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--- |
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library_name: transformers |
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tags: |
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- computer-vision |
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- image-classification |
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- vit |
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- deepfake-detection |
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- binary-classification |
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- pytorch |
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license: mit |
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metrics: |
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- accuracy: 99.20% |
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base_model: |
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- facebook/deit-base-distilled-patch16-224 |
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pipeline_tag: image-classification |
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--- |
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# Model Card for Virtus |
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Virtus is a fine-tuned Vision Transformer (ViT) model for binary image classification, specifically trained to distinguish between real and deepfake images. It achieves **~99.2% accuracy** on a balanced dataset of over 190,000 images. |
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## Model Details |
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### Model Description |
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Virtus is based on `facebook/deit-base-distilled-patch16-224` and was fine-tuned on a binary classification task using a large dataset of real and fake facial images. The training process involved class balancing, data augmentation, and evaluation using accuracy and F1 score. |
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- **Developed by:** [Agasta](https://github.com/Itz-Agasta) |
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- **Funded by:** None |
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- **Shared by:** Agasta |
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- **Model type:** Vision Transformer (ViT) for image classification |
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- **Language(s):** N/A (vision model) |
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- **License:** MIT |
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- **Finetuned from model:** [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) |
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### Model Sources |
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- **Repository:** [https://huggingface.co/agasta/virtus](https://huggingface.co/agasta/virtus) |
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## Uses |
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### Direct Use |
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This model can be used to predict whether an input image is a real or a deepfake. It can be deployed in image analysis pipelines or integrated into applications that require media authenticity detection. |
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### Downstream Use |
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Virtus may be used in broader deepfake detection systems, educational tools for detecting synthetic media, or pre-screening systems for online platforms. |
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### Out-of-Scope Use |
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- Detection of deepfakes in videos or audio |
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- General object classification tasks outside of the real/fake binary domain |
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## Bias, Risks, and Limitations |
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The dataset, while balanced, may still carry biases in facial features, lighting conditions, or demographics. The model is also not robust to non-standard input sizes or heavily occluded faces. |
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### Recommendations |
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- Use only on face images similar in nature to the training set. |
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- Do not use for critical or high-stakes decisions without human verification. |
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- Regularly re-evaluate performance with updated data. |
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## How to Get Started with the Model |
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```python |
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from transformers import AutoFeatureExtractor, AutoModelForImageClassification |
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from PIL import Image |
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import torch |
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model = AutoModelForImageClassification.from_pretrained("agasta/virtus") |
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extractor = AutoFeatureExtractor.from_pretrained("agasta/virtus") |
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image = Image.open("path_to_image.jpg") |
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inputs = extractor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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predicted_class = outputs.logits.argmax(-1).item() |
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print(model.config.id2label[predicted_class]) |
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``` |
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## Training Details |
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### Training Data |
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The dataset consisted of 190,335 self-collected real and deepfake face images, with RandomOverSampler used to balance the two classes. The data was split into 60% training and 40% testing, maintaining class stratification. |
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### Training Procedure |
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#### Preprocessing |
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- Images resized to 224x224 |
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- Augmentations: Random rotation, sharpness adjustments, normalization |
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#### Training Hyperparameters |
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- **Epochs:** 2 |
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- **Learning rate:** 1e-6 |
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- **Train batch size:** 32 |
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- **Eval batch size:** 8 |
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- **Weight decay:** 0.02 |
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- **Optimizer:** AdamW (via Trainer API) |
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- **Mixed precision:** Not used |
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## Evaluation |
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### Testing Data |
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Same dataset, stratified 60:40 split, used for evaluation. |
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### Metrics |
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- **Accuracy** |
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- **F1 Score (macro)** |
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- **Confusion matrix** |
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- **Classification report** |
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### Results |
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- **Accuracy:** 99.20% |
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- **F1 Score (macro):** 0.9920 |
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## Environmental Impact |
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- **Hardware Type:** NVIDIA Tesla V100 (Kaggle Notebook GPU) |
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- **Hours used:** ~2.3 hours |
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- **Cloud Provider:** Kaggle |
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- **Compute Region:** Unknown |
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- **Carbon Emitted:** Can be estimated via [MLCO2 Calculator](https://mlco2.github.io/impact#compute) |
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## Technical Specifications |
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### Model Architecture and Objective |
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The model is a distilled Vision Transformer (DeiT) designed for image classification with a binary objective: classify images as Real or Fake. |
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### Compute Infrastructure |
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- **Hardware:** 1x NVIDIA Tesla V100 GPU |
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- **Software:** PyTorch, Hugging Face Transformers, Datasets, Accelerate |
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## Citation |
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**BibTeX:** |
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```bibtex |
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@misc{virtus2025, |
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title={Virtus: Deepfake Detection using Vision Transformers}, |
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author={Agasta}, |
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year={2025}, |
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howpublished={\url{https://huggingface.co/agasta/virtus}}, |
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} |
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``` |
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**APA:** |
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Agasta. (2025). *Virtus: Deepfake Detection using Vision Transformers*. Hugging Face. https://huggingface.co/agasta/virtus |
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## Model Card Contact |
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For questions or feedback, reach out via [GitHub](https://github.com/Itz-Agasta) or open an issue on the [model repository](https://github.com/Itz-Agasta/Lopt/tree/main/models/image). or mail me at [email protected] |
