Instructions to use BeckerAnas/grateful-shape-212 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BeckerAnas/grateful-shape-212 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="BeckerAnas/grateful-shape-212") 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("BeckerAnas/grateful-shape-212") model = AutoModelForImageClassification.from_pretrained("BeckerAnas/grateful-shape-212", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2553e662d909c6323b4a8065c891061178aedfe46929d4ae5a104f494909e969
- Size of remote file:
- 5.78 kB
- SHA256:
- fe349f1943e660411273e37e731a16a3ebed896c26d2aa12dfd84075b6b3012c
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