Instructions to use gechim/posecls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gechim/posecls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="gechim/posecls") 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("gechim/posecls") model = AutoModelForImageClassification.from_pretrained("gechim/posecls", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34ce4553b2bb54ddf1eec6d8826e2073928a52bb40d3f305df5b10cae518a403
- Size of remote file:
- 4.92 kB
- SHA256:
- 7a28f4a5d419c51598c330cfcac907ae6ec5149882d4a7d590e3552092de2f71
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