Instructions to use ProbeX/Model-J__ResNet__model_idx_0631 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_0631 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_0631") 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_0631") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0631") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6fee526d3b7cb981d15089628af60837341c9510b8f1d5edc08077d409c4f8cb
- Size of remote file:
- 5.37 kB
- SHA256:
- 377dcc95cd3b0f628b3028f820fee4732a98bae330a09e808637707d0f751b8b
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