Update the olf info

README.md

@@ -22,11 +22,11 @@ tags:
 
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-> This is an upgraded version of AIRealNet. We have improved its performance on social media images of different qualities, making it a great upgrade from its previous version.
+> This is an upgraded version of AIRealNet. We have improved its performance on social media images of different qualities, making it a great upgrade from its previous version. 
 
 ## Overview
 
-In an era of rapidly advancing AI-generated imagery, deepfakes, and synthetic media, the need for reliable detection tools has never been higher. **AIRealNet** is a binary image classifier explicitly designed to distinguish **AI-generated images** from **real human photographs**. This model is optimized to detect conventional AI-generated content while adhering to strict privacy standards—avoiding personal or sensitive images.
+In an era of rapidly advancing AI-generated imagery, deepfakes, and synthetic media, the need for reliable detection tools has never been higher. **AIRealNet-2** is a binary image classifier explicitly designed to distinguish **AI-generated images** from **real human photographs**. This model is optimized to detect conventional AI-generated content while adhering to strict privacy standards—avoiding personal or sensitive images.
 
 * **Class 0:** AI-generated image
 * **Class 1:** Real human image
@@ -37,14 +37,13 @@ By leveraging the robust **SwinV2 Tiny** architecture as its backbone, AIRealNet
 
 ## Key Features
 
-1. **High Accuracy on Public Datasets:**
-   Despite using a **14k-image fine-tuning split(Part of main fine tuning split)**, AIRealNet demonstrates exceptional accuracy and robustness in detecting AI-generated images.
-
+1. **High Accuracy on Public Datasets:** Model is trained on data from social media 
+   
 2. **Balanced Training Split:**
    The dataset contains a balanced number of AI-generated and real images, ensuring unbiased training and minimizing class imbalance issues.
 
-   * **AI-Generated:** 60%
-   * **Human-Images:** 40%
+   * **AI-Generated:** 50%
+   * **Human-Images:** 50%
 
 4. **Ethical Design:**
    No personal photos were included, even if edited or AI-modified, respecting privacy and ethical AI principles.
@@ -56,8 +55,8 @@ By leveraging the robust **SwinV2 Tiny** architecture as its backbone, AIRealNet
 
 ## Training Data
 
-* **Dataset:** `Parveshiiii/AI-vs-Real` (open-sourced subset of main dataset )
-* **Size:** 14k images (balanced between AI and human)
+* **Dataset:** Collection of images from differnt plaforms like instagram, x.
+* **Size:** 98k images (balanced between AI and human)
 * **Split:** Used the train split for fine-tuning; validation performed on a separate balanced subset.
 * **Notes:** Images sourced from public datasets and AI generation tools. Edited personal photos were intentionally excluded.
 
@@ -164,7 +163,7 @@ Future iterations aim to:
 ### Citation
 ```bibtex
 @misc{Modotte_AIRealNet_2025,
-    title={AIRealNet: A Fine-Tuned Vision Transformer for Detecting AI-Generated vs Real Human Images},
+    title={AIRealNet-2: A Fine-Tuned Vision Transformer for Detecting AI-Generated vs Real Human Images},
     author={Parvesh Rawal},
     publisher={Hugging Face},
     year={2025},