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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b500d434c62689829c366ab525f1994ec072fa856a364b3d28132ee106e0f866
- Size of remote file:
- 128 Bytes
- SHA256:
- f797ef928cf6658340080a1b26549f55a0b3dcea14012dd7b708730dbd310690
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