Instructions to use ProbeX/Model-J__ResNet__model_idx_0826 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_0826 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_0826") 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_0826") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0826") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 38b9c57da74158e925fea0f6850ad86e64c6eb81de56a83ecaa161e400b442aa
- Size of remote file:
- 5.37 kB
- SHA256:
- 2eb2a608b6da7289e6a46cbeec6aea86597881e708887a22a4d03b079acba3c2
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