Instructions to use TalentoTechIA/william_Rosero with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TalentoTechIA/william_Rosero with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="TalentoTechIA/william_Rosero") 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/william_Rosero") model = AutoModelForImageClassification.from_pretrained("TalentoTechIA/william_Rosero") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6e90791537071a83040baa1b7ad259688a74c6aecad2a3b3317c19db74bfd0c
- Size of remote file:
- 5.37 kB
- SHA256:
- 1ad2c91e2ba59165e05469e3aa3fffe5215a3aef254a1d80d9d907e901705b44
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.