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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- image-classification |
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- ai-detection |
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- flux |
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- vision-transformer |
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- fake-detection |
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datasets: |
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- huggan/wikiart |
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- ash12321/flux-1-dev-generated-10k |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: FLUX Detector ViT |
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results: |
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- task: |
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type: image-classification |
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name: AI Image Detection |
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metrics: |
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- type: accuracy |
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value: 0.9985 |
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name: Test Accuracy |
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- type: f1 |
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value: 0.9985 |
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name: F1 Score |
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--- |
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# FLUX Detector - Vision Transformer |
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## Model Description |
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This model is a **specialized binary classifier** trained to detect images generated by **FLUX.1-dev** (Black Forest Labs). It achieves **99.85% accuracy** with **ZERO false positives** on held-out test data. |
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### Key Features |
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- 🎯 **Specialist Detector**: Optimized specifically for FLUX.1-dev images |
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- 🚀 **Exceptional Accuracy**: 99.85% test accuracy |
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- 🛡️ **Zero False Positives**: Never misclassifies real images as fake |
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- ⚡ **Fast Inference**: ~10ms per image on GPU |
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- 📊 **Well-Validated**: Separate train/val/test splits with no overlap |
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### Performance |
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``` |
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Test Accuracy: 0.9985 |
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Precision: 1.0000 (PERFECT!) |
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Recall: 0.9970 |
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F1 Score: 0.9985 |
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AUC-ROC: 1.0000 (PERFECT!) |
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False Positive Rate: 0.0000 (0.0%!) |
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False Negative Rate: 0.0030 |
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``` |
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## Quick Start |
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```python |
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import torch |
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from PIL import Image |
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from transformers import ViTForImageClassification, ViTImageProcessor |
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# Load model and processor |
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model = ViTForImageClassification.from_pretrained( |
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"ash12321/flux-detector-vit" |
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) |
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processor = ViTImageProcessor.from_pretrained( |
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"google/vit-base-patch16-224" |
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) |
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# Load image |
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image = Image.open("test.jpg") |
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inputs = processor(images=image, return_tensors="pt") |
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# Get prediction |
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model.eval() |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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probs = torch.softmax(outputs.logits, dim=1) |
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if probs[0][1] > 0.5: |
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print(f"FLUX-Generated ({probs[0][1]:.2%} confident)") |
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else: |
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print(f"Real Image ({probs[0][0]:.2%} confident)") |
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``` |
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## Using the model.py Helper |
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```python |
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from model import detect_image |
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result = detect_image("test.jpg", model_path="ash12321/flux-detector-vit") |
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print(f"Is Fake: {result['is_fake']}") |
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print(f"Confidence: {result['confidence']:.2%}") |
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``` |
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## Files in this Repository |
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- `pytorch_model.bin` - Model weights |
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- `config.json` - Model configuration |
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- `model.py` - Model architecture and helper functions |
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- `README.md` - This documentation |
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- `training_results.json` - Detailed training metrics |
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- `training_curves.png` - Training visualization |
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- `confusion_matrix.png` - Test set confusion matrix |
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## Citation |
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```bibtex |
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@misc{flux-detector-vit, |
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author = {ash12321}, |
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title = {FLUX Detector - Vision Transformer}, |
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year = {2024}, |
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publisher = {HuggingFace}, |
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howpublished = {\url{https://huggingface.co/ash12321/flux-detector-vit}}, |
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} |
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``` |
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--- |
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**License**: Apache 2.0 |
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**Created**: 2025-12-31 |
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