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Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers. ## Motivation Support DDRNet Paper: [Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes](https://arxiv.org/pdf/2101.06085) official Code: https://github.com/ydhongHIT/DDRNet There is already a PR https://github.com/open-mmlab/mmsegmentation/pull/1722 , but it has been inactive for a long time. ## Current Result ### Cityscapes #### inference with converted official weights | Method | Backbone | mIoU(official) | mIoU(converted weight) | | ------ | ------------- | -------------- | ---------------------- | | DDRNet | DDRNet23-slim | 77.8 | 77.84 | | DDRNet | DDRNet23 | 79.5 | 79.53 | #### training with converted pretrained backbone | Method | Backbone | Crop Size | Lr schd | Inf time(fps) | Device | mIoU | mIoU(ms+flip) | config | download | | ------ | ------------- | --------- | ------- | ------- | -------- | ----- | ------------- | ------------ | ------------ | | DDRNet | DDRNet23-slim | 1024x1024 | 120000 | 85.85 | RTX 8000 | 77.85 | 79.80 | [config](https://github.com/whu-pzhang/mmsegmentation/blob/ddrnet/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024.py) | model \| log | | DDRNet | DDRNet23 | 1024x1024 | 120000 | 33.41 | RTX 8000 | 79.53 | 80.98 | [config](https://github.com/whu-pzhang/mmsegmentation/blob/ddrnet/configs/ddrnet/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024.py) | model \| log | The converted pretrained backbone weights download link: 1. [ddrnet23s_in1k_mmseg.pth](https://drive.google.com/file/d/1Ni4F1PMGGjuld-1S9fzDTmneLfpMuPTG/view?usp=sharing) 2. [ddrnet23_in1k_mmseg.pth](https://drive.google.com/file/d/11rsijC1xOWB6B0LgNQkAG-W6e1OdbCyJ/view?usp=sharing) ## To do - [x] support inference with converted official weights - [x] support training on cityscapes dataset --------- Co-authored-by: xiexinch <xiexinch@outlook.com>
35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from .beit import BEiT
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from .bisenetv1 import BiSeNetV1
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from .bisenetv2 import BiSeNetV2
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from .cgnet import CGNet
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from .ddrnet import DDRNet
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from .erfnet import ERFNet
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from .fast_scnn import FastSCNN
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from .hrnet import HRNet
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from .icnet import ICNet
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from .mae import MAE
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from .mit import MixVisionTransformer
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from .mobilenet_v2 import MobileNetV2
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from .mobilenet_v3 import MobileNetV3
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from .mscan import MSCAN
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from .pidnet import PIDNet
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from .resnest import ResNeSt
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from .resnet import ResNet, ResNetV1c, ResNetV1d
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from .resnext import ResNeXt
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from .stdc import STDCContextPathNet, STDCNet
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from .swin import SwinTransformer
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from .timm_backbone import TIMMBackbone
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from .twins import PCPVT, SVT
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from .unet import UNet
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from .vit import VisionTransformer
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__all__ = [
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'ResNet', 'ResNetV1c', 'ResNetV1d', 'ResNeXt', 'HRNet', 'FastSCNN',
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'ResNeSt', 'MobileNetV2', 'UNet', 'CGNet', 'MobileNetV3',
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'VisionTransformer', 'SwinTransformer', 'MixVisionTransformer',
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'BiSeNetV1', 'BiSeNetV2', 'ICNet', 'TIMMBackbone', 'ERFNet', 'PCPVT',
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'SVT', 'STDCNet', 'STDCContextPathNet', 'BEiT', 'MAE', 'PIDNet', 'MSCAN',
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'DDRNet'
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]
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