Image Classification
Transformers
PyTorch
TensorBoard
English
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use DunnBC22/vit-base-patch16-224-in21k-weather-images-classification 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-weather-images-classification 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-weather-images-classification") 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-weather-images-classification") model = AutoModelForImageClassification.from_pretrained("DunnBC22/vit-base-patch16-224-in21k-weather-images-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad3b1fa049e91a806084ef1f34ffb728ed81415cbf9800ec39513f8c647913da
- Size of remote file:
- 343 MB
- SHA256:
- 4ad80e087f6bd5fe7768527e6cadb3f06f34ac84420b85b68b99d2582a6a82fc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.