Instructions to use ProbeX/Model-J__ResNet__model_idx_0320 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_0320 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_0320") 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_0320") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0320") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8edda3d3682d0427af20a8da1d11288f76185d09c85b40bc7a3cafd3e5b08409
- Size of remote file:
- 5.37 kB
- SHA256:
- bf5d2171c554ca1f82f2b67ef398678b6080f5adcc69163b19587126408155e9
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