Instructions to use ProbeX/Model-J__ResNet__model_idx_0682 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_0682 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_0682") 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_0682") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0682", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6cb877fcc3808c86acb0c23034705cb911d744a8a9aab40152a18a14c7448d32
- Size of remote file:
- 171 MB
- SHA256:
- 9c434110e1a0d02795fbb6a6276a6f5405e7138de93aa959514656f8ffd5660a
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