# Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
## Introduction
[ALGORITHM]
```latex
@inproceedings{deeplabv3plus2018,
title={Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
author={Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
booktitle={ECCV},
year={2018}
}
```
## Results and models
Note:
`D-8`/`D-16` here corresponding to the output stride 8/16 setting for DeepLab series.
`MG-124` stands for multi-grid dilation in the last stage of ResNet.
### Cityscapes
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download |
|------------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| DeepLabV3+ | R-50-D8 | 512x1024 | 40000 | 7.5 | 3.94 | 79.61 | 81.01 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610.log.json) |
| DeepLabV3+ | R-101-D8 | 512x1024 | 40000 | 11 | 2.60 | 80.21 | 81.82 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614.log.json) |
| DeepLabV3+ | R-50-D8 | 769x769 | 40000 | 8.5 | 1.72 | 78.97 | 80.46 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143.log.json) |
| DeepLabV3+ | R-101-D8 | 769x769 | 40000 | 12.5 | 1.15 | 79.46 | 80.50 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304.log.json) |
| DeepLabV3+ | R-18-D8 | 512x1024 | 80000 | 2.2 | 14.27 | 76.89 | 78.76 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes-20201226_080942.log.json) |
| DeepLabV3+ | R-50-D8 | 512x1024 | 80000 | - | - | 80.09 | 81.13 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049.log.json) |
| DeepLabV3+ | R-101-D8 | 512x1024 | 80000 | - | - | 80.97 | 82.03 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143.log.json) |
| DeepLabV3+ | R-18-D8 | 769x769 | 80000 | 2.5 | 5.74 | 76.26 | 77.91 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes-20201226_083346.log.json) |
| DeepLabV3+ | R-50-D8 | 769x769 | 80000 | - | - | 79.83 | 81.48 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233.log.json) |
| DeepLabV3+ | R-101-D8 | 769x769 | 80000 | - | - | 80.98 | 82.18 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20200607_000405-a7573d20.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20200607_000405.log.json) |
| DeepLabV3+ | R-101-D16-MG124 | 512x1024 | 40000 | 5.8 | 7.48 | 79.09 | 80.36 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json) |
| DeepLabV3+ | R-101-D16-MG124 | 512x1024 | 80000 | 9.9 | - | 79.90 | 81.33 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json) |
| DeepLabV3+ | R-18b-D8 | 512x1024 | 80000 | 2.1 | 14.95 | 75.87 | 77.52 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth) | [log](https://download.openmmlab.com/mmsegmentat
基于SegFormer的水稻重大叶片病害分割模型RSegFormer
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2023-12-26
13:16:11
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基于SegFormer的水稻重大叶片病害分割模型RSegFormer （779个子文件） 
make.bat 760B setup.cfg 427B Dockerfile 750B
seg_demo.gif 930KB .gitignore 1KB pytest.ini 293B MMSegmentation_Tutorial.ipynb 1.31MB inference_demo.ipynb 540KB LICENSE 4KB Makefile 634B config.md 20KB README.md 18KB README.md 18KB README.md 17KB README.md 15KB README.md 15KB README.md 15KB README.md 15KB README.md 14KB README.md 14KB README.md 11KB README.md 10KB README.md 10KB README.md 10KB README.md 10KB README.md 10KB README.md 10KB README.md 10KB README.md 10KB customize_runtime.md 9KB README.md 8KB README.md 8KB README.md 7KB README.md 7KB dataset_prepare.md 7KB get_started.md 7KB customize_models.md 6KB README.md 6KB changelog.md 6KB README.md 6KB model_zoo.md 6KB inference.md 5KB customize_datasets.md 5KB data_pipeline.md 4KB README.md 4KB README.md 4KB train.md 4KB README.md 3KB README.md 3KB README.md 3KB useful_tools.md 2KB training_tricks.md 2KB README.md 2KB README.md 1KB README.md 1B
demo.png 301KB image.png 65KB mmseg-logo.png 44KB transforms.py 42KB test_backbone.py 32KB test_unet.py 30KB test_heads.py 27KB resnet.py 24KB hrnet.py 21KB shuntednet.py 18KB unet.py 18KB test_transform.py 17KB mix_transformer.py 16KB point_head.py 14KB fast_scnn.py 14KB custom.py 14KB repMLp.py 13KB cgnet.py 13KB lovasz_loss.py 11KB lawin_head.py 11KB encoder_decoder.py 11KB base.py 10KB mobilenet_v3.py 10KB resnest.py 10KB test_dataset.py 10KB metrics.py 9KB formating.py 9KB decode_head.py 9KB ann_head.py 9KB fpn.py 9KB cityscapes.py 8KB test.py 8KB psa_head.py 7KB test_forward.py 7KB cross_entropy_loss.py 7KB class_names.py 7KB inverted_residual.py 7KB test_loading.py 7KB mobilenet_v2.py 7KB drop.py 7KB enc_head.py 7KB test_eval_hook.py 7KB test_losses.py 6KB test_segmentor.py 6KB self_attention_block.py 6KB共 779 条
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