Instructions to use ProbeX/Model-J__ResNet__model_idx_0472 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_0472 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_0472") 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_0472") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0472") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ebcf3a5dea07b02c891da82e87f674a6cd1fe79605fa7cf0c2c3c388194df72c
- Size of remote file:
- 5.37 kB
- SHA256:
- 591b54c1487229a2d2d5f081ae25d982424972c368fdffb024798dd61592cab3
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