first commit

src/md.py

@@ -7,6 +7,7 @@ ABOUT_TEXT = """
 The prompt-video pairs are sourced from [VideoGen-Eval](https://ailab-cvc.github.io/VideoGen-Eval/), a dataset featuring a diverse range of prompts and videos generated by state-of-the-art video diffusion models (VDMs). Our benchmark comprises 26.5k video pairs, each annotated with a corresponding preference label.
 <img src="https://i.postimg.cc/J7XhVLTh/image.png" alt="Video Duration and Resolution in VideoGen-RewardBench" style="width: 400px;"/>
 
+
 We report two accuracy metrics: ties-included accuracy **(w/ Ties)** and ties-excluded accuracy **(w/o Ties)**. 
 
 -  For ties-excluded accuracy, we exclude all data labeled as ”ties” and use only data labeled as ”A wins” or ”B wins” for calculation. We compute the rewards for each prompt-video pair, convert the relative reward relationships into binary labels, and calculate classification accuracy.