Instructions to use ProbeX/Model-J__ResNet__model_idx_0457 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_0457 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_0457") 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_0457") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0457") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7521ca9b54ed92558319d65960a21236cb763e314d00a39868ed4b1ff331cc6d
- Size of remote file:
- 5.37 kB
- SHA256:
- 1305828afd9553896103ba2f082c91a6a9cfcaf2f10418798d9ab16b65d4a869
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