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:
- 54ec7724e63e7104ae192ddf833bfec971350938ef0f285572683b449c1f5221
- Size of remote file:
- 343 MB
- SHA256:
- 524cdd31648e2ed840a06351f6955712f5af4bf7cbaa3509331ef73dcb79fa79
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