Instructions to use ProbeX/Model-J__ResNet__model_idx_0158 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_0158 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_0158") 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_0158") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0158") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee8658ba4befa024b65bfcf62d0fe19eef3c3f0f76ee83d893daeccd4d0a7895
- Size of remote file:
- 5.37 kB
- SHA256:
- 917e879f07ed1f770882804ec23fd89e7adfc60282cba6465f69a5bf2cd7d7ee
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