Instructions to use ProbeX/Model-J__ResNet__model_idx_0181 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_0181 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_0181") 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_0181") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0181") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 825fb58bfa4543510aaa79e699cb1f3832419a238b6b5d9ff989bd9c25dc5ed4
- Size of remote file:
- 5.37 kB
- SHA256:
- 69ff594c7286f3d58748bb9c54bf1e9b7dd3205f2c70c1f8869bdd108ad110a8
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