Instructions to use ProbeX/Model-J__ResNet__model_idx_0613 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_0613 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_0613") 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_0613") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0613") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11960612ffecc02756c47498c2809f6614d85784fc0bc9595155f7449c358b39
- Size of remote file:
- 171 MB
- SHA256:
- a29be81768753a237375e79c56bc2a24c702fd98efe65fd8d8106d92d6943e4c
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