Instructions to use jctivensa/Ivenpeople_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jctivensa/Ivenpeople_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jctivensa/Ivenpeople_v2") 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("jctivensa/Ivenpeople_v2") model = AutoModelForImageClassification.from_pretrained("jctivensa/Ivenpeople_v2") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
license: apache-2.0 tags:
- vision
- image-classification datasets:
- imagenet-1k widget:
- src: >- https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger
- src: >- https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot
- src: >- https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace
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