Instructions to use ProbeX/Model-J__ResNet__model_idx_0748 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_0748 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_0748") 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_0748") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0748", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bc134114237ac4a17e18b1d406273136a1b40243ac03834afc58e34b7b84e1f
- Size of remote file:
- 171 MB
- SHA256:
- 098b76634405c1cb26dc45ece6997481479550703e192f4ae3d274c2f7e60b72
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