Instructions to use ProbeX/Model-J__ResNet__model_idx_0092 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_0092 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_0092") 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_0092") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0092") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ddafa316aa6e76fa2a89c00ba205438d2c5c2cc2244065a9ba86a942d31ef4cc
- Size of remote file:
- 5.37 kB
- SHA256:
- cd06be021eaf8fc6c309c82deda33f0f322da750a6566fca949c88a243c24c27
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