Image Classification
Transformers
ONNX
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")# pip install -U transformers accelerate # 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
Download preprocessor_config.json from buildborderless/CommunityForensics-DeepfakeDet-ViT: direct link, hf CLI and curl.
- Browser
- Download file 258 Bytes
-
https://huggingface.co/buildborderless/CommunityForensics-DeepfakeDet-ViT/resolve/refs%2Fpr%2F7/preprocessor_config.json
- Command line
-
hf download hf://buildborderless/CommunityForensics-DeepfakeDet-ViT@refs/pr/7/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/buildborderless/CommunityForensics-DeepfakeDet-ViT/resolve/refs%2Fpr%2F7/preprocessor_config.json
258 Bytes
| { | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "do_center_crop": true, | |
| "crop_size": 384, | |
| "image_mean": [0.48145466, 0.4578275, 0.40821073], | |
| "image_std": [0.26862954, 0.26130258, 0.27577711], | |
| "resample": 3, | |
| "size": { | |
| "shortest_edge": 440 | |
| } | |
| } |