Instructions to use ProbeX/Model-J__ResNet__model_idx_0840 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_0840 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_0840") 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_0840") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0840") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c9e6bb4cb09b4fa7cadc9b74a81ae87e57e10263f89d90f39348eef5a8926292
- Size of remote file:
- 5.37 kB
- SHA256:
- 99e304796930a85d4b90a96760704a2a3387da4959cbf1ec57f2f6819bc70fa1
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