Instructions to use ProbeX/Model-J__ResNet__model_idx_0731 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_0731 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_0731") 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_0731") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0731") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be57232aeafa5e19d18d751b39af4686997d8eb6ef1861efa84a041162c57eee
- Size of remote file:
- 5.37 kB
- SHA256:
- 6786a04acb4d1f9819cedbab4ec0e2cd4e75169bc6f7fabb01d0137aaaa00784
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