Instructions to use ProbeX/Model-J__ResNet__model_idx_0718 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_0718 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_0718") 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_0718") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0718") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5032c182a781768f0ad80b0db5a2fdcce039777257c2bfc885b9c293ff6e36ed
- Size of remote file:
- 5.37 kB
- SHA256:
- 0e9657b99f07d0dbb58827d76d7f3922ba740b87814e222046093642802c46d1
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