Instructions to use ProbeX/Model-J__ResNet__model_idx_0225 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_0225 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_0225") 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_0225") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0225") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90acbce6852dc7a9743dace8df2b65e722f4c39deeac7c2b4b047560c9e08f79
- Size of remote file:
- 5.37 kB
- SHA256:
- 7270822af588df56a14b2665a72fcbb1fe108b4c36d704e5f49c5a736e85eb35
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