Instructions to use ProbeX/Model-J__ResNet__model_idx_0042 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_0042 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_0042") 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_0042") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0042") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6bacbfc57e47687b57551a7dbebd02705262fe376382d29574275341b6ac95e
- Size of remote file:
- 5.37 kB
- SHA256:
- d44eeb194c0f3558e5b66f77fe13f535187acb44d956b5eda507529414623cc0
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