Instructions to use averrous/workout_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use averrous/workout_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="averrous/workout_model") 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") model = AutoModelForImageClassification.from_pretrained("averrous/workout_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e9ac510e9f6d40e00d7c7fdd7620cd64483b5bb7c2bdf0a82b9644f348dc973
- Size of remote file:
- 343 MB
- SHA256:
- 6d97c73e4c66caf514b617a993bdb53550769b54e1a1c18603acc10d365893c9
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