Instructions to use ProbeX/Model-J__ResNet__model_idx_0363 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_0363 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_0363") 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_0363") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0363") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cef631095d4664a5880e58ce5791c8aa3de63bd82c073efd62e0f4aa1abb7fd8
- Size of remote file:
- 5.37 kB
- SHA256:
- 1b46d117e09e0625e8355831530ea82d450de23912d3c01811370a5b0bdc8096
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