Instructions to use n1hal/Pets_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use n1hal/Pets_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="n1hal/Pets_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("n1hal/Pets_Classification") model = AutoModelForImageClassification.from_pretrained("n1hal/Pets_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 554576e96034882bbe204082d804623c53a931e8d38f49909be7e88e890f1bc6
- Size of remote file:
- 343 MB
- SHA256:
- d974631f05c0503a84a1030d59b4f85b80f546ca05972ec47c3ff6cb2b5948ee
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