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
| 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' |