Instructions to use ProbeX/Model-J__ResNet__model_idx_0343 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_0343 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_0343") 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_0343") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0343") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24a7bc4970e773dfa9d81629b4d6e671b725178e99f5cd5730433fc40acd2990
- Size of remote file:
- 5.37 kB
- SHA256:
- d72e0c38ad1a6bea2fffae9a1642bf7989371ae96132c84371991704edfa1e3f
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