Instructions to use ProbeX/Model-J__ResNet__model_idx_0438 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_0438 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_0438") 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_0438") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0438") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a3c76602b892218653317984f156d40d33484e20c94d46f3e7b376f1f55cd090
- Size of remote file:
- 5.37 kB
- SHA256:
- 6552ee31f1dcd8cedd001cf1f3a63b70c8e83e63e2f29bf0a5edd6c94fabbbee
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