Image Classification
Transformers.js
ONNX
timm
Transformers
vit
detection
deepfake
forensics
deepfake_detection
community
opensight
Instructions to use onnx-community/CommunityForensics-DeepfakeDet-ViT-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use onnx-community/CommunityForensics-DeepfakeDet-ViT-ONNX with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-classification', 'onnx-community/CommunityForensics-DeepfakeDet-ViT-ONNX'); - timm
How to use onnx-community/CommunityForensics-DeepfakeDet-ViT-ONNX with timm:
import timm model = timm.create_model("hf_hub:onnx-community/CommunityForensics-DeepfakeDet-ViT-ONNX", pretrained=True) - Transformers
How to use onnx-community/CommunityForensics-DeepfakeDet-ViT-ONNX with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="onnx-community/CommunityForensics-DeepfakeDet-ViT-ONNX") 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("onnx-community/CommunityForensics-DeepfakeDet-ViT-ONNX") model = AutoModelForImageClassification.from_pretrained("onnx-community/CommunityForensics-DeepfakeDet-ViT-ONNX", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 433 Bytes
f9fc651 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"crop_pct": 0.875,
"crop_size": 384,
"do_convert_rgb": null,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_processor_type": "ViTImageProcessor",
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 440,
"width": 440
}
}
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