Instructions to use ProbeX/Model-J__ResNet__model_idx_0079 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_0079 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_0079") 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_0079") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0079") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ecc34e267ec130e1bff746a5f874057474185093e4e50050282e34d1f2bae683
- Size of remote file:
- 171 MB
- SHA256:
- 773afb5d962cbac62f489c0fbd89a6509fec1eea03520e58e59d7692f2198ff4
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