Instructions to use WinKawaks/vit-small-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WinKawaks/vit-small-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="WinKawaks/vit-small-patch16-224") 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("WinKawaks/vit-small-patch16-224") model = AutoModelForImageClassification.from_pretrained("WinKawaks/vit-small-patch16-224") - Inference
- Notebooks
- Google Colab
- Kaggle
What is the dataset on which it was pretrained?
#3
by plthon - opened
Unlike the model pretrained on ImageNet-21k, this one does not provide information about the dataset on which it was pretrained. When pretrained is set to True, there is an improvement in performance. Therefore, it would be beneficial to know the dataset used for the pretrained model. Thanks.
WinKawaks changed discussion status to closed