Instructions to use Mo0310/5242_scratch_wbc100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mo0310/5242_scratch_wbc100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Mo0310/5242_scratch_wbc100") 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("Mo0310/5242_scratch_wbc100") model = AutoModelForImageClassification.from_pretrained("Mo0310/5242_scratch_wbc100", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a819fadaf4ac337c0ef588aa331e361ae8f5e7b99f9362cb77da0a63eb369aee
- Size of remote file:
- 343 MB
- SHA256:
- e074eacd012bb2cc959150d754f864889582dd8c19a6d8f9af1e73a13566059c
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