Instructions to use ProbeX/Model-J__ResNet__model_idx_0809 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_0809 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_0809") 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_0809") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0809", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d6c11f9be783a034839a24ea9ffac25a0a21aefe744980ab8434d2a171c2fbe
- Size of remote file:
- 5.37 kB
- SHA256:
- c8742419fbd76828141c75e603d4041ac8e3a8c83ae462c68b4c87b643d23c24
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