Instructions to use ProbeX/Model-J__ResNet__model_idx_0001 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_0001 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_0001") 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_0001") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0001") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 794b87106ade81c26c7b8d30337afe89c71253c61e1e1182384ad4d51067609f
- Size of remote file:
- 5.37 kB
- SHA256:
- 5540e82adae990076fa62f833e4ed906536ae21533329635b5c5772176a1b637
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