Instructions to use ProbeX/Model-J__ResNet__model_idx_0089 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_0089 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_0089") 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_0089") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0089", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98584ecf3ff6f0379003bff49f7a78e1aa4e24c85869c1afea02e9d25dee749d
- Size of remote file:
- 5.37 kB
- SHA256:
- c8de434530a125b019a7eb1468d6b0d009446a3bf8067fdecf6405a850acff43
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