Instructions to use ProbeX/Model-J__ResNet__model_idx_0548 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_0548 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_0548") 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_0548") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0548") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e098650040e9f12dafbc8d43102e60fdebf31a866b8492528e68253d0593604
- Size of remote file:
- 5.37 kB
- SHA256:
- f3253917e3f2dff8bf3a4081e9063dfde7d99fa154c1a0fbeca0143d5d8c6274
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