Instructions to use ProbeX/Model-J__ResNet__model_idx_0458 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_0458 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_0458") 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_0458") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0458", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 472b01ef56700258100588713ff6dcc3f1732688d31c49a5c6f61ed3b818850d
- Size of remote file:
- 5.37 kB
- SHA256:
- b910cb5b6a376827a61e676ec218805b8145d599ddf100b4fefcb54bf2007071
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