Update README.md

README.md

@@ -16,6 +16,10 @@ The standard datasets (except ImageNet) used for CLIP-based Prompt Tuning resear
 
 Based on the original datasets, this repository adds **foreground segmentation masks** (generated by [SEEM](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once)) of all raw images.
 
+<div align="left">
+	<img src="_mask_examples.png" alt="fail" width="50%"">
+</div>     
+
 Datasets contain: [ImageNet](https://image-net.org/challenges/LSVRC/2012/index.php), [Caltech101](https://data.caltech.edu/records/mzrjq-6wc02), [Oxford Pets](https://www.robots.ox.ac.uk/~vgg/data/pets/), [StanfordCars](https://ai.stanford.edu/~jkrause/cars/car_dataset.html), [Flowers102](https://www.robots.ox.ac.uk/~vgg/data/flowers/102/), [Food101](https://vision.ee.ethz.ch/datasets_extra/food-101/), [FGVC Aircraft](https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/), [SUN397](http://vision.princeton.edu/projects/2010/SUN/), [DTD](https://www.robots.ox.ac.uk/~vgg/data/dtd/), [EuroSAT](https://github.com/phelber/EuroSAT) and [UCF101](https://www.crcv.ucf.edu/data/UCF101.php).
 
 # Scope of Application
@@ -28,6 +32,37 @@ Datasets are suitable for training and improving **foreground-supervised prompt
 
 Also, they are **fully compatible** with other original prompt tuning approaches.
 
+# Data Preparation
+
+The datasets include the original images, the `split_zhou_xxx.json` annotations, and **foreground masks**.
+
+The `mask` directory is located under the dataset root, and its internal subpath is consistent with the image directory, e.g.:
+
+- Image directory: `./flowers-102/oxford_flowers/jpg`
+- Mask directory: `./flowers-102/mask/oxford_flowers/jpg`
+
+```Python
+mask_path = join(dataset_root, "mask", image_path_suffix)
+```
+
+For the foreground masks, the RGB value of the foreground region is `[255, 255, 255]`, and the background region is `[0, 0, 0]`. The shorter side is always fixed to 512 px, and the scaling ratio is the same as that of the corresponding raw image.
+
+During pre-processing, the mask input needs to be resized, e.g.:
+
+```Python
+def _transform_pair(self, img, mask):
+        if mask.size != img.size:
+            mask = TF.resize(
+                mask,
+                [img.height, img.width],
+                interpolation=InterpolationMode.NEAREST
+            )
+```
+
+Additionally, you can prepare ImageNet dataset from: [[Raw Images](https://www.kaggle.com/c/imagenet-object-localization-challenge/overview/description)] [[annoations](https://drive.google.com/file/d/1-61f_ol79pViBFDG_IDlUQSwoLcn2XXF/view?usp=sharing)] [[val conversion script](https://www.kaggle.com/c/imagenet-object-localization-challenge/overview/description)]
+
+
+
 # Acknowledgements
 
-Our repository is built based on [DPC](arxiv.org/abs/2503.13443), [DAPT](arxiv.org/abs/2508.00395) and [zhengli97](https://huggingface.co/zhengli97/prompt_learning_dataset).
+Our repository is built based on [DPC](arxiv.org/abs/2503.13443), [DAPT](https://github.com/SII-Ferenas/DAPT) and [zhengli97](https://huggingface.co/zhengli97/prompt_learning_dataset).