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
| { | |
| "DEVICE": "cpu", | |
| "_name_or_path": "not-lain/deepfake", | |
| "architectures": [ | |
| "DeepFakeModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "deepfakeconfig.DeepFakeConfig", | |
| "AutoModelForImageClassification": "deepfakemodel.DeepFakeModel" | |
| }, | |
| "custom_pipelines": { | |
| "deepfake": { | |
| "default": { | |
| "model": { | |
| "pt": [ | |
| "not-lain/deepfake", | |
| "main" | |
| ] | |
| } | |
| }, | |
| "impl": "pipeline.DeepFakePipeline", | |
| "pt": [ | |
| "AutoModelForImageClassification" | |
| ], | |
| "tf": [], | |
| "type": "multimodal" | |
| } | |
| }, | |
| "model_type": "ResNet", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.26.1" | |
| } | |