Instructions to use ProbeX/Model-J__ResNet__model_idx_0710 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_0710 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_0710") 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_0710") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0710", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6051dfe04989d4603c79905db29a55ee1af24c187084a89fb87634bbc91f6ad
- Size of remote file:
- 171 MB
- SHA256:
- 1781a0462af3fe72d189829843a04f5855d6a713871c066980b6f97ed3422962
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