Instructions to use ProbeX/Model-J__ResNet__model_idx_0935 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_0935 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_0935") 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_0935") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0935", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80ab11a8a605a6fbe7da61c1c448aea4e0d58ff7e12757500b617b3fe2f8d724
- Size of remote file:
- 5.37 kB
- SHA256:
- f6225aa6f0d1e0cd16ef5eefa1da93aa7bd1775dc59d368024dfae01ec108e50
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