Instructions to use ProbeX/Model-J__ResNet__model_idx_0485 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_0485 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_0485") 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_0485") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0485") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be1d6b96c7c88f3d27133789a51e72d71caac664bdc7ced1ad38113df52913fb
- Size of remote file:
- 5.37 kB
- SHA256:
- 8b244d8dfafff3d8829a04a067b9272c44e4180ebcea26d4b190fa32ab72d047
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