Update README.md

README.md

@@ -10,7 +10,7 @@ tags:
 - CLIP
 ---
 
-# Dataset Introduction
+# ⭐ Dataset Introduction
 
 The standard datasets (except ImageNet) used for CLIP-based Prompt Tuning research (e.g., [CoOp](https://github.com/KaiyangZhou/CoOp)).
 
@@ -20,19 +20,21 @@ Based on the original datasets, this repository adds **foreground segmentation m
 	<img src="_mask_examples.png" alt="fail" width="50%"">
 </div>     
 
+- 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.
+
 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
+# 🏷️ Scope of Application
 
 Datasets are suitable for training and improving **foreground-supervised prompt tuning** methods. For example:
 
-- _Decouple before Align: Visual Disentanglement Enhances Prompt Tuning_
-
 - _FVG-PT: Adaptive Foreground View-Guided Prompt Tuning for Vision-Language Models_
 
 Also, they are **fully compatible** with other original prompt tuning approaches.
 
-# Data Preparation
+# ⚙ Data Preparation
 
 The datasets include the original images, the `split_zhou_xxx.json` annotations, and **foreground masks**.
 
@@ -41,28 +43,33 @@ The `mask` directory is located under the dataset root, and its internal subpath
 - 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)
-```
+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://github.com/soumith/imagenetloader.torch/blob/master/valprep.sh)]
+
+**_NOTE: You can build the file tree by referring to the [[FVG-PT repository](https://github.com/JREion/FVG-PT/blob/main/docs/DATASETS.md)]._**
 
-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.
+# 🖥︎ When You Write Your Own Code ...
 
-During pre-processing, the mask input needs to be resized, e.g.:
+1. You can refer to `./Dassl.pytorch` in the [FVG-PT repository](https://github.com/JREion/FVG-PT/tree/main) to build a DataLoader that can pass masks.
 
-```Python
-def _transform_pair(self, img, mask):
-        if mask.size != img.size:
-            mask = TF.resize(
-                mask,
-                [img.height, img.width],
-                interpolation=InterpolationMode.NEAREST
-            )
-```
+    ```Python
+    mask_path = join(dataset_root, "mask", image_path_suffix)
+    ```
+
+2. 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://github.com/soumith/imagenetloader.torch/blob/master/valprep.sh)]
 
 
 
 # Acknowledgements
 
-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).
+Our repository is built based on [DPC](arxiv.org/abs/2503.13443), [FVG-PT](https://github.com/JREion/FVG-PT), [DAPT](https://github.com/SII-Ferenas/DAPT) and [zhengli97](https://huggingface.co/zhengli97/prompt_learning_dataset).