Instructions to use ProbeX/Model-J__ResNet__model_idx_0913 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_0913 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_0913") 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_0913") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0913") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4d2d59eb5317d82bd9dd96231f25a02d11c94eec71e029c27c71e22bcbbbe83
- Size of remote file:
- 171 MB
- SHA256:
- ae42c23b64f34da5a20967f0f469a02ff49b5efd2e2c9f9ff561d6d8630f7bea
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