Instructions to use ProbeX/Model-J__ResNet__model_idx_0949 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_0949 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_0949") 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_0949") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0949") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee04ccaafd50a63be70c998394e849a3a4451e8705177a2ba0196c1fcf875d0d
- Size of remote file:
- 5.37 kB
- SHA256:
- c3fe32211238a5f08cf1960c8d14ab6b95cf728bbdde8a702f431039477cb353
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