Instructions to use ProbeX/Model-J__ResNet__model_idx_0073 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_0073 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_0073") 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_0073") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0073") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9d9be985f7012a9ceab334b4394916eed75006771a25d4b583a3cdff8c0579f
- Size of remote file:
- 5.37 kB
- SHA256:
- 5e72b95ef25d1811fb9c5289051a43601d27a56abc98f990f8aa582fc4fcec89
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