Instructions to use ProbeX/Model-J__ResNet__model_idx_0315 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_0315 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_0315") 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_0315") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0315", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 388f9b60f09da8f0ddd1b5a46c50307cbb33ad866edd374f0c2c5e7db6a329b8
- Size of remote file:
- 5.37 kB
- SHA256:
- ec82fc11ea30a5fe25c2605cb2c7695b127ef33a53ea91d6624259c7707ac856
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