Update README.md

README.md

@@ -3,4 +3,44 @@ license: mit
 language:
 - en
 pretty_name: W-Bench
----
+size: 10,000 instances
+---
+
+# What is it?
+W-Bench is the first holistic benchmark that incorporates four types of image editing techniques to assess the robustness of watermarking methods. Eleven representative watermarking methods are evaluated on the W-Bench. The W-Bench contains 10,000 images sourced from datasets such as COCO, Flickr, ShareGPT4V, etc.
+
+# Dataset Structure
+
+The evaluation set is divided into 6 different categories:
+- 1,000 samples for stochastic regeneration
+- 1,000 samples for deterministic regeneration
+- 1,000 samples for global editing
+- 5,000 samples for local editing (divided into five sets, each containing 1,000 images, 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
+
+# How to download and use 🍷 W-Bench
+
+## Using `huggingface_hub`
+
+```python
+from huggingface_hub import snapshot_download
+folder = snapshot_download(
+"Shilin-LU/W-Bench",
+repo_type="dataset",
+local_dir="./W-Bench/",
+allow_patterns="DET_INVERSION_1K/image/*")
+```
+
+For faster downloads, make sure to install `pip install huggingface_hub[hf_transfer]` and set the environment variable `HF_HUB_ENABLE_HF_TRANSFER=1`.
+
+## Using `datasets`
+
+```python
+from datasets import load_dataset
+wbench = load_dataset("Shilin-LU/W-Bench", streaming=True)
+```
+
+# Citation Information
+Paper on [arXiv](https://arxiv.org/abs/2410.18775)
+