Instructions to use ProbeX/Model-J__ResNet__model_idx_0972 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_0972 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_0972") 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_0972") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0972") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea27a4c9a79ad97b4f6d3eacb4532f8b33bc75ee72c5e7020b05f9d411584cfc
- Size of remote file:
- 5.37 kB
- SHA256:
- 524dfa2ccc83071ab3de842d090703fa3d13a5f0d9594699280aa23b07e9d6c7
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