Instructions to use ProbeX/Model-J__ResNet__model_idx_0681 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_0681 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_0681") 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_0681") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0681", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 120de1768ca316b0a8151995336b0bf252d4c791883bb7ffa57086784ac6fa83
- Size of remote file:
- 171 MB
- SHA256:
- 5ca385c2211cb15494d56e21f0b40e4233edd4bc7245b3167ef4db07f635d771
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