Instructions to use ProbeX/Model-J__ResNet__model_idx_0882 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_0882 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_0882") 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_0882") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0882", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d039e02b0cf273cae1db41185729ff273ed56ed75f6989078c2d8cd7eaa8cf80
- Size of remote file:
- 171 MB
- SHA256:
- 428b5d6d6ee83811453b13c94a050876ed7e21cb66b680e53fdb357671939785
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