Instructions to use ProbeX/Model-J__ResNet__model_idx_0738 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_0738 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_0738") 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_0738") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0738") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2486ff4382b7f7fc778f44fadf3327dad9074607c43ad702e74f4c9b79219809
- Size of remote file:
- 5.37 kB
- SHA256:
- 83df84909d5921aca879e1da9f06207f856d65df221c2e65e047e614aaa28973
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