Instructions to use ProbeX/Model-J__ResNet__model_idx_0044 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_0044 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_0044") 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_0044") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0044", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 14c0af3e5f17ff17537fb78a656f074a0754fca7e1598c72fc29fbe24e9a66a9
- Size of remote file:
- 171 MB
- SHA256:
- df66f5d7a68b26692fea948654fe5500e4f2dee2bf39337d336e0c662ccd558c
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