Instructions to use ProbeX/Model-J__ResNet__model_idx_0459 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_0459 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_0459") 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_0459") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0459") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b98f26eb24af6b66dc697100a4686706d79154e5673e90704f6e62c98f3910df
- Size of remote file:
- 5.37 kB
- SHA256:
- 2b20d532712f11ca1711c1f0d78a0cc4045a026ca81357320ef7308f01a9e2dc
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