Instructions to use ProbeX/Model-J__ResNet__model_idx_0722 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_0722 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_0722") 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_0722") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0722") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb4e7f84ad619cb83e392f950df3aebd7304f4bf33f0175be333a7665e7c6a45
- Size of remote file:
- 5.37 kB
- SHA256:
- d95e649e70b10cb1c2f9271694a1f366d2171b6b8621513aafb0695a8c7b39c3
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