Instructions to use ProbeX/Model-J__ResNet__model_idx_0313 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_0313 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_0313") 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_0313") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0313") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6cf36cb181000c1ace6a3f627af010942aec274bdcdddb919fc6fd8b255e5d7b
- Size of remote file:
- 171 MB
- SHA256:
- b61f64a80a4aadf1be611d25e15c44aac18e6ea1c478eceb6945aff826edcf83
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