Instructions to use ProbeX/Model-J__ResNet__model_idx_0919 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_0919 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_0919") 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_0919") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0919", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a71f9ff24a1a5e7ce5616eba7f6dbcfcd6ca899c8bd55c552ce63e86ae340387
- Size of remote file:
- 5.37 kB
- SHA256:
- 13eed2399f8ca46802e3270c73cfa373a98abe909d2b553c61e1d41f99020299
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