Instructions to use ProbeX/Model-J__ResNet__model_idx_0237 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_0237 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_0237") 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_0237") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0237") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff6e3eff0bd88a0e2d9db03bbd4f8494f551a965002dd41f085207e04448d42b
- Size of remote file:
- 5.37 kB
- SHA256:
- 9f66824403fc7d15fe6134709a35af50f9550e0ca757e34d28a30a4ef5e91044
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