NYUAD AI-generated Images Detector
| Property | Value |
|---|---|
| Parameter Count | 85.8M |
| Model Type | Vision Transformer (ViT) |
| License | Apache 2.0 |
| Accuracy | 97.36% |
| Latest Loss | 0.0987 |
What is NYUAD_AI-generated_images_detector?
NYUAD_AI-generated_images_detector is a state-of-the-art image classification model designed to detect AI-generated images. Developed by the NYUAD-ComNets team, this model leverages Vision Transformer architecture to achieve exceptional accuracy in distinguishing between real and AI-generated images.
Implementation Details
The model is implemented using the Transformers library and utilizes a Vision Transformer (ViT) architecture. It has been trained through a careful process, achieving progressively better results over multiple epochs, with the final validation accuracy reaching 97.36%.
- Built using the Hugging Face Transformers framework
- Utilizes F32 tensor type for computations
- Implements TensorBoard for training visualization
- Uses Safetensors for model weight storage
Core Capabilities
- High-accuracy image classification (97.36%)
- Easy integration with Python applications
- Efficient processing of image data
- Robust performance with low loss (0.0987)
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its exceptional accuracy in detecting AI-generated images, achieving 97.36% accuracy with a very low loss rate of 0.0987. It utilizes modern Vision Transformer architecture and has been extensively trained through multiple epochs.
Q: What are the recommended use cases?
This model is ideal for content moderation systems, digital forensics, and verification of image authenticity in various applications where distinguishing between real and AI-generated images is crucial.
