Instructions to use ProbeX/Model-J__ResNet__model_idx_0637 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_0637 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_0637") 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_0637") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0637", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 92f7bb027f1ec6b7d2db96fee6871a50b1410fcd1bf00b6c7316b5ea7f3eddc5
- Size of remote file:
- 5.37 kB
- SHA256:
- 794c3fca4019574de5cfa9a2e9f13d1b062d4be3d6197da5a2a505077c6f1a7a
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