Instructions to use ProbeX/Model-J__ResNet__model_idx_0398 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_0398 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_0398") 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_0398") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0398") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0ccc15c7f580afdc3b029efe078303f60f56cdf822a8e79bfe4e3fa586eac610
- Size of remote file:
- 5.37 kB
- SHA256:
- 9ff340cebd1e81c49150f59eb3994fdabc6877bec75ae5faaf318b5e9624ed91
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