Instructions to use ProbeX/Model-J__ResNet__model_idx_0239 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_0239 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_0239") 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_0239") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0239") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a53991d601a6aa982863940c32ced3b0303fa4e8a6d32ddfb721187f0c981c07
- Size of remote file:
- 5.37 kB
- SHA256:
- 78e33348d0cd5ca368b9bf5cdce5ff94fb396eee7a6ea59f2ecf054d24692eee
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