Instructions to use ProbeX/Model-J__ResNet__model_idx_0979 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_0979 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_0979") 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_0979") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0979", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a0aaafa817681b97ce98c01a1169ecec57ba0713431504fd06730e2342f4e6dc
- Size of remote file:
- 171 MB
- SHA256:
- f7f2009df35c7df71c5b3963c503b3d05a0f5ca110bda62c0e868a2642186ad0
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