Instructions to use ProbeX/Model-J__ResNet__model_idx_0139 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_0139 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_0139") 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_0139") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0139", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 260fce3547a72914aa6a8c6acd55fe8243344f228afac395314b597be1ee1377
- Size of remote file:
- 5.37 kB
- SHA256:
- 99730cd882c687f85d971a1718dcd71411971ace68734830015af462d1d117d3
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