Instructions to use ProbeX/Model-J__ResNet__model_idx_0357 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_0357 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_0357") 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_0357") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0357") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3558af0d004200ecca37ea3fda5752aeb8b5f5adf2487fe71e2b5273c37bb5f5
- Size of remote file:
- 5.37 kB
- SHA256:
- 1774ba317e3cc88d609c9ebd6b8342388d3084b70033191577924db97ef4e814
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