Instructions to use ProbeX/Model-J__ResNet__model_idx_0111 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_0111 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_0111") 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_0111") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0111") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12dcd78597d7b02e7b08a3bd18d68131fa065b67e36a67ef8b7a08b6369a4343
- Size of remote file:
- 5.37 kB
- SHA256:
- f70725c2dfe1fa69ecbce3fd86fd3e19c448637fcad12fb87ee4516f8b721e5a
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