Instructions to use DevforMM/mutli_class_clasification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DevforMM/mutli_class_clasification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DevforMM/mutli_class_clasification") 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("DevforMM/mutli_class_clasification") model = AutoModelForImageClassification.from_pretrained("DevforMM/mutli_class_clasification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 74769395c887ceeb20c8159b00dbba9ec1c57f6acdd892a1bb8072217516d7db
- Size of remote file:
- 343 MB
- SHA256:
- 0ee48b3138b1299798603bf97efc460ac6c99d77d8ce272ad43b88d20863335a
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