Instructions to use ProbeX/Model-J__ResNet__model_idx_0130 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_0130 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_0130") 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_0130") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0130") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6a28fe1a824ee0b55334d187016e8233fb84e7ba4f9b3e6299af3b5a3ff1d0ec
- Size of remote file:
- 5.37 kB
- SHA256:
- f416cc78d87b66807b6da2f7fc95ef75327ce95e154fd106e0d304dc466c69bc
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