Instructions to use ProbeX/Model-J__ResNet__model_idx_0169 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_0169 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_0169") 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_0169") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0169") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6aaa914dadebd2d3948e361243dc062d6afeb8712f53a370dfa29ff87a8e93af
- Size of remote file:
- 5.37 kB
- SHA256:
- 4418ff77a87d3455ee7b3752f49838034b4fd67f31987d8e46a8998a7c4fe986
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