Instructions to use ProbeX/Model-J__ResNet__model_idx_0116 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_0116 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_0116") 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_0116") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0116") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a8ccb653599b946680f5387c02a9343296927975e04588963374bfe9487eac98
- Size of remote file:
- 5.37 kB
- SHA256:
- d85afbfd7913f9817cc08170fdb6af9a5e046a9edf269ebba43c70cdfe7d642b
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