Instructions to use ProbeX/Model-J__ResNet__model_idx_0221 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_0221 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_0221") 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_0221") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0221") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cc42c402afb79dad01ad90ed18be2b393ff072654ce8408e43f1659ebea8fd79
- Size of remote file:
- 5.37 kB
- SHA256:
- dd05918592163275b40c1b6e7bf55c22cf332817c5d9626210773613c8cd07b6
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