Instructions to use nguyenkhoa/dinov2_Liveness_detection_v2.2.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nguyenkhoa/dinov2_Liveness_detection_v2.2.3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nguyenkhoa/dinov2_Liveness_detection_v2.2.3") 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("nguyenkhoa/dinov2_Liveness_detection_v2.2.3") model = AutoModelForImageClassification.from_pretrained("nguyenkhoa/dinov2_Liveness_detection_v2.2.3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93e5eda5154ff0b9dc563ef81bcd691b476f6aa0b9daf25f9891d7f9871b566c
- Size of remote file:
- 5.37 kB
- SHA256:
- d4d5004cc5a618215840a424b0fa3f745b1654433d7970fb1f2f8cbe5aa2436b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.