Update readme

README.md

@@ -2,3 +2,73 @@
 license: apache-2.0
 ---
 💫 Official implementation of Charm: The Missing Piece in ViT fine-tuning for Image Aesthetic Assessment 
+
+> [**Accepted at CVPR 2025**](https://cvpr.thecvf.com/virtual/2025/poster/34423)<br>
+
+<div align="left">
+<a href="https://github.com/FBehrad/Charm">
+    <img src="https://github.com/FBehrad/Charm/blob/main/MainFigure_new.jpg?raw=true" alt="Overall framework" width="400"/>
+</a>
+</div>
+
+We introduce **Charm** , a novel tokenization approach that preserves **C**omposition, **H**igh-resolution,
+**A**spect **R**atio, and **M**ulti-scale information simultaneously. By preserving critical aesthetic information, <em> Charm </em> achieves significant performance improvement across different image aesthetic and quality assessment datasets.
+
+
+### Quick Inference
+
+* Step 1) Check our [GitHub Page](https://github.com/FBehrad/Charm/) and install the requirements. 
+
+```setup
+pip install -r requirements.txt
+```
+___
+* Step 2) Install Charm tokenizer.
+```setup
+pip install Charm-tokenizer
+```
+___
+* Step 3) Tokenization + Position embedding preparation
+
+<div align="center">
+<a href="https://github.com/FBehrad/Charm">
+    <img src="https://github.com/FBehrad/Charm/blob/main/charm.gif?raw=true" alt="Charm tokenizer" width="700"/>
+</a>
+</div>
+
+```python
+from Charm_tokenizer.ImageProcessor import Charm_Tokenizer
+
+img_path = r"img.png"
+
+charm_tokenizer = Charm_Tokenizer(patch_selection='frequency', training_dataset='tad66k', without_pad_or_dropping=True)
+tokens, pos_embed, mask_token = charm_tokenizer.preprocess(img_path)
+```
+Charm Tokenizer has the following input args:
+* patch_selection (str): The method for selecting important patches
+  * Options: 'saliency', 'random', 'frequency', 'gradient', 'entropy', 'original'.
+* training_dataset (str): Used to set the number of ViT input tokens to match a specific training dataset from the paper.
+  * Aesthetic assessment datasets: 'aadb', 'tad66k', 'para', 'baid'.
+  * Quality assessment datasets: 'spaq', 'koniq10k'.
+* backbone (str): The ViT backbone model (default: 'facebook/dinov2-small').
+* factor (float): The downscaling factor for less important patches (default: 0.5).
+* scales (int): The number of scales used for multiscale processing (default: 2).
+* random_crop_size (tuple): Used for the 'original' patch selection strategy (default: (224, 224)).
+* downscale_shortest_edge (int): Used for the 'original' patch selection strategy (default: 256).
+* without_pad_or_dropping (bool): Whether to avoid padding or dropping patches (default: True).
+
+The output is the preprocessed tokens, their corresponding positional embeddings, and a mask token that indicates which patches are in high resolution and which are in low resolution.
+___
+
+* Step 4) Predicting aesthetic/quality score
+
+```python
+from Charm_tokenizer.Backbone import backbone
+
+model = backbone(training_dataset='tad66k', device='cpu')
+prediction = model.predict(tokens, pos_embed, mask_token)
+```
+
+**Note:**
+1. While random patch selection during training helps avoid overfitting,for consistent results during inference, fully deterministic patch selection approaches should be used. 
+2. For the training code, check our [GitHub Page](https://github.com/FBehrad/Charm/).