Instructions to use mccaly/test2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mccaly/test2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="mccaly/test2")# Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("mccaly/test2") model = UperNetForSemanticSegmentation.from_pretrained("mccaly/test2") - Notebooks
- Google Colab
- Kaggle
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README.md
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license: apache-2.0
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datasets:
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- EduardoPacheco/FoodSeg103
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# A Large-Scale Benchmark for Food Image Segmentation
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license: apache-2.0
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datasets:
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- EduardoPacheco/FoodSeg103
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library_name: transformers
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---
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# A Large-Scale Benchmark for Food Image Segmentation
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