Instructions to use ProbeX/Model-J__ResNet__model_idx_0711 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_0711 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_0711") 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_0711") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0711") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f7693e61a592dfefbc1e7423ab576d1cf25d41f51a06de0ab2b2ff3162026216
- Size of remote file:
- 5.37 kB
- SHA256:
- 976b110ae602889a6349612fa14f7872dbaf4f2bdfe199eb9193d15650201e35
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