Instructions to use ProbeX/Model-J__ResNet__model_idx_0155 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_0155 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_0155") 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_0155") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0155") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c7ac6e4dfe31c8ad9622042b4dca5c887dcbdd5a7984c41a2d66b29a942b502
- Size of remote file:
- 5.37 kB
- SHA256:
- 9279780958923393cb8839a3839b0a5f79e526e85f5708e86b309ce8976d6fbb
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