Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use jayanta/vit-base-patch16-224-in21k-face-recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayanta/vit-base-patch16-224-in21k-face-recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jayanta/vit-base-patch16-224-in21k-face-recognition") 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("jayanta/vit-base-patch16-224-in21k-face-recognition") model = AutoModelForImageClassification.from_pretrained("jayanta/vit-base-patch16-224-in21k-face-recognition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c0be43285321a110e4957b333a1d5aa4f607b0a5a466d03168c71f998b1edce2
- Size of remote file:
- 343 MB
- SHA256:
- 1a18500560d047cc80c4b4ae30f12d9aa420913abdc09bde7ec9e800153fdd6a
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