Instructions to use ProbeX/Model-J__ResNet__model_idx_0401 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_0401 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_0401") 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_0401") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0401", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 306a82c5b83ad5155f9854b66bf3d32eedb7979eeebf40cbf94aa010ebb358be
- Size of remote file:
- 171 MB
- SHA256:
- b2a95d314328f292a458eb0d75914f37c861fd15164b7a716123824019caa741
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