Instructions to use ProbeX/Model-J__ResNet__model_idx_0348 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_0348 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_0348") 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_0348") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0348", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd409426ac4e9fa1323064bd860dbc0136c8b7b566825ad6fe26f2bf055edf3d
- Size of remote file:
- 171 MB
- SHA256:
- 0d3e34e22b2c2561062308b4f21d477aebdbb90ede949688a8fd796a91091165
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