Instructions to use ProbeX/Model-J__ResNet__model_idx_0739 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_0739 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_0739") 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_0739") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0739", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e5fabbf13f4414420b8f7f51cffdadcb671668bd469d48d021cbff169c0baa33
- Size of remote file:
- 171 MB
- SHA256:
- a22dfdff33923618c54dda96ef4c5ce4cef9e2673f1485f8a8f84cb83be6f404
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