Instructions to use softdev629/xjmuddvt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use softdev629/xjmuddvt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="softdev629/xjmuddvt") 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("softdev629/xjmuddvt") model = AutoModelForImageClassification.from_pretrained("softdev629/xjmuddvt") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 796118ea968f825d5a438ad2c5d426e1051aa36f94086d6557fa0ea0b3753b1f
- Size of remote file:
- 128 Bytes
- SHA256:
- 78635a031f4442590ce06eef9a02abce37572f3aacdc0cc18c61d88750de5555
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