Instructions to use ProbeX/Model-J__ResNet__model_idx_0451 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_0451 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_0451") 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_0451") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0451") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e812906bba002133965d4c35034b3c6ab88d10e6f6fc8bb1d3d7c82263680569
- Size of remote file:
- 5.37 kB
- SHA256:
- 23b460e3504b66e03bd115275b7e5f99e096a1f651965bb10a30832537aae2da
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