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ideepankarsharma2003
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AI_ImageClassification_MidjourneyV6_SDXL

Image Classification
Transformers
Safetensors
swin
ai-gen-images
Model card Files Community
AI_ImageClassification_MidjourneyV6_SDXL
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---
datasets:
- ideepankarsharma2003/ImageClassificationStableDiffusion_small
- ideepankarsharma2003/Midjourney_v6_Classification_small_shuffled
- ideepankarsharma2003/AIGeneratedImages_Midjourney
tags:
- image-classification
- ai-gen-images
---


# Model Card for AI Image Classification - Midjourney V6 & SDXL  

## Model Details  

### Model Description  

This model is a **Swin Transformer-based classifier** designed to distinguish between **AI-generated** and **human-created** images, specifically focusing on outputs from **Midjourney V6** and **Stable Diffusion XL (SDXL)**. It has been trained on a curated dataset of AI-generated images.  

- **Developed by:** Deepankar Sharma  
- **Model type:** Image Classification (Swin Transformer)  
- **Finetuned from model:** SwinForImageClassification  

### Model Sources  

- **Repository:** [Hugging Face Model Repository](https://huggingface.co/ideepankarsharma2003/AI_ImageClassification_MidjourneyV6_SDXL)  

## Uses  

### Direct Use  

This model can be used for **detecting AI-generated images** from Midjourney V6 and SDXL. It is useful for content moderation, fact-checking, and detecting synthetic media.  

### Out-of-Scope Use  

- The model is **not designed** for detecting AI-generated images from all generative models.  
- It **may not perform well** on heavily edited AI-generated images or images mixed with human elements.  
- It is **not intended for forensic-level deepfake detection**.  

## Bias, Risks, and Limitations  

This model is trained specifically on **Midjourney V6** and **Stable Diffusion XL** datasets. It may not generalize well to images generated by other AI models. Additionally, biases in the dataset could lead to **false positives** (flagging real images as AI-generated) or **false negatives** (failing to detect AI-generated content).  

### Recommendations  

Users should verify results with additional tools and **not solely rely on this model** for high-stakes decisions. Model performance should be tested on domain-specific datasets before deployment.  

## How to Get Started with the Model  

You can use this model with the 🤗 Transformers library:  

```python
from transformers import AutoModelForImageClassification, AutoFeatureExtractor
from PIL import Image
import torch

# Load model and feature extractor
model_name = "ideepankarsharma2003/AI_ImageClassification_MidjourneyV6_SDXL"
model = AutoModelForImageClassification.from_pretrained(model_name)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)

# Load and preprocess image
image = Image.open("path_to_image.jpg")
inputs = feature_extractor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_label = logits.argmax(-1).item()

# Label Mapping
id2label = {0: "ai_gen", 1: "human"}
print("Predicted label:", id2label[predicted_label])
```

## Training Details  

### Training Data  

The model was trained on the following datasets:  

- [ImageClassificationStableDiffusion_small](https://huggingface.co/datasets/ideepankarsharma2003/ImageClassificationStableDiffusion_small)  
- [Midjourney_v6_Classification_small_shuffled](https://huggingface.co/datasets/ideepankarsharma2003/Midjourney_v6_Classification_small_shuffled)  
- [AIGeneratedImages_Midjourney](https://huggingface.co/datasets/ideepankarsharma2003/AIGeneratedImages_Midjourney)  

### Training Procedure  

- **Image Size:** 224x224  
- **Patch Size:** 4  
- **Embedding Dimension:** 128  
- **Layers:** 4  
- **Attention Heads per Stage:** [4, 8, 16, 32]  
- **Dropout Rates:**  
  - Attention: 0.0  
  - Hidden: 0.0  
  - Drop Path: 0.1  
- **Activation Function:** GeLU  
- **Optimizer:** AdamW  
- **Learning Rate Scheduler:** Cosine Annealing  
- **Precision:** float32  
- **Training Steps:** 3414  

## Evaluation  

### Testing Data, Factors & Metrics  

#### Testing Data  

The model was evaluated on a separate validation split from the training datasets.  

#### Metrics  

- **Accuracy**  
- **Precision & Recall**  
- **F1 Score**  

### Summary  

The model effectively distinguishes between AI-generated and human-created images, but its performance may be affected by dataset biases and out-of-distribution examples.  

## Citation  

If you use this model, please cite:  

```bibtex
@misc{ai_image_classification,
  author = {Deepankar Sharma},
  title = {AI Image Classification - Midjourney V6 & SDXL},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/ideepankarsharma2003/AI_ImageClassification_MidjourneyV6_SDXL}}
}
```

## Model Card Authors  

- **Author:** Deepankar Sharma  

---