Instructions to use ProbeX/Model-J__ResNet__model_idx_0905 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_0905 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_0905") 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_0905") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0905") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e553e86a6e137806a00ebda4be8590e8087898b88d76f45127d74f2c11717d0
- Size of remote file:
- 5.37 kB
- SHA256:
- fe1fc5fed7a837cd5a909aae16becc8e8c586423b9d294241678cd0f102ccb82
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