Image Classification
Transformers
PyTorch
TensorBoard
English
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use DunnBC22/vit-base-patch16-224-in21k_Human_Activity_Recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/vit-base-patch16-224-in21k_Human_Activity_Recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DunnBC22/vit-base-patch16-224-in21k_Human_Activity_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("DunnBC22/vit-base-patch16-224-in21k_Human_Activity_Recognition") model = AutoModelForImageClassification.from_pretrained("DunnBC22/vit-base-patch16-224-in21k_Human_Activity_Recognition") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b31118b94322f768c35531d9dbe730c3aff4b4e30f6467cce874d41ced99bce9
- Size of remote file:
- 3.5 kB
- SHA256:
- 1b599d4866c81747bdb5965096f6ca49fe90d8ad2a979839a1b091ad8c69cc69
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