Instructions to use ProbeX/Model-J__ResNet__model_idx_0432 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_0432 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_0432") 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_0432") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0432", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fc881363a0c208a9bcd3785d5ab47a4922c972f350529939bdf24c3430c1fa5b
- Size of remote file:
- 5.37 kB
- SHA256:
- 9903eda5173ef5e28e127ffb7cd2c8c51eb3cf7a13b77e20a02b7e5898f0cd1a
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