Instructions to use SivaResearch/Fake_Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SivaResearch/Fake_Detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SivaResearch/Fake_Detection", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("SivaResearch/Fake_Detection", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 260 Bytes
f3f8c30 | 1 2 3 4 5 6 7 | from transformers import PretrainedConfig
import torch
class DeepFakeConfig(PretrainedConfig):
model_type = "ResNet"
def __init__(self,**kwargs):
super().__init__(**kwargs)
self.DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu' |