Instructions to use ProbeX/Model-J__ResNet__model_idx_0829 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_0829 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_0829") 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_0829") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0829") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9af081a060f64ca035dd1b4662baa14ea9c219672066e66a8666101c3fa53cae
- Size of remote file:
- 5.37 kB
- SHA256:
- d5fa1c36a328643d918f8ba9fffd8e5f57227cf43f9bc340acae1bacdfc86516
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