Instructions to use ProbeX/Model-J__ResNet__model_idx_0279 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_0279 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_0279") 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_0279") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0279") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f4e27ed5428a25aa874d4723b5b78e906c19fb76404b7115cd521e92a01445a1
- Size of remote file:
- 5.37 kB
- SHA256:
- ee503ecbe1ba895ea3a9ac2c3564bf6896f3d0cc2deb5cb7d1ff5772ec5d00b6
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