metadata
tags:
- vision
- image-classification
- vit
- transformer
- fake-image-detection
license: apache-2.0
datasets:
- ciplab/real-and-fake-face-detection
model-index:
- name: SahAi
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: Real and Fake Face Detection Dataset
type: ciplab/real-and-fake-face-detection
metrics:
- name: Accuracy
type: accuracy
value: 99.12
- name: Precision
type: precision
value: 98.95
- name: Recall
type: recall
value: 99
SahAi - Enhanced Fake Image Detection Model
SahAi is a fine-tuned Vision Transformer (ViT) model designed for fake image localization in social media.
🚀 Model Description
- Base Model: ViT-B/16
- Input Size: 224x224
- Output Classes: Real (0), Fake (1)
🔥 How to Use
from transformers import ViTForImageClassification, AutoFeatureExtractor
from PIL import Image
import torch
model = ViTForImageClassification.from_pretrained("SahilSha/SahAi")
feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
image = Image.open("test_image.jpg").convert("RGB")
inputs = feature_extractor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
prediction = outputs.logits.argmax(-1).item()
print("Real" if prediction == 0 else "Fake")