Instructions to use ProbeX/Model-J__ResNet__model_idx_0813 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_0813 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_0813") 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_0813") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0813") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8fc484a48219018ba097e24195de35d6f21a853704d52433762e7931cfee9e62
- Size of remote file:
- 5.37 kB
- SHA256:
- 80646a578501101ef8e31c8b01135b0bf7ed7b5dbd049a9f59f8ed2571104c36
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