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:
- 64bee462e0635ce388fba6e681751450f134ed6a9ba3d2ed17cc860128333dd4
- Size of remote file:
- 343 MB
- SHA256:
- 50dcaefc090c82f33aa1d0f7e4d52f16bf465b4f867930eb893d366456424e36
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