Instructions to use ProbeX/Model-J__ResNet__model_idx_0426 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_0426 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_0426") 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_0426") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0426") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de657792cb767ce0a8717131823488309ca7e10377e2ae41affa0e4b50b3a08b
- Size of remote file:
- 5.37 kB
- SHA256:
- cf8b59fa5500039c337b9542275f430289eac3db25aea33d7d8babef87ce0803
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