Update constants.py

constants.py

@@ -4,36 +4,33 @@ TASK_INFO = [ 'Resolution', 'FPS', 'Open Source', 'Length', 'Speed', 'Motion', '
 TASK_INFO_v2 = ['Final Sum Score', 'Motion Quality', 'Text-Video Alignment', 'Visual Quality', 'Temporal Consistency', 'Resolution', 'FPS', 'Open Source', 'Length', 'Speed', 'Motion', 'Camera']
 
 AVG_INFO = ['Final Sum Score', 'Motion Quality', 'Text-Video Alignment', 'Visual Quality', 'Temporal Consistency']
-DATA_TITILE_TYPE = ["markdown", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]
+DATA_TITILE_TYPE = ["markdown", "markdown", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]
 CSV_DIR = "./file/result.csv"
 
-# COLUMN_NAMES = MODEL_INFO + TASK_INFO
 COLUMN_NAMES = MODEL_INFO + TASK_INFO_v2
 
-DATA_NUM = [3158, 1831, 4649, 978, 2447, 657, 97, 331, 85, 1740, 2077, 1192]
-
-
 LEADERBORAD_INTRODUCTION = """# EvalCrafter Leaderboard 🏆
 
     Welcome to the cutting-edge leaderboard for text-to-video generation, where we meticulously evaluate state-of-the-art generative models using our comprehensive framework, ensuring high-quality results that align with user opinions. Join us in this exciting journey towards excellence! 🛫  
 
-    More methods will be evalcrafted soon, stay tunned ❤️ Join our evaluation by sending an email 📧 (vinthony@gmail.com)! You may also read the [EvalCrafter paper](https://arxiv.org/abs/2310.11440) for more detailed information 🤗
+    More methods will be evalcrafted soon, stay tunned ❤️ Join our evaluation by sending an email 📧 (vinthony@gmail.com)! You may also read the [Code](https://github.com/EvalCrafter/EvalCrafter), [Paper](https://arxiv.org/abs/2310.11440), and [Project page](https://evalcrafter.github.io/)  for more detailed information 🤗
     """
 
+
 TABLE_INTRODUCTION = """In the table below, we summarize each dimension performance of all the models.    """
 
 LEADERBORAD_INFO = """
-      The vision and language generative models have been overgrown in recent years. For video generation, 
-      various open-sourced models and public-available services are released for generating high-visual quality videos. 
-      However, these methods often use a few academic metrics, \eg, FVD or IS, to evaluate the performance. We argue that
-      it is hard to judge the large conditional generative models from the simple metrics since these models are often trained
-      on very large datasets with multi-aspect abilities. Thus, we propose a new framework and pipeline to exhaustively evaluate
+      The vision and language generative models have been overgrown in recent years. For video generation, various
+      open-sourced models and public-available services are released for generating high-visual quality videos. However, 
+      these methods often use a few academic metrics, \eg, FVD or IS, to evaluate the performance. We argue that it is 
+      hard to judge the large conditional generative models from the simple metrics since these models are often trained on
+      very large datasets with multi-aspect abilities. Thus, we propose a new framework and pipeline to exhaustively evaluate
       the performance of the generated videos. To achieve this, we first conduct a new prompt list for text-to-video generation 
       by analyzing the real-world prompt list with the help of the large language model. Then, we evaluate the state-of-the-art video 
       generative models on our carefully designed benchmarks, in terms of visual qualities, content qualities, motion qualities, and 
-      text-caption alignment with around 18 objective metrics. To obtain the final leaderboard of the models, we also fit a series of
-      coefficients to align the objective metrics to the users' opinions. Based on the proposed opinion alignment method, our final score
-      shows a higher correlation than simply averaging the metrics, showing the effectiveness of the proposed evaluation method.
+      text-caption alignment with around 17 objective metrics. To obtain the final leaderboard of the models, we also fit a series of
+      coefficients to align the objective metrics to the users' opinions. Based on the proposed opinion alignment method, our final 
+      score shows a higher correlation than simply averaging the metrics, showing the effectiveness of the proposed evaluation method.
 """