Instructions to use prithivMLmods/Gym-Workout-Classifier-SigLIP2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Gym-Workout-Classifier-SigLIP2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Gym-Workout-Classifier-SigLIP2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Gym-Workout-Classifier-SigLIP2") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Gym-Workout-Classifier-SigLIP2") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -74,7 +74,7 @@ The model categorizes images into 22 workout classes:
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- **Class 19:** "t bar row"
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- **Class 20:** "tricep dips"
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- **Class 21:** "tricep pushdown"
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# **Dataset ID2LABEL**
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```py
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- **Class 19:** "t bar row"
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- **Class 20:** "tricep dips"
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- **Class 21:** "tricep pushdown"
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# **Dataset ID2LABEL**
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```py
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