Instructions to use ProbeX/Model-J__ResNet__model_idx_0491 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_0491 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_0491") 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_0491") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0491", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d4f8bc0b73b083c38784803bc46a304689873ee8e106ee389572f4fc00e3fa7
- Size of remote file:
- 171 MB
- SHA256:
- c3f27d72cee799f82d62e4e98850d2d5821c966bd74123a31de108c678d755fa
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