Instructions to use frncscp/patacoswin_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frncscp/patacoswin_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="frncscp/patacoswin_v1") 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("frncscp/patacoswin_v1") model = AutoModelForImageClassification.from_pretrained("frncscp/patacoswin_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f3c1fb2fb9572befc902c12c298c79804e18980cd20c34dfea5a738ab506fd1
- Size of remote file:
- 348 MB
- SHA256:
- ebee986153bdd1176753a378ca85ace9531e77df28644e41969c7f47f515974e
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