Update README.md

README.md

@@ -12,7 +12,7 @@ size: 10,000 instances
 
 **W-Bench is the first benchmark to evaluate watermarking robustness across four types of image editing techniques, including regeneration, global editing, local editing, and image-to-video generation.** 
 
-Eleven representative watermarking methods are evaluated on the W-Bench. The W-Bench contains 10,000 samples sourced from datasets such as COCO, Flickr, ShareGPT4V, etc. 
+Eleven representative watermarking methods are evaluated on the W-Bench. The W-Bench contains 10,000 instances sourced from datasets such as COCO, Flickr, ShareGPT4V, etc. 
 
 GitHub Repo: [https://github.com/Shilin-LU/VINE](https://github.com/Shilin-LU/VINE)
 
@@ -24,7 +24,7 @@ The evaluation set consists of six subsets, each targeting a different type of A
 - 1,000 samples for global editing
 - 5,000 samples for local editing (divided into five sets, each containing 1,000 images and 1,000 masks, with different mask sizes ranging from 10–60% of the image area)
 - 1,000 samples for image-to-video generation
-- 1,000 samples for testing conventional distortion
+- 1,000 samples for testing conventional distortion (identical to the 1,000 samples used for deterministic regeneration)
 
 # How to download and use 🍷 W-Bench