Instructions to use ProbeX/Model-J__ResNet__model_idx_0191 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_0191 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_0191") 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_0191") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0191") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9409cddf4c7403ed9eddf4964d47022f7a6ab7cf2086db3d8a2985f25181b04
- Size of remote file:
- 5.37 kB
- SHA256:
- 5e7003fc46afe992f1e0d47c2c2167cb387c1e0a6c15ee4647ba434c12ef11e4
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