Instructions to use ProbeX/Model-J__ResNet__model_idx_0444 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_0444 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_0444") 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_0444") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0444") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49c086d5d18212508390bf60280da105859b59094d4517c209f0f652fe01ec01
- Size of remote file:
- 5.37 kB
- SHA256:
- 61bcc049626c87c50489ba86378bfd99dcd98e7a91adf47cca8c17ed1287419e
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