Instructions to use ProbeX/Model-J__ResNet__model_idx_0300 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_0300 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_0300") 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_0300") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0300") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 477c8a39b3ce60669ed50d7842667e283b37f35601e2413df90d4fc16c4e1c6d
- Size of remote file:
- 5.37 kB
- SHA256:
- f17c2dca19b5c846e597c5f3d1db4f090ed81af04a84382fc432c50f8743e757
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