Instructions to use ProbeX/Model-J__ResNet__model_idx_0995 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_0995 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_0995") 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_0995") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0995", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee452597713972d848052a031d3f3574ec2fccb2cf577d68c5a76f26fe436395
- Size of remote file:
- 5.37 kB
- SHA256:
- c0431b9d967d2857b0ff6384f7b85ad065814bd1df455da3dd3f2853a928f933
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