Instructions to use ProbeX/Model-J__ResNet__model_idx_0620 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_0620 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_0620") 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_0620") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0620") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3cf8f69bd295ef7d8dcfc10abda7fb65ec719e919a3f6193f4b4abc3fc5804bb
- Size of remote file:
- 171 MB
- SHA256:
- 4ae9b8a250cb0832abb232cae2cdd7ab1dbbccda087bc11b5df41ace7ffbda5f
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