Instructions to use ProbeX/Model-J__ResNet__model_idx_0841 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_0841 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_0841") 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_0841") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0841") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96cac46efc4d47e1718a405015292175474b3dca45080c113b7eb390c7fbb63d
- Size of remote file:
- 5.37 kB
- SHA256:
- 99f8ebb68588a7c7c93fd41166e16e4b1aaa710e8dbb545cdc8091496e68a744
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