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:
- 55127e87c680760fc10b80ef8b854b9bd1f70f4c1c9bd45bdf326dc23e86ae42
- Size of remote file:
- 343 MB
- SHA256:
- 52eb78f9d9440c6d89f188b9d06e0d060033e4a9cb725dbe78bc90aab6fc6826
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