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## Motivation Support SOTA real-time semantic segmentation method in [Paper with code](https://paperswithcode.com/task/real-time-semantic-segmentation) Paper: https://arxiv.org/pdf/2206.02066.pdf Official repo: https://github.com/XuJiacong/PIDNet ## Current results **Cityscapes** |Model|Ref mIoU|mIoU (ours)| |---|---|---| |PIDNet-S|78.8|78.74| |PIDNet-M|79.9|80.22| |PIDNet-L|80.9|80.89| ## TODO - [x] Support inference with official weights - [x] Support training on Cityscapes - [x] Update docstring - [x] Add unit test
19 lines
811 B
Python
19 lines
811 B
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from .accuracy import Accuracy, accuracy
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from .boundary_loss import BoundaryLoss
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from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
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cross_entropy, mask_cross_entropy)
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from .dice_loss import DiceLoss
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from .focal_loss import FocalLoss
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from .lovasz_loss import LovaszLoss
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from .ohem_cross_entropy_loss import OhemCrossEntropy
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from .tversky_loss import TverskyLoss
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from .utils import reduce_loss, weight_reduce_loss, weighted_loss
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__all__ = [
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'accuracy', 'Accuracy', 'cross_entropy', 'binary_cross_entropy',
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'mask_cross_entropy', 'CrossEntropyLoss', 'reduce_loss',
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'weight_reduce_loss', 'weighted_loss', 'LovaszLoss', 'DiceLoss',
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'FocalLoss', 'TverskyLoss', 'OhemCrossEntropy', 'BoundaryLoss'
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]
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