Instructions to use ProbeX/Model-J__ResNet__model_idx_0060 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_0060 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_0060") 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_0060") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0060") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 92f4df2f34f416481ccf7764daa781505634192ad49c67824c3a98c9cd102969
- Size of remote file:
- 5.37 kB
- SHA256:
- 46261e50b4f65a725d514e7c7454e6c645213757471b9d97549e733f70a31ced
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