Instructions to use ProbeX/Model-J__ResNet__model_idx_0957 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_0957 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_0957") 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_0957") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0957", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e6f61968b0ac11e93df9ef4629caa5d52b55304eaf06cff11381d04a281b3708
- Size of remote file:
- 171 MB
- SHA256:
- cfb308eaef03365fdce7cf32cd908799571b43568812bb4f9c7d5115fd0d6e08
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