Instructions to use ProbeX/Model-J__ResNet__model_idx_0420 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_0420 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_0420") 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_0420") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0420") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 50b6facdfe87c7f5a178a8515ddc90b6feb149e341724fd09da79958b2c1572b
- Size of remote file:
- 171 MB
- SHA256:
- 1249713e60f8f8b74636033d598aa4ae0a53c830d51eddcb65f5c87393af8ae0
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