Instructions to use ProbeX/Model-J__ResNet__model_idx_0234 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_0234 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_0234") 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_0234") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0234") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b1bf82a8ec1bf4e2cfe1159bff699de903ca646099aea2ef036b919931d00fe
- Size of remote file:
- 5.37 kB
- SHA256:
- 6a35b97eae26426639b5bd543c543f26407f7a0d9e673883d3a3b897501c3497
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