Instructions to use ProbeX/Model-J__ResNet__model_idx_0936 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_0936 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_0936") 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_0936") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0936") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 888de15cec5d211ff9c955aff99350ec656d9de8bd40d7ad7654dbfa3cea0981
- Size of remote file:
- 5.37 kB
- SHA256:
- dc55d10a410d5c7062286fcf48535b1d0cd38b1457bf9bedb4c066a100d0fa28
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