Update README.md

README.md

@@ -16,6 +16,30 @@ Our dataset is built upon the Uground, Jedi, and additional public paper-style a
 This dataset is set with the image processor max tokens to be 2700, a.k.a max_pixels=2700x14x14x2x2 , the coordinates were resized to be smaller and you have to resize the image as well within max_pixels=2700x14x14x2x2 via image processor to make them align.
 Make sure you also follow it in your training procedure, otherwise the performance will not be as expected.
 
+
+# Note
+
+If you'd like to check the annotated coordinates on the screenshots, please refer to `images_resized.zip`. In this zip file, all images are preprocessed by the same pre-procession pipeline from [Qwen2.5-VL](https://arxiv.org/abs/2502.13923)
+  
+```python
+from transformers.models.qwen2_vl.image_processing_qwen2_vl_fast import (
+    smart_resize as qwen_smart_resize,
+)
+import torchvision.transforms.functional as tvF
+
+resized_height, resized_width = qwen_smart_resize(
+    image.height,
+    image.width,
+    max_pixels=2116800,  # 2700 * 14 * 14 * 2 * 2 / 2 (adjusted)
+    min_pixels=12544,
+)
+resized_image = tvF.resize(
+    image_tensor,
+    [resized_height, resized_width],
+    interpolation=tvF.InterpolationMode.BILINEAR,
+    antialias=True,
+)
+
 ## Citation
 
 If you find our data, model, benchmark or the general resources useful, please consider citing: