Instructions to use Hasano20/SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hasano20/SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Hasano20/SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Medium")# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("Hasano20/SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Medium") model = SegformerForSemanticSegmentation.from_pretrained("Hasano20/SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Medium") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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model-index:
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- name: SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Medium
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Mean Iou: 0.9793
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- Mean Accuracy: 0.9903
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- Overall Accuracy: 0.9947
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- Transformers 4.41.2
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- Pytorch 2.0.1+cu117
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- Datasets 2.19.2
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- Tokenizers 0.19.1
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model-index:
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- name: SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Medium
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results: []
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pipeline_tag: image-segmentation
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train-Loss: 0.0088
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- Val-Loss: 0.0134
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- Mean Iou: 0.9793
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- Mean Accuracy: 0.9903
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- Overall Accuracy: 0.9947
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- Transformers 4.41.2
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- Pytorch 2.0.1+cu117
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- Datasets 2.19.2
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- Tokenizers 0.19.1
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