Instructions to use ProbeX/Model-J__ResNet__model_idx_0359 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_0359 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_0359") 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_0359") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0359", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f1f13bef5276dbaa8d9daca7746e4d160ba84ec1aa77dc2710b7f7ede067b511
- Size of remote file:
- 5.37 kB
- SHA256:
- 3ad9c82c84d972f62f5fc2b36a8018dd5ea09337ce846a016c30794cc638ecf9
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