Instructions to use ProbeX/Model-J__ResNet__model_idx_0626 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_0626 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_0626") 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_0626") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0626") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 16fcec8c6c76dfad58c5163eee6383c9f66a707430c5692623d4e21707d18b04
- Size of remote file:
- 5.37 kB
- SHA256:
- fd6100af5d4dfc7baf61f565353c5baccbd88d2f002c80e62fe914572a544816
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