Instructions to use stochastic/vit_transfer_randomstreetview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stochastic/vit_transfer_randomstreetview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="stochastic/vit_transfer_randomstreetview") 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("stochastic/vit_transfer_randomstreetview") model = AutoModelForImageClassification.from_pretrained("stochastic/vit_transfer_randomstreetview", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 465a4f3d860bcfdd2bf7e7b66f9f226285ba7077b89369e8a40e7c43b9a6f475
- Size of remote file:
- 3.38 kB
- SHA256:
- 7af35f75c85a7715fe5fdced99d71d68d3de7a979152dc20e26f1f7eb986fc6c
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