Instructions to use gustavomalkomes/vit-base-patch16-224-in21k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gustavomalkomes/vit-base-patch16-224-in21k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="gustavomalkomes/vit-base-patch16-224-in21k") 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("gustavomalkomes/vit-base-patch16-224-in21k") model = AutoModelForImageClassification.from_pretrained("gustavomalkomes/vit-base-patch16-224-in21k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a0f4aa8a39e4bdbdd538e28590da9745a3926d34d1052ecff0fe4c7f4cb171ab
- Size of remote file:
- 5.11 kB
- SHA256:
- 319797304309ece23d68b0b3a280accc09a1425748647ed856607cd27b4a1659
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