Instructions to use ProbeX/Model-J__ResNet__model_idx_0027 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_0027 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_0027") 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_0027") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0027") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b7271fb9d57817a8ffe35ff2aef731a324c26fa25b61d133c251854ad266f6f
- Size of remote file:
- 5.37 kB
- SHA256:
- 5354220859546a4333a3f19fd9138d63434ba935e73d4b28ea7f461a5a2785b4
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