Instructions to use ProbeX/Model-J__ResNet__model_idx_0129 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_0129 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_0129") 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_0129") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0129") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba21ce56f6e2c51fe39ff4cf6172c53e145f30ab8f778e1e42e73833d929ee92
- Size of remote file:
- 5.37 kB
- SHA256:
- 331f1c0d1263be7a8c49bad51578c1944991fc416df63908825d41d458260ad4
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