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: 682 Bytes
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"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"
}
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