Instructions to use ProbeX/Model-J__ResNet__model_idx_0753 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_0753 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_0753") 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_0753") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0753") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dfcc24b24f0e06a4d68890f24b00859233f56c3d43190b9ba51dab4167af954a
- Size of remote file:
- 5.37 kB
- SHA256:
- 1afc8eb76549b403f21de2a489e4f8cb431e70f7f94004d984e8078228b1845a
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