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")# pip install -U transformers accelerate # 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
Update metadata with huggingface_hub
Browse files
README.md
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- name: vit-base-patch16-224-in21k-face-recognition
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: imagefolder
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type: imagefolder
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split: train
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args: default
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metrics:
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type: accuracy
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value: 0.999957997311828
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- name: vit-base-patch16-224-in21k-face-recognition
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results:
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- task:
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type: image-classification
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name: Image Classification
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dataset:
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name: imagefolder
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type: imagefolder
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split: train
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args: default
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metrics:
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- type: accuracy
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value: 0.999957997311828
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name: Accuracy
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- task:
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type: image-classification
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name: Image Classification
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dataset:
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name: custom
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type: custom
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split: test
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metrics:
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- type: precision
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value: 1.0
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name: Precision
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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