Instructions to use ProbeX/Model-J__ResNet__model_idx_0776 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_0776 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_0776") 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_0776") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0776") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e359ddc3f68714c16bbeb7f8f57fe908f1997b9024352f731f4e56d61fd72fa8
- Size of remote file:
- 5.37 kB
- SHA256:
- 636d78cd83d50d73444cb038313a6939988e975928abe773af5dc3eed3465b68
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