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 checkpoint-303/rng_state.pth from prithivMLmods/Gym-Workout-Classifier-SigLIP2: direct link, hf CLI and curl.
- Browser
- Download file 14.2 kB
-
https://huggingface.co/prithivMLmods/Gym-Workout-Classifier-SigLIP2/resolve/main/checkpoint-303/rng_state.pth
- Command line
-
hf download hf://prithivMLmods/Gym-Workout-Classifier-SigLIP2/checkpoint-303/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/prithivMLmods/Gym-Workout-Classifier-SigLIP2/resolve/main/checkpoint-303/rng_state.pth
14.2 kB
- Xet hash:
- eda990e4ca77788bbd0a3ce0057038ac04175ac223c4cf60b4d76f87ca82e588
- Size of remote file:
- 14.2 kB
- SHA256:
- 5ea4d82ec065f1a80d09cb43154415696fffa5c5758f6f13af898d13df5452dd
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