Instructions to use universalml/fs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalml/fs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="universalml/fs") 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("universalml/fs") model = AutoModelForImageClassification.from_pretrained("universalml/fs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bca5b12d60b3a97953eb377f738e4373a55f37f8f10485276859adb14381d926
- Size of remote file:
- 343 MB
- SHA256:
- 569064084863f9a14e379fc05fc4b9fff6df19f8c5ebe63e9bbaf5fd1bd9e16f
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