Instructions to use ProbeX/Model-J__ResNet__model_idx_0785 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_0785 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_0785") 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_0785") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0785") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 61bb0cf3c0da7aaf1303a08e2da53eb1b03819ecfc1182671664175f304c6e4b
- Size of remote file:
- 5.37 kB
- SHA256:
- b2c4e4b61a32866cce191751783a21d44ca682a34412dca450906ba327ad8c5c
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