Instructions to use ProbeX/Model-J__ResNet__model_idx_0891 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_0891 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_0891") 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_0891") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0891") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 321a4014295834f947eacdad34cff45099c8339235d80bba9e6550bb39c8b77e
- Size of remote file:
- 5.37 kB
- SHA256:
- f92e69ac6d3ff26cc69ab4bbecbdec6d32165c4763b0fa403704adc8d5c7cb41
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