Instructions to use ProbeX/Model-J__ResNet__model_idx_0710 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_0710 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_0710") 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_0710") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0710", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c01bdeaf4ceabbdb3a17da222197e994a77dd586b782ba1d586be7b3ed21cfde
- Size of remote file:
- 5.37 kB
- SHA256:
- 8850d6b261e3e7e12575ae36cdec9311385b73fa58b945fed76bf92eba4cbe34
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