Instructions to use ProbeX/Model-J__ResNet__model_idx_0780 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_0780 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_0780") 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_0780") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0780", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae48c2446c8f35054a78e6d98f6539cc507e60cc2e66d901eae7569c7619f291
- Size of remote file:
- 5.37 kB
- SHA256:
- abb1b27563e2c40812920fb536eb4fa01fc77818c1a6399b250eb007ca157431
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