Instructions to use ProbeX/Model-J__ResNet__model_idx_0422 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_0422 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_0422") 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_0422") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0422") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be3ddf7c30317fd1a438cee3bdcef155735d80856ba91011f31de861cc9628fc
- Size of remote file:
- 5.37 kB
- SHA256:
- 16e0a513b03ab954dacc5e2ea72352c65a421fb7d1b70f1728348a80268774a6
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