Instructions to use ProbeX/Model-J__ResNet__model_idx_0182 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_0182 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_0182") 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_0182") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0182") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d9b341d4d08ba99a8d0526a85afcc1e3a1b670ffd229164bd890a15002f5adc
- Size of remote file:
- 5.37 kB
- SHA256:
- 3d38c2ddcb110cbd01a96e0d2149474233dd03dcee119daa84e3ea983de52b94
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