metadata
tags:
- image-classification
- vision
- vit
- deepfake
- binary-classification
pipeline_tag: image-classification
language: en
license: apache-2.0
🧠 Model1-v1-Rival — Deepfake Image Classifier
This model is a fine-tuned Vision Transformer (ViT) for detecting whether a face image is REAL or FAKE (Deepfake).
It was trained using a mixed deepfake dataset with augmentations to ensure robustness across manipulation methods and compression artifacts.
📌 Model Details
| Field | Value |
|---|---|
| Base Model | google/vit-base-patch16-224-in21k |
| Task | Image Classification (Binary) |
| Labels | {0: Fake, 1: Real} |
| File Format | safetensors |
| Optimizer | AdamW |
| Epochs | 2 |
| Learning Rate | 1e-6 |
| Batch Size | 32 |
🏷️ Labels
The model predicts:
| Label | Meaning |
|---|---|
fake |
manipulated / deepfake image |
real |
authentic human face |
🚀 Usage
🔧 With transformers
from transformers import AutoModelForImageClassification, AutoImageProcessor
from PIL import Image
import torch
model_name = "alrivalda/Model1-v1-Rival"
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)
img = Image.open("your_image.jpg")
inputs = processor(img, return_tensors="pt")
outputs = model(**inputs).logits
probabilities = torch.softmax(outputs, dim=1)
pred_id = torch.argmax(probabilities).item()
label = model.config.id2label[pred_id]
print("Prediction:", label)
print("Confidence:", float(probabilities[0][pred_id]))