Instructions to use ProbeX/Model-J__ResNet__model_idx_0834 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_0834 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_0834") 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_0834") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0834") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6de1a62e9b44ec36e3e5d8a9cd5c0eb3c0b92955f57527796c452332b1ac64e8
- Size of remote file:
- 5.37 kB
- SHA256:
- f0e939f04e67dd554837e1f432fc853e04681121926440d1aeff059d0b7239c0
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