Instructions to use ProbeX/Model-J__ResNet__model_idx_0792 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_0792 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_0792") 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_0792") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0792", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a53aaf011da962e76a7bc01973d0706e6decf9e684f37664eb11d1bb1f5e380f
- Size of remote file:
- 5.37 kB
- SHA256:
- d20137d89f457cd0fbd1d1405495ae3cd7c62425410ff4332d5a701f8d3b4a51
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