Instructions to use ditobagus/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ditobagus/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ditobagus/image_classification") 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("ditobagus/image_classification") model = AutoModelForImageClassification.from_pretrained("ditobagus/image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ee42cfaf31b4321f6e6bc0facffeddcf7c5e4f6b3ad9c0f424f10e24229f025
- Size of remote file:
- 4.09 kB
- SHA256:
- 5dabdf4a6eb0878567888394280f2f7f82b60fc2dbb855c5b59c8bf38d1d94a3
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