Model card added

README.md

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+---
+license: mit
+base_model:
+- Modotte/AIRealNet
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- safety
+- Modotte
+- SoTA
+---
+# Modotte
+
+<p align="center">
+  <img 
+    src="https://cdn-uploads.huggingface.co/production/uploads/677fcdf29b9a9863eba3f29f/m7ddTjuxxLUntdXVk0t5N.png" 
+    alt="AIRealNet Banner" 
+    width="90%" 
+    style="border-radius:15px;"
+  />
+</p>
+
+---
+
+> 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.
+
+* **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, chosen to illustrate stable performance after fine-tuning.
+
+<p align="center">
+  <img 
+    src="https://cdn-uploads.huggingface.co/production/uploads/677fcdf29b9a9863eba3f29f/Z7rfOLgZuxDn96ss25bRL.png" 
+    alt="AIRealNet Banner" 
+    width="90%" 
+    style="border-radius:15px;"
+  />
+</p>
+
+---
+
+## Demo and Usage
+
+1. **Installing dependencies**
+
+```python
+pip install -U transformers
+```
+2. **Loading and running a demo**
+
+```python
+from transformers import pipeline
+
+pipe = pipeline("image-classification", model="Modotte/AIRealNet")
+pipe("https://cdn-uploads.huggingface.co/production/uploads/677fcdf29b9a9863eba3f29f/rYzxmRIFgqg2_EfEFRi89.jpeg")# example image
+```
+# Demo
+
+* **Given Image**: This image is actually real, but the older model would classify it as fake because it was not trained on varied social media platform compressions.
+
+<p align="center">
+  <img 
+    src="https://cdn-uploads.huggingface.co/production/uploads/677fcdf29b9a9863eba3f29f/rYzxmRIFgqg2_EfEFRi89.jpeg" 
+    alt="AIRealNet Banner" 
+    width="90%" 
+    style="border-radius:15px;"
+  />
+</p>
+
+* **New Model Output**
+
+```bash
+[{'label': 'real', 'score': 0.9999173879623413},
+ {'label': 'artificial', 'score': 8.256531145889312e-05}]
+```
+
+
+---
+
+## 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).
+
+---
+
+### Citation
+```bibtex
+@misc{Modotte_AIRealNet_2025,
+    title={AIRealNet: A Fine-Tuned Vision Transformer for Detecting AI-Generated vs Real Human Images},
+    author={Parvesh Rawal},
+    publisher={Hugging Face},
+    year={2025},
+    url={https://huggingface.co/Modotte/AIRealNet}
+}
+```
+
+## 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
+
+---