Instructions to use softdev629/gm2jdlzu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use softdev629/gm2jdlzu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="softdev629/gm2jdlzu") 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/gm2jdlzu") model = AutoModelForImageClassification.from_pretrained("softdev629/gm2jdlzu") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a2a8a636b0c0ad50358f94cf0ac8858080c9a7cf110403f541f0040b01779a42
- Size of remote file:
- 343 MB
- SHA256:
- 56a376863c5b2cb7201fe3537625c115bbe163b600185bb62dcd4772f13b6d38
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