Instructions to use ProbeX/Model-J__ResNet__model_idx_0122 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_0122 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_0122") 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_0122") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0122") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 64f5fe6352e5bfb028b95e0f6e160d3ec42555e8e43c46c1074c79ad6e592474
- Size of remote file:
- 5.37 kB
- SHA256:
- 87124ed11107ed110beb5b344f598ead851d7ec103fa040b9e3f4d83ed37c13b
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