This fine-tuned DenseNet121 model is designed to classify images into the following categories:
DALL-E Generated Images
Human-Created Images
Other AI-Generated Images
The model is ideal for detecting AI-generated content, particularly useful in creative fields such as art and design.
Use Cases
AI Art Detection: Identifies whether an image was generated by AI or created by a human.
Content Moderation: Useful in media, art, and design industries where distinguishing AI-generated content is essential.
Educational Purposes: Useful for exploring the differences between AI and human-generated content.
Model Performance
Accuracy: 95% on the validation dataset.
Loss: 0.0552 after 15 epochs of training.
Training Details
Base Model: DenseNet121, pretrained on ImageNet.
Optimizer: Adam with a learning rate of 0.0001.
Loss Function: Cross-Entropy Loss.
Batch Size: 32
Epochs: 15
The model was fine-tuned using data augmentation techniques like random flips, rotations, and color jittering to improve robustness.
Training Metrics
1. Loss Over Epochs
Loss Over Epochs
This graph shows the decrease in loss over 15 epochs, indicating the model's improved ability to fit the data.
2. Accuracy Over Epochs
Accuracy Over Epochs
This graph shows the increase in accuracy, reflecting the model's growing ability to correctly classify images.
Sample Dataset
Here is a visual representation of the dataset used for training and validation:
Sample Dataset
This image shows a collage of examples from the dataset used to fine-tune the DenseNet model. The dataset includes a diverse mix of images from three distinct categories:
Human-Created Images – Traditional artwork or photographs made by humans.
DALL-E Generated Images – Images created using DALL-E, an advanced AI model designed to generate visual content.
Other AI-Generated Images – Visual content generated by other AI systems, aside from DALL-E, to provide variety in the training data.
This diversity allows the model to effectively learn how to distinguish between different forms of image creation, ensuring robust performance across a range of AI-generated and human-created content.
Model Output Samples
Here are some examples of the model's predictions on various images:
Sample 1: Human-Created Image
Sample 1
Predicted: Human-Created
Sample 2: DALL-E Generated Image
Sample 2
Predicted: DALL-E Generated
Sample 3: Other AI-Generated Image
Sample 3
Predicted: Other AI-Generated
Model Architecture
Feature Extractor: DenseNet121 with frozen layers to retain general features from ImageNet.
Classifier: A fully connected layer with 3 output nodes, one for each class (DALL-E, Human-Created, Other AI).
Limitations
Data Bias: The model's performance is dependent on the balance and diversity of the training dataset.
Generalization: Further testing on more diverse datasets is recommended to validate the model’s performance across different domains and types of images.
Model Download
You can download the fine-tuned DenseNet121 model using the following link: Download the Model
