Instructions to use google/vit-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/vit-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/vit-base-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("google/vit-base-patch16-224") model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224") - Inference
- Notebooks
- Google Colab
- Kaggle
Question: edge/mobile deployment β anyone tested?
#24
by 3morixd - opened
We benchmark models on 40 phones (Snapdragon 865) at Dispatch AI (FZE, UAE).
Question: has anyone tested this model on mobile/edge? Interested in:
- Inference speed (t/s)
- Model size after quantization
- RAM usage
Happy to share phone farm benchmark results.
- Dispatch AI (FZE), Sharjah UAE