Instructions to use averrous/workout_model_vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use averrous/workout_model_vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="averrous/workout_model_vit") 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("averrous/workout_model_vit") model = AutoModelForImageClassification.from_pretrained("averrous/workout_model_vit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 632a4263c2d5ec777eb6ed33f6ceddc2dbf0f994416a8dca83f9783e902ebe7d
- Size of remote file:
- 5.11 kB
- SHA256:
- 40c50a4a0c4e9ff2f9e5869e056c68370989e225cc1e67e3e676f2662ad9aaeb
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