Instructions to use ProbeX/Model-J__ResNet__model_idx_0137 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_0137 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_0137") 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_0137") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0137") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eb91b22ce249a2b70b65cdf775bd03f9db3ea78386fd7453ff69903201def2e8
- Size of remote file:
- 5.37 kB
- SHA256:
- bebd91df72beacbdb9c18497cd499895cc7330eaf30847a62550647ca4f75a85
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