Instructions to use ProbeX/Model-J__ResNet__model_idx_0749 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_0749 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_0749") 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_0749") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0749", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9e77039bf271aa3cb517ec07b1b75c1d3358d150976b551ae39dc9f55c1ab405
- Size of remote file:
- 5.37 kB
- SHA256:
- 984abc30627e7711d2c450e78f956cd7cc4042b92829cbce04fb823b2ba1f810
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