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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from prithivMLmods/Gym-Workout-Classifier-SigLIP2: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/prithivMLmods/Gym-Workout-Classifier-SigLIP2/resolve/main/training_args.bin
- Command line
-
hf download hf://prithivMLmods/Gym-Workout-Classifier-SigLIP2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/prithivMLmods/Gym-Workout-Classifier-SigLIP2/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- a922748aee673d59db34d9c7d428bb025c84347cae016d215f2b3b40b63a1c42
- Size of remote file:
- 5.3 kB
- SHA256:
- 65e93f97e638176dea767ae127697cac596f48d3f186cb5ebd2afc04890df283
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