Instructions to use ProbeX/Model-J__ResNet__model_idx_0854 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0854 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0854") 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("ProbeX/Model-J__ResNet__model_idx_0854") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0854") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cddfb763dc5bd3fee2033c827fcb5b758b96e20772330139f830a55c42bf60db
- Size of remote file:
- 5.37 kB
- SHA256:
- 7f46252eedbebb0616a9cc8166b8ad15367c686ac71d86d39b4dda85147e11e1
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