Instructions to use ProbeX/Model-J__ResNet__model_idx_0718 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_0718 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_0718") 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_0718") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0718", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f60045652169a7fd3ec6c50a3a5a0ec8b644be6584b1289e186c364be2fce4ae
- Size of remote file:
- 171 MB
- SHA256:
- aab493d45a258d0d37d4ebac2054d180d4eb66a11ccab91bfea17e7b90425c80
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