Instructions to use ProbeX/Model-J__ResNet__model_idx_0698 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_0698 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_0698") 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_0698") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0698", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1ede58d41d1b6ce1d8aad10ebca8ecb9c8a66e53d3f22c2840e59cabe4c57791
- Size of remote file:
- 171 MB
- SHA256:
- 436de23397c099ed90d85652dad586f6ffda6ca981b0a9379363c1f6291c34df
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