Instructions to use ProbeX/Model-J__ResNet__model_idx_0844 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_0844 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_0844") 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_0844") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0844") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fd8a4a5cd1689bad546510f43a262a0bcc83510c9461d43160bba8de256de20c
- Size of remote file:
- 5.37 kB
- SHA256:
- 59097e1b52e32e97185a84ca64d1947978fb917ded657d286af7f657b20e941d
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