Instructions to use flatmoon102/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flatmoon102/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="flatmoon102/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("flatmoon102/image_classification") model = AutoModelForImageClassification.from_pretrained("flatmoon102/image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd0b7ef46fcaa2a580410b3fe426013c441ebc5fad3078956d35431cfbbc1d37
- Size of remote file:
- 343 MB
- SHA256:
- 38fa48067816977e6510b7f14aa3936163ca09265aeea460f3f0494d8b1c7630
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