Instructions to use ProbeX/Model-J__ResNet__model_idx_0241 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_0241 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_0241") 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_0241") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0241") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf1375848483e4b216198c8483198d3ee460c7ac9483e145db16e675da46b519
- Size of remote file:
- 5.37 kB
- SHA256:
- ff7f799f10303ccee96ba8f319d558bf92f84fc31785baa3b932cd3deb983e3d
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