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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f5bb08dbd4369100e8a0a4a65764282d978f8e47e9ed0714ec95e51a35674d0e
- Size of remote file:
- 5.37 kB
- SHA256:
- 0e18ce31b50960b5cc52c06a9d3baa8644bab460373d7b85f7a192b7ef6d6ec5
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