Instructions to use ProbeX/Model-J__ResNet__model_idx_0424 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_0424 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_0424") 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_0424") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0424") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57e0870601e9d1ead6e731d6f01ba32ac973f07625b187e815a48c22d9fd1a56
- Size of remote file:
- 5.37 kB
- SHA256:
- 0f51a70b9c52f2644c979e6fa10030ff98d3f6b2fbd1c59c77b5b2b1a89f7113
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