Instructions to use ProbeX/Model-J__ResNet__model_idx_0616 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_0616 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_0616") 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_0616") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0616", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 04f01b970353fac1f5d6ac56361a008a036522c9ed62e3ab21ec18a9c2b48e0a
- Size of remote file:
- 171 MB
- SHA256:
- e68f7c4244b4f360a6875dc593a18b7f4c4b221a987f3c5afd69af0c92554532
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