Instructions to use ProbeX/Model-J__ResNet__model_idx_0421 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_0421 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_0421") 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_0421") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0421") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c085f5a81686ee8dc381e9f057b6d603d926db27f399f027990f0c0fb5654f6
- Size of remote file:
- 5.37 kB
- SHA256:
- 4a25892a536a018f3836a8d73bcdcee7d9678e68fec71ddb895ddc931d8a8861
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