Instructions to use ProbeX/Model-J__ResNet__model_idx_0190 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_0190 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_0190") 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_0190") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0190") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b2600516afe080bdb9ff44194e61f60f5ba758467fc3d9125900b3cf9b677743
- Size of remote file:
- 5.37 kB
- SHA256:
- 95d7bfa4233aa0ad330c489197157d30ffc3b69e8c9e3504f783c5f71de35572
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