Instructions to use ProbeX/Model-J__ResNet__model_idx_0927 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_0927 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_0927") 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_0927") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0927") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9eef9bcff4318e4dca9462ec0d0ffc76d496cef17cab660389f02a9db9533a9f
- Size of remote file:
- 5.37 kB
- SHA256:
- 0a66e8d40185dd7b8ad936b33f4e158861e3b89d8f8988eb92c7b260d227ff87
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