Instructions to use ProbeX/Model-J__ResNet__model_idx_0555 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_0555 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_0555") 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_0555") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0555") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 64b5efaa144f4f870ed4e467d62ada30ea35c5a9f472c907a60d2302dffd2ff9
- Size of remote file:
- 5.37 kB
- SHA256:
- 19099b3efc24fe68e68d5d3c0d0ffc0e4c7d0df2a921c9c1cdbdaa24c97a1777
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