Document the VDiff-Bench release

README.md

@@ -33,3 +33,49 @@ configs:
   - split: train
     path: data/train-*
 ---
+
+# VDiff-Bench
+
+VDiff-Bench is a multiple-choice benchmark for fine-grained visual difference identification. Each example presents two similar images and four candidate descriptions, exactly one of which states a real difference between the images.
+
+## Dataset structure
+
+The `train` split contains 1,756 questions with the following fields:
+
+- `id`: stable example identifier.
+- `image_1`, `image_2`: the paired images.
+- `choices`: an object containing answer choices `A`, `B`, `C`, and `D`.
+- `ground_truth`: label of the correct choice.
+- `category`: visual change category.
+
+The public release intentionally excludes source identifiers, intermediate difference annotations, generated-negative metadata, and other dataset-construction fields.
+
+## Change categories
+
+| Category | Questions |
+|---|---:|
+| Change of position | 158 |
+| Change of motion | 138 |
+| Change of color (regional) | 170 |
+| Change of color (whole image) | 147 |
+| Appear / disappear | 201 |
+| Change of noise / resolution | 150 |
+| Change of texture | 177 |
+| Substitution and size | 199 |
+| OCR | 221 |
+| Illumination | 195 |
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("elaine1wan/image_diff_data", split="train")
+example = dataset[0]
+```
+
+`image_1` and `image_2` are decoded as PIL images by default. Benchmark performance is measured using exact-match accuracy on `ground_truth`.
+
+## Citation
+
+Citation information will be added with the accompanying paper release.