Instructions to use ProbeX/Model-J__ResNet__model_idx_0636 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_0636 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_0636") 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_0636") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0636", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 411c57d2104eea953659afe12ccc43810d4598ccf9c490bd52717bfd498a8e17
- Size of remote file:
- 171 MB
- SHA256:
- 19babb3ec17196741a37f6640b43c32af20d64d3049c54b03d02aecd7632f6ac
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