Instructions to use ProbeX/Model-J__ResNet__model_idx_0812 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_0812 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_0812") 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_0812") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0812") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 093168e59cba9b5d6e07603ab0ce1a5040fbec7323ac5244b106f02ca146d692
- Size of remote file:
- 5.37 kB
- SHA256:
- 65b7ddb5f51d97a61ecfa50bdca7ebd0d714ecfb2b1fadcb82cae152b7a15820
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