Update README.md

README.md

@@ -21,3 +21,122 @@ tags:
   />
 </p>
 
+---
+
+# XenArcAI/AIRealNet
+
+**Model type:** Image Classification (Binary)
+**Task:** AI-generated vs Human image detection
+**Base model:** `Microsoft/swinv2-tiny-patch4-window16-256`
+**Fine-tuned by:** `Parveshiiii/AI-vs-Real` dataset split
+
+---
+
+## Overview
+
+In an era of rapidly advancing AI-generated imagery, deepfakes, and synthetic media, the need for reliable detection tools has never been higher. **XenArcAI/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.
+
+* **Class 0:** AI-generated image
+* **Class 1:** Real human image
+
+By leveraging the robust **SwinV2 Tiny** architecture as its backbone, AIRealNet achieves a high degree of accuracy while remaining lightweight enough for practical deployment.
+
+---
+
+## 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.
+
+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%
+
+4. **Ethical Design:**
+   No personal photos were included, even if edited or AI-modified, respecting privacy and ethical AI principles.
+
+5. **Fast and Scalable:**
+   Based on a transformer vision model, AIRealNet can be deployed efficiently in both research and production environments.
+
+---
+
+## Training Data
+
+* **Dataset:** `Parveshiiii/AI-vs-Real` (open-sourced subset of main dataset )
+* **Size:** 14k 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.
+
+---
+
+## Limitations
+
+While AIRealNet performs exceptionally well on typical AI-generated images, users should note:
+
+1. **Subtle Edits:** The model struggles with nano-scale edits or ultra-precise modifications, like “nano banana” edits.
+2. **Edited Personal Images(over precise):** Images of real people that have been AI-modified are **not detected**, aligning with privacy and ethical guidelines.
+3. **Domain Generalization:** Performance may vary on images from completely unseen AI generators or extremely unconventional content.
+
+---
+
+## Performance Metrics
+
+> Metrics shown are from **Epoch 2**, chosen to illustrate stable performance after fine-tuning.
+
+<p align="center">
+  <img 
+    src="https://cdn-uploads.huggingface.co/production/uploads/677fcdf29b9a9863eba3f29f/3NVa0KLX0iAxTP2e6IlGH.png" 
+    alt="AIRealNet Banner" 
+    width="90%" 
+    style="border-radius:15px;"
+  />
+</p>
+
+**Note:** Extremely low loss and high accuracy are due to the controlled dataset environment. Real-world performance may be lower depending on the image domain.(In our testing this is model is over accurate despite it can't detect Nano-Banana images(only edited fully generated images can be detected over accurately))
+
+---
+
+## Intended Use
+
+* Detect AI-generated imagery on social media, research publications, and digital media platforms.
+* Assist content moderators, researchers, and fact-checkers in identifying synthetic media.
+* **Not intended** for legal verification without human corroboration.
+
+---
+
+## Ethical Considerations
+
+* **Privacy-first Approach:** Personal photos, even if AI-edited, were excluded.
+* **Responsible Deployment:** Users should combine model predictions with human review to avoid false positives or negatives.
+* **Transparency:** The model card openly communicates its limitations and dataset design to prevent misuse.
+
+---
+
+## How It Works
+
+1. Images are preprocessed and resized to `256x256`.
+2. Features are extracted using the **SwinV2 Tiny** vision transformer backbone.
+3. A binary classification head outputs probabilities for AI-generated vs real human images.
+4. Predictions are interpreted as class 0 (AI) or class 1 (Human).
+
+---
+
+## Future Work
+
+Future iterations aim to:
+
+* Improve detection of subtle AI-generated edits and “nano banana” modifications.
+* Expand training data with diverse AI generators to enhance generalization.
+* Explore multi-modal detection capabilities (e.g., video, metadata, and image combined).
+
+---
+
+## References
+
+* Microsoft SwinV2 Tiny: [https://github.com/microsoft/Swin-Transformer](https://github.com/microsoft/Swin-Transformer)
+* Parveshiiii/AI-vs-Real dataset (subset): Open-sourced by our team member
+
+---
+