Instructions to use ProbeX/Model-J__ResNet__model_idx_0251 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_0251 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_0251") 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_0251") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0251") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de7812614351a4ce5e731e5a3cd2b7b9fec5fef377117fc84693aa09720ab630
- Size of remote file:
- 5.37 kB
- SHA256:
- 2c83cd0417f832f48ecf5ce1de165673a72b312cd8ebc1d37bbe29113eb69a98
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