Instructions to use Transformers123/testing_303 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Transformers123/testing_303 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Transformers123/testing_303")# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("Transformers123/testing_303") model = SegformerForSemanticSegmentation.from_pretrained("Transformers123/testing_303") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): c7b8a61
Model save
Browse files
README.md
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tags:
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- retouch
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- image-segmentation
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- generated_from_trainer
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model-index:
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- name: testing_303
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# testing_303
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This model is a fine-tuned version of [
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It achieves the following results on the evaluation set:
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- Loss: 0.0228
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- Mean Iou: 1.0
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license: other
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base_model: nvidia/mit-b2
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tags:
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- generated_from_trainer
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model-index:
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- name: testing_303
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# testing_303
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This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0228
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- Mean Iou: 1.0
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