Instructions to use hawada/vit-base-patch16-224-in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hawada/vit-base-patch16-224-in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hawada/vit-base-patch16-224-in1k") 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("hawada/vit-base-patch16-224-in1k") model = AutoModelForImageClassification.from_pretrained("hawada/vit-base-patch16-224-in1k") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
This is a ViT-base model, pre-trained for image classification on ImageNet100-224 dataset: https://www.kaggle.com/datasets/lyfora/processed-imagenet-dataset-224
Uses
The model is intended for benchmarking of HawAda adapters (e.g., https://huggingface.co/hawada/vit-base-patch16-224-rtx4090-gated)
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Model tree for hawada/vit-base-patch16-224-in1k
Base model
google/vit-base-patch16-224