Instructions to use ProbeX/Model-J__ResNet__model_idx_0636 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_0636 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_0636") 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_0636") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0636") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f197858460f9382701aae4f47969855c5868fcd6ed2d61a2796c1c9b094c3ab
- Size of remote file:
- 5.37 kB
- SHA256:
- 990a4e5b482af3170ccb41061407d126b076a93050c7d19000a0d3acc096c259
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