import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("dima806/deepfake_vs_real_image_detection", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("ompathak/DeepFakeDetection")
prompt = "-"
image = pipe(prompt).images[0]DeepFakeDetection
- Prompt
- -
Model description
license: apache-2.0 metrics: - accuracy - f1
Checks whether an image is real or fake (AI-generated).
See https://www.kaggle.com/code/dima806/deepfake-vs-real-faces-detection-vit for more details.
``` Classification report:
precision recall f1-score support
Real 0.9921 0.9933 0.9927 38080
Fake 0.9933 0.9921 0.9927 38081
accuracy 0.9927 76161
macro avg 0.9927 0.9927 0.9927 76161 weighted avg 0.9927 0.9927 0.9927 76161 ```
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Model tree for ompathak/DeepFakeDetection
Base model
google/vit-base-patch16-224-in21k Finetuned
dima806/deepfake_vs_real_image_detection