Instructions to use ProbeX/Model-J__ResNet__model_idx_0672 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_0672 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_0672") 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_0672") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0672", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3cc3f1c1c31677622a5c3a010446b17468ecba645237916c7c581e012f8ea4df
- Size of remote file:
- 5.37 kB
- SHA256:
- 2d7fd0c1cd5cce2afa15439df4be3fb28fd1bfd63cf20695ea6a6a93eba7d287
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