🧠 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.

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📌 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

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🏷️ Labels

The model predicts:

Label	Meaning
fake	manipulated / deepfake image
real	authentic human face

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🚀 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]))