Instructions to use ProbeX/Model-J__ResNet__model_idx_0487 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_0487 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_0487") 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_0487") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0487") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cde21604619f93d1c915b7c58bb39b823cd8871c4f55114683d949fc4ac06994
- Size of remote file:
- 5.37 kB
- SHA256:
- 279fb779b8064038178f5e7c9c7408b0c59b083f99e0e4258a488adbc81c1dfb
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