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:
- a37abcd14d7d92e34289fdbf3d59ed2b60ddc75a818d8feaa1ab3b7d37f5deca
- Size of remote file:
- 4.09 kB
- SHA256:
- 869074406868df89c8571f285ca751d997164261962d4f646ba8f3b18d6aadce
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