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:
- 53b641e7279b73efcec4f46e151670895aabbdc3fff4ba90957423bee98f4b7f
- Size of remote file:
- 6.15 kB
- SHA256:
- 42cd5507faa0a396faa547caa0125727d666eb09fd9192151a6dfb703e35fb18
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.