Instructions to use ProbeX/Model-J__ResNet__model_idx_0169 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_0169 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_0169") 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_0169") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0169", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a88ba6345f01bb064cdd0670f35ba7f5dd1bad70922b667fca4aee54afcdb82
- Size of remote file:
- 171 MB
- SHA256:
- 2c132e7058231678e574f2821d8592a3ac93c08747210c528c6d3bf17e0a0736
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