Instructions to use ProbeX/Model-J__ResNet__model_idx_0156 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_0156 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_0156") 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_0156") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0156") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fceb602ce67362b73f5377358f124bdb9814fa7458446a6b46a81766062c4b64
- Size of remote file:
- 171 MB
- SHA256:
- 5e2eb99d93f7f1e8e8403e65334706aa0442538452dce3047a84967a0816031d
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