Instructions to use ProbeX/Model-J__ResNet__model_idx_0638 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_0638 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_0638") 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_0638") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0638", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3c567d377c061b9d76d5c3d03bce7ec0899c71dcb7e0f6a5b527d71d2749344
- Size of remote file:
- 5.37 kB
- SHA256:
- 66945c5f57fafa1c7a28a1d841c0883b3c4786518331c6acb997fe8f512bbde5
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