Instructions to use ProbeX/Model-J__ResNet__model_idx_0644 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_0644 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_0644") 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_0644") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0644") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed3a18f6338b9cf409ef96b926de6add211c9d51417e69c613fcff2466433e57
- Size of remote file:
- 5.37 kB
- SHA256:
- d063735291e2ab87f35cac2564e998fee840502494ffeb2b961d72f490ba1c47
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