Instructions to use ProbeX/Model-J__ResNet__model_idx_0932 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_0932 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_0932") 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_0932") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0932", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4f54caa8ddc643c39c11210b8b570929c507ef911358e1bc0e5e563b2219a02
- Size of remote file:
- 5.37 kB
- SHA256:
- ddfc2594fc6c22c3e518978febf1f38cc251d418f07ddd527aa9f522887c2d58
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