Instructions to use ProbeX/Model-J__ResNet__model_idx_0214 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_0214 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_0214") 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_0214") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0214", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1ca1d17a7db3f4fe09659e9b7010adba2ecbb2eed6b318745cf2c39b899dbb2
- Size of remote file:
- 5.37 kB
- SHA256:
- 9ac9bf15fec495884b139b1f82982867bcb751eb8990334a7c13bf250d2d8e03
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