Instructions to use ProbeX/Model-J__ResNet__model_idx_0875 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_0875 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_0875") 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_0875") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0875", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59e0120c98e8b7d1ca3f8ed6b0544175b21c92b82920f0bedf72575136b6b228
- Size of remote file:
- 5.37 kB
- SHA256:
- ea376c106e2f5c35995830dbea264ab2d6a4aeba7ba8c536446390385427eb31
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