Image Classification
Transformers
Safetensors
vit
deep-fake
ViT
detection
Image
transformers-4.49.0.dev0
precision-92.12
v2
Instructions to use prithivMLmods/Deep-Fake-Detector-v2-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Deep-Fake-Detector-v2-Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Deep-Fake-Detector-v2-Model") 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("prithivMLmods/Deep-Fake-Detector-v2-Model") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Deep-Fake-Detector-v2-Model") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a5f7d2a4ed8e71d52bf388678b98573ccbc13529a5d123040e23f54d3a894f0
- Size of remote file:
- 343 MB
- SHA256:
- 88ac0939285009e3700cfc9b9a0912b14b092d200ef0df86efe4f2cf190ea4de
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.