Instructions to use ProbeX/Model-J__ResNet__model_idx_0483 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_0483 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_0483") 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_0483") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0483") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 15e87b6a33c77b39431fa7b55d22fe86e1ed79a6c3403215ad6bd5c915dd3ac3
- Size of remote file:
- 5.37 kB
- SHA256:
- 55049509b655f2dfa716150b9c8df05480cb40758dcfeff29c083fb363b6556d
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