Instructions to use ProbeX/Model-J__ResNet__model_idx_0613 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_0613 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_0613") 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_0613") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0613", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 35011b175806e69bd64b90373e3810b1a60121274f1616cbf656568bef265aa9
- Size of remote file:
- 5.37 kB
- SHA256:
- a027e6cd846bfa9057609f3f45dc15a7bf293c0a19feef54fe7a2e4bb4168b8f
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