---
language: en
license: mit
tags:
  - image-classification
  - densenet
  - ai-generated-content
  - human-created-content
  - model-card
---

# **Fine-tuned DenseNet for Image Classification**

## **Model Overview**
This fine-tuned **DenseNet121** model is designed to classify images into the following categories:
1. **DALL-E Generated Images**
2. **Human-Created Images**
3. **Other AI-Generated Images**

The model is ideal for detecting AI-generated content, particularly useful in creative fields such as art and design.

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## **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.

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## **Model Performance**
- **Accuracy**: **95%** on the validation dataset.
- **Loss**: **0.0552** after 15 epochs of training.

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## **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.

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## **Training Metrics**
### **1. Loss Over Epochs**
![Loss Over Epochs](https://huggingface.co/alokpandey/DenseNet-DH3Classifier/resolve/main/Screenshot%202024-10-22%20003510.png)  
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](https://huggingface.co/alokpandey/DenseNet-DH3Classifier/resolve/main/Screenshot%202024-10-22%20003525.png)  
This graph shows the increase in accuracy, reflecting the model's growing ability to correctly classify images.

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## **Sample Dataset**
Here is a visual representation of the dataset used for training and validation:

![Sample Dataset](https://huggingface.co/alokpandey/DenseNet-DH3Classifier/resolve/main/collage5.png)

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:
1. **Human-Created Images** – Traditional artwork or photographs made by humans.
2. **DALL-E Generated Images** – Images created using DALL-E, an advanced AI model designed to generate visual content.
3. **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.

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## **Model Output Samples**
Here are some examples of the model's predictions on various images:

#### Sample 1: Human-Created Image
![Sample 1](HG_22.jpg)  
*Predicted: Human-Created*

#### Sample 2: DALL-E Generated Image
![Sample 2](DallE_4.jpg)  
*Predicted: DALL-E Generated*

#### Sample 3: Other AI-Generated Image
![Sample 3](OtherAI_5.jpg)  
*Predicted: Other AI-Generated*

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## **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).

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## **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.

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## **Model Download**
You can download the fine-tuned DenseNet121 model using the following link:  
[**Download the Model**](https://huggingface.co/alokpandey/DenseNet-DH3Classifier/resolve/main/densenet_finetuned_dense.pth)

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## **References**
For more information on DenseNet, refer to the original research paper:  
[**Densely Connected Convolutional Networks (DenseNet)**](https://arxiv.org/abs/1608.06993)