Instructions to use ProbeX/Model-J__ResNet__model_idx_0233 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_0233 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_0233") 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_0233") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0233", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a1c09cd75fd93f39b8098af66e9ca1b18a9466a1813cf1d7afed17874e85c9e9
- Size of remote file:
- 5.37 kB
- SHA256:
- 99864183417b16817d434a9b6c3e2575962d2ba4730c4e4e9bf0c76cc2dc83f3
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