Instructions to use ProbeX/Model-J__ResNet__model_idx_0889 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_0889 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_0889") 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_0889") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0889", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a61fd0e2d0e14858fb9abb13fce4cd90d41e8e3a03004b7449028f18c991b802
- Size of remote file:
- 171 MB
- SHA256:
- fde87172ae88feb9a79a8f4f808f96afc01f8376cccde14315cbf7283877680b
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