Image Classification
Transformers
Safetensors
timm
vit
detection
deepfake
forensics
deepfake_detection
community
opensight
Instructions to use buildborderless/CommunityForensics-DeepfakeDet-ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use buildborderless/CommunityForensics-DeepfakeDet-ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="buildborderless/CommunityForensics-DeepfakeDet-ViT") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("buildborderless/CommunityForensics-DeepfakeDet-ViT") model = AutoModelForImageClassification.from_pretrained("buildborderless/CommunityForensics-DeepfakeDet-ViT", device_map="auto") - timm
How to use buildborderless/CommunityForensics-DeepfakeDet-ViT with timm:
import timm model = timm.create_model("hf_hub:buildborderless/CommunityForensics-DeepfakeDet-ViT", pretrained=True) - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 369 Bytes
6f9c8ad | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"quantization": {
"int8": "onnxruntime dynamic quantization, QInt8",
"uint8": "onnxruntime dynamic quantization, QUInt8",
"quantized": "copy of int8 for compatibility",
"q4": "MatMulNBitsQuantizer, bits=4, block_size=32, symmetric"
},
"export_tool": "optimum-cli export onnx",
"export_task": "image-classification",
"onnx_version": "v1.1"
}
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