Instructions to use TalentoTechIA/Stevensm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TalentoTechIA/Stevensm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="TalentoTechIA/Stevensm") 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("TalentoTechIA/Stevensm") model = AutoModelForImageClassification.from_pretrained("TalentoTechIA/Stevensm") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b0283b6027d4cc64b9beca03255cb1572e2c23cda6b4f29471954f62417f02a6
- Size of remote file:
- 343 MB
- SHA256:
- 2f56d5591102d04ba4c7751be91fbda8813fe8d0c7e73396486286ad2a50b903
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