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") - 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
| { | |
| "architectures": [ | |
| "ViTForImageClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "encoder_stride": 16, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 384, | |
| "image_size": 384, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-06, | |
| "mlp_ratio": 4, | |
| "model_type": "vit", | |
| "num_attention_heads": 12, | |
| "num_channels": 3, | |
| "num_classes": 1, | |
| "num_heads": 6, | |
| "num_hidden_layers": 12, | |
| "num_layers": 12, | |
| "patch_size": 16, | |
| "qkv_bias": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.50.0.dev0" | |
| } | |