Instructions to use ProbeX/Model-J__ResNet__model_idx_0378 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_0378 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_0378") 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_0378") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0378", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 075dbd6ce46496f570b94f0754072c507f8c3ba0a26f9c53d52249fb2bcfc4d9
- Size of remote file:
- 5.37 kB
- SHA256:
- b751489122801576d940bd7ed9b4e398f95a53ded31e7d399480802e93663c1a
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