Instructions to use ProbeX/Model-J__ResNet__model_idx_0982 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_0982 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_0982") 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_0982") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0982") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 849565e5b42ccadf705bd2f948e7fc1e364e02236ab3a3d5a22cbc803a086126
- Size of remote file:
- 5.37 kB
- SHA256:
- d7b4d0f94625faf8535da316ad3e2557983d6761c979f31569601bbcc86a4d94
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