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:
- f21a303a7d8f13680726e67973d2310ac43764f0796cd0e4051742c18773803a
- Size of remote file:
- 343 MB
- SHA256:
- 1d72475fb3701aebff59baa9c99a23bfe624365d4318ff118ff474355fc336ad
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