Instructions to use ProbeX/Model-J__ResNet__model_idx_0851 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_0851 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_0851") 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_0851") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0851") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3be0b2de3af9926b7e9168bfabe6ae7ae8bf2b5bb59b888e14f386d2b3ef2e0
- Size of remote file:
- 5.37 kB
- SHA256:
- 23c50e924f0381e70f6ce9bb30207dd4f687b366208ab8ebb9b256d90bd21a62
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