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# PSPNet
> [Pyramid Scene Parsing Network](https://arxiv.org/abs/1612.01105)
## Introduction
<!-- [ALGORITHM] -->
<a href="https://github.com/hszhao/PSPNet">Official Repo</a>
<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63">Code Snippet</a>
## Abstract
<!-- [ABSTRACT] -->
Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this paper, we exploit the capability of global context information by different-region-based context aggregation through our pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet). Our global prior representation is effective to produce good quality results on the scene parsing task, while PSPNet provides a superior framework for pixel-level prediction tasks. The proposed approach achieves state-of-the-art performance on various datasets. It came first in ImageNet scene parsing challenge 2016, PASCAL VOC 2012 benchmark and Cityscapes benchmark. A single PSPNet yields new record of mIoU accuracy 85.4% on PASCAL VOC 2012 and accuracy 80.2% on Cityscapes.
<!-- [IMAGE] -->
<div align=center>
<img src="https://user-images.githubusercontent.com/24582831/142902444-9f93b99e-9261-443b-a0a4-17e78eefb525.png" width="70%"/>
</div>
<div align=center >
<img alt="PSPNet-R50-D8" src="https://user-images.githubusercontent.com/47882088/209554973-66804b14-de5a-4f83-b54e-26683a91818a.jpg"/>
PSPNet-R50 D8 model structure
</div>
## Results and models
### Cityscapes
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
| ------------- | ------------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| PSPNet | R-50-D8 | 512x1024 | 40000 | 6.1 | 4.07 | V100 | 77.85 | 79.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338.log.json) |
| PSPNet | R-101-D8 | 512x1024 | 40000 | 9.6 | 2.68 | V100 | 78.34 | 79.74 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751.log.json) |
| PSPNet | R-50-D8 | 769x769 | 40000 | 6.9 | 1.76 | V100 | 78.26 | 79.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725-86638686.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725.log.json) |
| PSPNet | R-101-D8 | 769x769 | 40000 | 10.9 | 1.15 | V100 | 79.08 | 80.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753-61c6f5be.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753.log.json) |
| PSPNet | R-18-D8 | 512x1024 | 80000 | 1.7 | 15.71 | V100 | 74.87 | 76.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes_20201225_021458-09ffa746.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes-20201225_021458.log.json) |
| PSPNet | R-50-D8 | 512x1024 | 80000 | - | - | V100 | 78.55 | 79.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131.log.json) |
| PSPNet | R-50b-D8 rsb | 512x1024 | 80000 | 6.2 | 3.82 | V100 | 78.47 | 79.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8-rsb_4xb2-adamw-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220315_123238-588c30be.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220315_123238.log.json) |
| PSPNet | R-101-D8 | 512x1024 | 80000 | - | - | V100 | 79.76 | 81.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211.log.json) |
| PSPNet (FP16) | R-101-D8 | 512x1024 | 80000 | 5.34 | 8.77 | V100 | 79.46 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes/pspnet_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230919-a0875e5c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes/pspnet_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230919.log.json) |
| PSPNet | R-18-D8 | 769x769 | 80000 | 1.9 | 6.20 | V100 | 75.90 | 77.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes_20201225_021458-3deefc62.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes-20201225_021458.log.json) |
| PSPNet | R-50-D8 | 769x769 | 80000 | - | - | V100 | 79.59 | 80.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121-5ccf03dd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121.log.json) |
| PSPNet | R-101-D8 | 769x769 | 80000 | - | - | V100 | 79.77 | 81.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.oz1z1penmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055-dba412fa.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055.log.json) |
| PSPNet | R-18b-D8 | 512x1024 | 80000 | 1.5 | 16.28 | V100 | 74.23 | 75.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes_20201226_063116-26928a60.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes-20201226_063116.log.json) |
| PSPNet | R-50b-D8 | 512x1024 | 80000 | 6.0 | 4.30 | V100 | 78.22 | 79.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes_20201225_094315-6344287a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes-20201225_094315.log.json) |
| PSPNet | R-101b-D8 | 512x1024 | 80000 | 9.5 | 2.76 | V100 | 79.69 | 80.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes-20201226_170012.log.json) |
| PSPNet | R-18b-D8 | 769x769 | 80000 | 1.7 | 6.41 | V100 | 74.92 | 76.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes_20201226_080942-bf98d186.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes-20201226_080942.log.json) |
| PSPNet | R-50b-D8 | 769x769 | 80000 | 6.8 | 1.88 | V100 | 78.50 | 79.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes_20201225_094316-4c643cf6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes-20201225_094316.log.json) |
| PSPNet | R-101b-D8 | 769x769 | 80000 | 10.8 | 1.17 | V100 | 78.87 | 80.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes-20201226_171823.log.json) |
| PSPNet | R-50-D32 | 512x1024 | 80000 | 3.0 | 15.21 | V100 | 73.88 | 76.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes/pspnet_r50-d32_512x1024_80k_cityscapes_20220316_224840-9092b254.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes/pspnet_r50-d32_512x1024_80k_cityscapes_20220316_224840.log.json) |
| PSPNet | R-50b-D32 rsb | 512x1024 | 80000 | 3.1 | 16.08 | V100 | 74.09 | 77.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d32_rsb_4xb2-adamw-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220316_141229-dd9c9610.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220316_141229.log.json) |
| PSPNet | R-50b-D32 | 512x1024 | 80000 | 2.9 | 15.41 | V100 | 72.61 | 75.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes/pspnet_r50b-d32_512x1024_80k_cityscapes_20220311_152152-23bcaf8c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes/pspnet_r50b-d32_512x1024_80k_cityscapes_20220311_152152.log.json) |
### ADE20K
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-50-D8 | 512x512 | 80000 | 8.5 | 23.53 | V100 | 41.13 | 41.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128-15a8b914.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128.log.json) |
| PSPNet | R-101-D8 | 512x512 | 80000 | 12 | 15.30 | V100 | 43.57 | 44.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423-b6e782f0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423.log.json) |
| PSPNet | R-50-D8 | 512x512 | 160000 | - | - | V100 | 42.48 | 43.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358.log.json) |
| PSPNet | R-101-D8 | 512x512 | 160000 | - | - | V100 | 44.39 | 45.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650.log.json) |
### Pascal VOC 2012 + Aug
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-50-D8 | 512x512 | 20000 | 6.1 | 23.59 | V100 | 76.78 | 77.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958-ed5dfbd9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958.log.json) |
| PSPNet | R-101-D8 | 512x512 | 20000 | 9.6 | 15.02 | V100 | 78.47 | 79.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003-4aef3c9a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003.log.json) |
| PSPNet | R-50-D8 | 512x512 | 40000 | - | - | V100 | 77.29 | 78.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222-ae9c1b8c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222.log.json) |
| PSPNet | R-101-D8 | 512x512 | 40000 | - | - | V100 | 78.52 | 79.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222-bc933b18.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222.log.json) |
### Pascal Context
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-101-D8 | 480x480 | 40000 | 8.8 | 9.68 | V100 | 46.60 | 47.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context_20200911_211210-bf0f5d7c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context-20200911_211210.log.json) |
| PSPNet | R-101-D8 | 480x480 | 80000 | - | - | V100 | 46.03 | 47.15 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context_20200911_190530-c86d6233.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context-20200911_190530.log.json) |
### Pascal Context 59
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-101-D8 | 480x480 | 40000 | - | - | V100 | 52.02 | 53.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59_20210416_114524-86d44cd4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59-20210416_114524.log.json) |
| PSPNet | R-101-D8 | 480x480 | 80000 | - | - | V100 | 52.47 | 53.99 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59_20210416_114418-fa6caaa2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59-20210416_114418.log.json) |
### Dark Zurich and Nighttime Driving
We support evaluation results on these two datasets using models above trained on Cityscapes training set.
| Method | Backbone | Training Dataset | Test Dataset | mIoU | config | evaluation checkpoint |
| ------ | --------- | ----------------------- | ------------------------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| PSPNet | R-50-D8 | Cityscapes Training set | Dark Zurich | 10.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338.log.json) |
| PSPNet | R-50-D8 | Cityscapes Training set | Nighttime Driving | 23.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338.log.json) |
| PSPNet | R-50-D8 | Cityscapes Training set | Cityscapes Validation set | 77.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338.log.json) |
| PSPNet | R-101-D8 | Cityscapes Training set | Dark Zurich | 10.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751.log.json) |
| PSPNet | R-101-D8 | Cityscapes Training set | Nighttime Driving | 20.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751.log.json) |
| PSPNet | R-101-D8 | Cityscapes Training set | Cityscapes Validation set | 78.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751.log.json) |
| PSPNet | R-101b-D8 | Cityscapes Training set | Dark Zurich | 15.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes-20201226_170012.log.json) |
| PSPNet | R-101b-D8 | Cityscapes Training set | Nighttime Driving | 22.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes-20201226_170012.log.json) |
| PSPNet | R-101b-D8 | Cityscapes Training set | Cityscapes Validation set | 79.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes-20201226_170012.log.json) |
### COCO-Stuff 10k
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-50-D8 | 512x512 | 20000 | 9.6 | 20.5 | V100 | 35.69 | 36.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb4-20k_coco-stuff10k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k_20210820_203258-b88df27f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k_20210820_203258.log.json) |
| PSPNet | R-101-D8 | 512x512 | 20000 | 13.2 | 11.1 | V100 | 37.26 | 38.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb4-20k_coco-stuff10k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k_20210820_232135-76aae482.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k_20210820_232135.log.json) |
| PSPNet | R-50-D8 | 512x512 | 40000 | - | - | V100 | 36.33 | 37.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_030857-92e2902b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_030857.log.json) |
| PSPNet | R-101-D8 | 512x512 | 40000 | - | - | V100 | 37.76 | 38.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb4-40k_coco-stuff10k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_014022-831aec95.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_014022.log.json) |
### COCO-Stuff 164k
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-50-D8 | 512x512 | 80000 | 9.6 | 20.5 | V100 | 38.80 | 39.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb4-80k_coco-stuff164k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-0e41b2db.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034.log.json) |
| PSPNet | R-101-D8 | 512x512 | 80000 | 13.2 | 11.1 | V100 | 40.34 | 40.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb4-80k_coco-stuff164k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-7eb41789.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034.log.json) |
| PSPNet | R-50-D8 | 512x512 | 160000 | - | - | V100 | 39.64 | 39.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb4-160k_coco-stuff164k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-51276a57.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004.log.json) |
| PSPNet | R-101-D8 | 512x512 | 160000 | - | - | V100 | 41.28 | 41.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb4-160k_coco-stuff164k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-4af9621b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004.log.json) |
| PSPNet | R-50-D8 | 512x512 | 320000 | - | - | V100 | 40.53 | 40.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-be9610cc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004.log.json) |
| PSPNet | R-101-D8 | 512x512 | 320000 | - | - | V100 | 41.95 | 42.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb4-320k_coco-stuff164k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-72220c60.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004.log.json) |
### LoveDA
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| PSPNet | R-18-D8 | 512x512 | 80000 | 1.45 | 26.87 | V100 | 48.62 | 47.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r18-d8_4xb4-80k_loveda-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x512_80k_loveda/pspnet_r18-d8_512x512_80k_loveda_20211105_052100-b97697f1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x512_80k_loveda/pspnet_r18-d8_512x512_80k_loveda_20211105_052100.log.json) |
| PSPNet | R-50-D8 | 512x512 | 80000 | 6.14 | 6.60 | V100 | 50.46 | 50.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb4-80k_loveda-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728-88610f9f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728.log.json) |
| PSPNet | R-101-D8 | 512x512 | 80000 | 9.61 | 4.58 | V100 | 51.86 | 51.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb4-80k_loveda-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212-1c06c6a8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212.log.json) |
### Potsdam
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-18-D8 | 512x512 | 80000 | 1.50 | 85.12 | V100 | 77.09 | 78.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r18-d8_4xb4-80k_potsdam-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam/pspnet_r18-d8_4x4_512x512_80k_potsdam_20211220_125612-7cd046e1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam/pspnet_r18-d8_4x4_512x512_80k_potsdam_20211220_125612.log.json) |
| PSPNet | R-50-D8 | 512x512 | 80000 | 6.14 | 30.21 | V100 | 78.12 | 78.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb4-80k_potsdam-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam/pspnet_r50-d8_4x4_512x512_80k_potsdam_20211219_043541-2dd5fe67.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam/pspnet_r50-d8_4x4_512x512_80k_potsdam_20211219_043541.log.json) |
| PSPNet | R-101-D8 | 512x512 | 80000 | 9.61 | 19.40 | V100 | 78.62 | 79.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb4-80k_potsdam-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam/pspnet_r101-d8_4x4_512x512_80k_potsdam_20211220_125612-aed036c4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam/pspnet_r101-d8_4x4_512x512_80k_potsdam_20211220_125612.log.json) |
### Vaihingen
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-18-D8 | 512x512 | 80000 | 1.45 | 85.06 | V100 | 71.46 | 73.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r18-d8_4xb4-80k_vaihingen-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen/pspnet_r18-d8_4x4_512x512_80k_vaihingen_20211228_160355-52a8a6f6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen/pspnet_r18-d8_4x4_512x512_80k_vaihingen_20211228_160355.log.json) |
| PSPNet | R-50-D8 | 512x512 | 80000 | 6.14 | 30.29 | V100 | 72.36 | 73.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355-382f8f5b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355.log.json) |
| PSPNet | R-101-D8 | 512x512 | 80000 | 9.61 | 19.97 | V100 | 72.61 | 74.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r101-d8_4xb4-80k_vaihingen-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806-8eba0a09.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806.log.json) |
### iSAID
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PSPNet | R-18-D8 | 896x896 | 80000 | 4.52 | 26.91 | V100 | 60.22 | 61.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r18-d8_4xb4-80k_isaid-896x896.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526-e84c0b6a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526.log.json) |
| PSPNet | R-50-D8 | 896x896 | 80000 | 16.58 | 8.88 | V100 | 65.36 | 66.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/pspnet/pspnet_r50-d8_4xb4-80k_isaid-896x896.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629-1f21dc32.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629.log.json) |
Note:
- `FP16` means Mixed Precision (FP16) is adopted in training.
- `896x896` is the Crop Size of iSAID dataset, which is followed by the implementation of [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://arxiv.org/pdf/2103.06564.pdf)
- `rsb` is short for 'Resnet strikes back'.
- The `b` in `R-50b` means ResNetV1b, which is a standard ResNet backbone. In MMSegmentation, default backbone is ResNetV1c, which usually performs better in semantic segmentation task.
## Citation
```bibtex
@inproceedings{zhao2017pspnet,
title={Pyramid Scene Parsing Network},
author={Zhao, Hengshuang and Shi, Jianping and Qi, Xiaojuan and Wang, Xiaogang and Jia, Jiaya},
booktitle={CVPR},
year={2017}
}
```
```bibtex
@article{wightman2021resnet,
title={Resnet strikes back: An improved training procedure in timm},
author={Wightman, Ross and Touvron, Hugo and J{\'e}gou, Herv{\'e}},
journal={arXiv preprint arXiv:2110.00476},
year={2021}
}
```

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Seg_All_In_One_MMSeg/configs/pspnet/metafile.yaml Normal file
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Seg_All_In_One_MMSeg/configs/pspnet/my_pspnet_r18_Pre_g1-300e_val1_check10_publicdataset_autolaparo-512x512.py Normal file
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_base_ = ['../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/publicdataset_autolaparo.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
norm_cfg = {'type': 'BN'}
crop_size = (512, 512)
data_preprocessor = {'size': (512, 512), 'mean': [123.62464353460942, 85.34836259209033, 82.31539425671558], 'std': [47.172211618459315, 47.08256715323592, 48.135121265163605], 'bgr_to_rgb': False}
model = {'pretrained': './My_Local_Model/open_mmlab/resnet18_v1c.pth', 'backbone': {'depth': 18, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [123.62464353460942, 85.34836259209033, 82.31539425671558], 'std': [47.172211618459315, 47.08256715323592, 48.135121265163605], 'bgr_to_rgb': False}, 'decode_head': {'in_channels': 512, 'channels': 128, 'num_classes': 10, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 256, 'channels': 64, 'num_classes': 10, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}}
test_cfg = {'crop_size': (512, 512)}
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r18_Pre_g1-300e_val1_check10_publicdataset_autolaparo-512x512'}}]
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r18_Pre_g1-300e_val1_check10_publicdataset_autolaparo-512x512'}}]}

20
Seg_All_In_One_MMSeg/configs/pspnet/my_pspnet_r18_Pre_g1-300e_val1_check10_publicdataset_cholecseg8k-512x512.py Normal file
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_base_ = ['../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/publicdataset_cholecseg8k.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
norm_cfg = {'type': 'BN'}
crop_size = (512, 512)
data_preprocessor = {'size': (512, 512), 'mean': [85.65740418979115, 53.99282220050495, 46.074045888534535], 'std': [72.24589167201978, 56.76979155397199, 49.056637115061775], 'bgr_to_rgb': False}
model = {'pretrained': './My_Local_Model/open_mmlab/resnet18_v1c.pth', 'backbone': {'depth': 18, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [85.65740418979115, 53.99282220050495, 46.074045888534535], 'std': [72.24589167201978, 56.76979155397199, 49.056637115061775], 'bgr_to_rgb': False}, 'decode_head': {'in_channels': 512, 'channels': 128, 'num_classes': 13, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 256, 'channels': 64, 'num_classes': 13, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}}
test_cfg = {'crop_size': (512, 512)}
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r18_Pre_g1-300e_val1_check10_publicdataset_cholecseg8k-512x512'}}]
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r18_Pre_g1-300e_val1_check10_publicdataset_cholecseg8k-512x512'}}]}

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Seg_All_In_One_MMSeg/configs/pspnet/my_pspnet_r18_Pre_g1-300e_val1_check10_publicdataset_dresden-512x512.py Normal file
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_base_ = ['../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/publicdataset_dresden.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
norm_cfg = {'type': 'BN'}
crop_size = (512, 512)
data_preprocessor = {'size': (512, 512), 'mean': [103.172638338208, 61.44762740851152, 51.407770213021976], 'std': [75.77031253622098, 54.63616729031377, 49.45572239497569], 'bgr_to_rgb': False}
model = {'pretrained': './My_Local_Model/open_mmlab/resnet18_v1c.pth', 'backbone': {'depth': 18, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [103.172638338208, 61.44762740851152, 51.407770213021976], 'std': [75.77031253622098, 54.63616729031377, 49.45572239497569], 'bgr_to_rgb': False}, 'decode_head': {'in_channels': 512, 'channels': 128, 'num_classes': 11, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 256, 'channels': 64, 'num_classes': 11, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}}
test_cfg = {'crop_size': (512, 512)}
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r18_Pre_g1-300e_val1_check10_publicdataset_dresden-512x512'}}]
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r18_Pre_g1-300e_val1_check10_publicdataset_dresden-512x512'}}]}

20
Seg_All_In_One_MMSeg/configs/pspnet/my_pspnet_r18_Pre_g1-300e_val1_check10_publicdataset_endovis_2017-512x512.py Normal file
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_base_ = ['../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/publicdataset_endovis_2017.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
norm_cfg = {'type': 'BN'}
crop_size = (512, 512)
data_preprocessor = {'size': (512, 512), 'mean': [122.21429912990676, 77.0821859677977, 87.03836664626716], 'std': [50.53335800365262, 42.895340354037465, 47.739426483390446], 'bgr_to_rgb': False}
model = {'pretrained': './My_Local_Model/open_mmlab/resnet18_v1c.pth', 'backbone': {'depth': 18, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [122.21429912990676, 77.0821859677977, 87.03836664626716], 'std': [50.53335800365262, 42.895340354037465, 47.739426483390446], 'bgr_to_rgb': False}, 'decode_head': {'in_channels': 512, 'channels': 128, 'num_classes': 8, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 256, 'channels': 64, 'num_classes': 8, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}}
test_cfg = {'crop_size': (512, 512)}
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r18_Pre_g1-300e_val1_check10_publicdataset_endovis_2017-512x512'}}]
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r18_Pre_g1-300e_val1_check10_publicdataset_endovis_2017-512x512'}}]}

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Seg_All_In_One_MMSeg/configs/pspnet/my_pspnet_r18_Pre_g1-300e_val1_check10_publicdataset_endovis_2018-512x512.py Normal file
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_base_ = ['../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/publicdataset_endovis_2018.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
norm_cfg = {'type': 'BN'}
crop_size = (512, 512)
data_preprocessor = {'size': (512, 512), 'mean': [122.21429912990676, 77.0821859677977, 87.03836664626716], 'std': [50.53335800365262, 42.895340354037465, 47.739426483390446], 'bgr_to_rgb': False}
model = {'pretrained': './My_Local_Model/open_mmlab/resnet18_v1c.pth', 'backbone': {'depth': 18, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [122.21429912990676, 77.0821859677977, 87.03836664626716], 'std': [50.53335800365262, 42.895340354037465, 47.739426483390446], 'bgr_to_rgb': False}, 'decode_head': {'in_channels': 512, 'channels': 128, 'num_classes': 8, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 256, 'channels': 64, 'num_classes': 8, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}}
test_cfg = {'crop_size': (512, 512)}
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r18_Pre_g1-300e_val1_check10_publicdataset_endovis_2018-512x512'}}]
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r18_Pre_g1-300e_val1_check10_publicdataset_endovis_2018-512x512'}}]}

22
Seg_All_In_One_MMSeg/configs/pspnet/my_pspnet_r50_Pre_g1-300e_val1_check10_publicdataset_autolaparo-512x512.py Normal file
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_base_ = ['../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/publicdataset_autolaparo.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
norm_cfg = {'type': 'BN'}
crop_size = (512, 512)
train_dataloader = dict(batch_size=8) # TODO
data_preprocessor = {'size': (512, 512), 'mean': [123.62464353460942, 85.34836259209033, 82.31539425671558], 'std': [47.172211618459315, 47.08256715323592, 48.135121265163605], 'bgr_to_rgb': False}
model = {'pretrained': './My_Local_Model/open_mmlab/resnet50_v1c.pth', 'backbone': {'depth': 50, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [123.62464353460942, 85.34836259209033, 82.31539425671558], 'std': [47.172211618459315, 47.08256715323592, 48.135121265163605], 'bgr_to_rgb': False}, 'decode_head': {'in_channels': 2048, 'channels': 512, 'num_classes': 10, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 1024, 'channels': 256, 'num_classes': 10, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}}
test_cfg = {'crop_size': (512, 512)}
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r50_Pre_g1-300e_val1_check10_publicdataset_autolaparo-512x512'}}]
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r50_Pre_g1-300e_val1_check10_publicdataset_autolaparo-512x512'}}]}

22
Seg_All_In_One_MMSeg/configs/pspnet/my_pspnet_r50_Pre_g1-300e_val1_check10_publicdataset_cholecseg8k-512x512.py Normal file
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_base_ = ['../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/publicdataset_cholecseg8k.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
norm_cfg = {'type': 'BN'}
crop_size = (512, 512)
train_dataloader = dict(batch_size=8) # TODO
data_preprocessor = {'size': (512, 512), 'mean': [85.65740418979115, 53.99282220050495, 46.074045888534535], 'std': [72.24589167201978, 56.76979155397199, 49.056637115061775], 'bgr_to_rgb': False}
model = {'pretrained': './My_Local_Model/open_mmlab/resnet50_v1c.pth', 'backbone': {'depth': 50, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [85.65740418979115, 53.99282220050495, 46.074045888534535], 'std': [72.24589167201978, 56.76979155397199, 49.056637115061775], 'bgr_to_rgb': False}, 'decode_head': {'in_channels': 2048, 'channels': 512, 'num_classes': 13, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 1024, 'channels': 256, 'num_classes': 13, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}}
test_cfg = {'crop_size': (512, 512)}
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r50_Pre_g1-300e_val1_check10_publicdataset_cholecseg8k-512x512'}}]
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r50_Pre_g1-300e_val1_check10_publicdataset_cholecseg8k-512x512'}}]}

22
Seg_All_In_One_MMSeg/configs/pspnet/my_pspnet_r50_Pre_g1-300e_val1_check10_publicdataset_dresden-512x512.py Normal file
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_base_ = ['../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/publicdataset_dresden.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
norm_cfg = {'type': 'BN'}
crop_size = (512, 512)
train_dataloader = dict(batch_size=4) # TODO
data_preprocessor = {'size': (512, 512), 'mean': [103.172638338208, 61.44762740851152, 51.407770213021976], 'std': [75.77031253622098, 54.63616729031377, 49.45572239497569], 'bgr_to_rgb': False}
model = {'pretrained': './My_Local_Model/open_mmlab/resnet50_v1c.pth', 'backbone': {'depth': 50, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [103.172638338208, 61.44762740851152, 51.407770213021976], 'std': [75.77031253622098, 54.63616729031377, 49.45572239497569], 'bgr_to_rgb': False}, 'decode_head': {'in_channels': 2048, 'channels': 512, 'num_classes': 11, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 1024, 'channels': 256, 'num_classes': 11, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}}
test_cfg = {'crop_size': (512, 512)}
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r50_Pre_g1-300e_val1_check10_publicdataset_dresden-512x512'}}]
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r50_Pre_g1-300e_val1_check10_publicdataset_dresden-512x512'}}]}

22
Seg_All_In_One_MMSeg/configs/pspnet/my_pspnet_r50_Pre_g1-300e_val1_check10_publicdataset_endovis_2017-512x512.py Normal file
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_base_ = ['../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/publicdataset_endovis_2017.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
norm_cfg = {'type': 'BN'}
crop_size = (512, 512)
train_dataloader = dict(batch_size=8) # TODO
data_preprocessor = {'size': (512, 512), 'mean': [122.21429912990676, 77.0821859677977, 87.03836664626716], 'std': [50.53335800365262, 42.895340354037465, 47.739426483390446], 'bgr_to_rgb': False}
model = {'pretrained': './My_Local_Model/open_mmlab/resnet50_v1c.pth', 'backbone': {'depth': 50, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [122.21429912990676, 77.0821859677977, 87.03836664626716], 'std': [50.53335800365262, 42.895340354037465, 47.739426483390446], 'bgr_to_rgb': False}, 'decode_head': {'in_channels': 2048, 'channels': 512, 'num_classes': 8, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 1024, 'channels': 256, 'num_classes': 8, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}}
test_cfg = {'crop_size': (512, 512)}
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r50_Pre_g1-300e_val1_check10_publicdataset_endovis_2017-512x512'}}]
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r50_Pre_g1-300e_val1_check10_publicdataset_endovis_2017-512x512'}}]}

22
Seg_All_In_One_MMSeg/configs/pspnet/my_pspnet_r50_Pre_g1-300e_val1_check10_publicdataset_endovis_2018-512x512.py Normal file
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_base_ = ['../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/publicdataset_endovis_2018.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']
norm_cfg = {'type': 'BN'}
crop_size = (512, 512)
train_dataloader = dict(batch_size=8) # TODO
data_preprocessor = {'size': (512, 512), 'mean': [122.21429912990676, 77.0821859677977, 87.03836664626716], 'std': [50.53335800365262, 42.895340354037465, 47.739426483390446], 'bgr_to_rgb': False}
model = {'pretrained': './My_Local_Model/open_mmlab/resnet50_v1c.pth', 'backbone': {'depth': 50, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [122.21429912990676, 77.0821859677977, 87.03836664626716], 'std': [50.53335800365262, 42.895340354037465, 47.739426483390446], 'bgr_to_rgb': False}, 'decode_head': {'in_channels': 2048, 'channels': 512, 'num_classes': 8, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 1024, 'channels': 256, 'num_classes': 8, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}}
test_cfg = {'crop_size': (512, 512)}
optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}
param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]
vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r50_Pre_g1-300e_val1_check10_publicdataset_endovis_2018-512x512'}}]
visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r50_Pre_g1-300e_val1_check10_publicdataset_endovis_2018-512x512'}}]}

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024.py Normal file
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_base_ = './pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py Normal file
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_base_ = './pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py' # noqa
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py Normal file
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_base_ = './pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py' # noqa
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-769x769.py Normal file
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_base_ = './pspnet_r50-d8_4xb2-40k_cityscapes-769x769.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py Normal file
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_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-769x769.py Normal file
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_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

6
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py Normal file
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_base_ = './pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py'
optim_wrapper = dict(
_delete_=True,
type='AmpOptimWrapper',
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005),
loss_scale=512.)

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb4-160k_ade20k-512x512.py Normal file
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_base_ = './pspnet_r50-d8_4xb4-160k_ade20k-512x512.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb4-160k_coco-stuff164k-512x512.py Normal file
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_base_ = './pspnet_r50-d8_4xb4-160k_coco-stuff164k-512x512.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb4-20k_coco-stuff10k-512x512.py Normal file
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_base_ = './pspnet_r50-d8_4xb4-20k_coco-stuff10k-512x512.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb4-20k_voc12aug-512x512.py Normal file
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_base_ = './pspnet_r50-d8_4xb4-20k_voc12aug-512x512.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb4-320k_coco-stuff164k-512x512.py Normal file
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_base_ = './pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb4-40k_coco-stuff10k-512x512.py Normal file
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_base_ = './pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-480x480.py Normal file
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_base_ = './pspnet_r50-d8_4xb4-40k_pascal-context-480x480.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-59-480x480.py Normal file
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_base_ = './pspnet_r50-d8_4xb4-40k_pascal-context-59-480x480.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py Normal file
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_base_ = './pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb4-80k_ade20k-512x512.py Normal file
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_base_ = './pspnet_r50-d8_4xb4-80k_ade20k-512x512.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb4-80k_coco-stuff164k-512x512.py Normal file
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_base_ = './pspnet_r50-d8_4xb4-80k_coco-stuff164k-512x512.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb4-80k_loveda-512x512.py Normal file
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_base_ = './pspnet_r50-d8_4xb4-80k_loveda-512x512.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-480x480.py Normal file
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_base_ = './pspnet_r50-d8_4xb4-80k_pascal-context-480x480.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py Normal file
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_base_ = './pspnet_r50-d8_4xb4-80k_pascal-context-59-480x480.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb4-80k_potsdam-512x512.py Normal file
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_base_ = './pspnet_r50-d8_4xb4-80k_potsdam-512x512.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101-d8_4xb4-80k_vaihingen-512x512.py Normal file
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_base_ = './pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py'
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

4
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024.py Normal file
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@@ -0,0 +1,4 @@
_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))

4
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.py Normal file
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_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.py' # noqa
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))

4
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.py Normal file
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@@ -0,0 +1,4 @@
_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.py' # noqa
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))

4
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-769x769.py Normal file
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@@ -0,0 +1,4 @@
_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))

9
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-512x1024.py Normal file
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@@ -0,0 +1,9 @@
_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))

9
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-769x769.py Normal file
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@@ -0,0 +1,9 @@
_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))

9
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r18-d8_4xb4-80k_isaid-896x896.py Normal file
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@@ -0,0 +1,9 @@
_base_ = './pspnet_r50-d8_4xb4-80k_isaid-896x896.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))

9
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r18-d8_4xb4-80k_loveda-512x512.py Normal file
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@@ -0,0 +1,9 @@
_base_ = './pspnet_r50-d8_4xb4-80k_loveda-512x512.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))

9
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r18-d8_4xb4-80k_potsdam-512x512.py Normal file
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@@ -0,0 +1,9 @@
_base_ = './pspnet_r50-d8_4xb4-80k_potsdam-512x512.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))

9
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r18-d8_4xb4-80k_vaihingen-512x512.py Normal file
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@@ -0,0 +1,9 @@
_base_ = './pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py'
model = dict(
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))

9
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py Normal file
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@@ -0,0 +1,9 @@
_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
model = dict(
pretrained='torchvision://resnet18',
backbone=dict(type='ResNet', depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))

9
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-769x769.py Normal file
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@@ -0,0 +1,9 @@
_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py'
model = dict(
pretrained='torchvision://resnet18',
backbone=dict(type='ResNet', depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))

9
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d32_4xb2-80k_cityscapes-512x1024.py Normal file
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@@ -0,0 +1,9 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
crop_size = (512, 1024)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
backbone=dict(dilations=(1, 1, 2, 4), strides=(1, 2, 2, 2)))

35
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d32_rsb_4xb2-adamw-80k_cityscapes-512x1024.py Normal file
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@@ -0,0 +1,35 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
crop_size = (512, 1024)
data_preprocessor = dict(size=crop_size)
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = dict(
data_preprocessor=data_preprocessor,
pretrained=None,
backbone=dict(
type='ResNet',
init_cfg=dict(
type='Pretrained', prefix='backbone.', checkpoint=checkpoint),
dilations=(1, 1, 2, 4),
strides=(1, 2, 2, 2)))
optim_wrapper = dict(
_delete_=True,
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=0.0005, weight_decay=0.05),
clip_grad=dict(max_norm=1, norm_type=2))
# learning policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
end=1000),
dict(
type='MultiStepLR',
begin=1000,
end=80000,
by_epoch=False,
milestones=[60000, 72000],
)
]

33
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8-rsb_4xb2-adamw-80k_cityscapes-512x1024.py Normal file
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@@ -0,0 +1,33 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
crop_size = (512, 1024)
data_preprocessor = dict(size=crop_size)
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = dict(
data_preprocessor=data_preprocessor,
pretrained=None,
backbone=dict(
type='ResNet',
init_cfg=dict(
type='Pretrained', prefix='backbone.', checkpoint=checkpoint)))
optim_wrapper = dict(
_delete_=True,
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=0.0005, weight_decay=0.05),
clip_grad=dict(max_norm=1, norm_type=2))
# learning policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
end=1000),
dict(
type='MultiStepLR',
begin=1000,
end=80000,
by_epoch=False,
milestones=[60000, 72000],
)
]

7
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py Normal file
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@@ -0,0 +1,7 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
crop_size = (512, 1024)
data_preprocessor = dict(size=crop_size)
model = dict(data_preprocessor=data_preprocessor)

24
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py Normal file
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@@ -0,0 +1,24 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
crop_size = (512, 1024)
data_preprocessor = dict(size=crop_size)
model = dict(data_preprocessor=data_preprocessor)
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=(1920, 1080), keep_ratio=True),
# add loading annotation after ``Resize`` because ground truth
# does not need to do resize data transform
dict(type='LoadAnnotations'),
dict(type='PackSegInputs')
]
test_dataloader = dict(
dataset=dict(
type='DarkZurichDataset',
data_root='data/dark_zurich/',
data_prefix=dict(
img_path='rgb_anon/val/night/GOPR0356',
seg_map_path='gt/val/night/GOPR0356'),
pipeline=test_pipeline))

25
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py Normal file
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@@ -0,0 +1,25 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
crop_size = (512, 1024)
data_preprocessor = dict(size=crop_size)
model = dict(data_preprocessor=data_preprocessor)
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=(1920, 1080), keep_ratio=True),
# add loading annotation after ``Resize`` because ground truth
# does not need to do resize data transform
dict(type='LoadAnnotations'),
dict(type='PackSegInputs')
]
test_dataloader = dict(
dataset=dict(
type='NightDrivingDataset',
data_root='data/NighttimeDrivingTest/',
data_prefix=dict(
img_path='leftImg8bit/test/night',
seg_map_path='gtCoarse_daytime_trainvaltest/test/night'),
pipeline=test_pipeline))

12
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-769x769.py Normal file
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@@ -0,0 +1,12 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
crop_size = (769, 769)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

7
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py Normal file
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@@ -0,0 +1,7 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
crop_size = (512, 1024)
data_preprocessor = dict(size=crop_size)
model = dict(data_preprocessor=data_preprocessor)

25
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.py Normal file
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@@ -0,0 +1,25 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
crop_size = (512, 1024)
data_preprocessor = dict(size=crop_size)
model = dict(data_preprocessor=data_preprocessor)
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=(1920, 1080), keep_ratio=True),
# add loading annotation after ``Resize`` because ground truth
# does not need to do resize data transform
dict(type='LoadAnnotations'),
dict(type='PackSegInputs')
]
test_dataloader = dict(
dataset=dict(
type='DarkZurichDataset',
data_root='data/dark_zurich/',
data_prefix=dict(
img_path='rgb_anon/val/night/GOPR0356',
seg_map_path='gt/val/night/GOPR0356'),
pipeline=test_pipeline))

25
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.py Normal file
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@@ -0,0 +1,25 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
crop_size = (512, 1024)
data_preprocessor = dict(size=crop_size)
model = dict(data_preprocessor=data_preprocessor)
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=(1920, 1080), keep_ratio=True),
# add loading annotation after ``Resize`` because ground truth
# does not need to do resize data transform
dict(type='LoadAnnotations'),
dict(type='PackSegInputs')
]
test_dataloader = dict(
dataset=dict(
type='NightDrivingDataset',
data_root='data/NighttimeDrivingTest/',
data_prefix=dict(
img_path='leftImg8bit/test/night',
seg_map_path='gtCoarse_daytime_trainvaltest/test/night'),
pipeline=test_pipeline))

12
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py Normal file
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@@ -0,0 +1,12 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
crop_size = (769, 769)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

10
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb4-160k_ade20k-512x512.py Normal file
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@@ -0,0 +1,10 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
crop_size = (512, 512)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(num_classes=150),
auxiliary_head=dict(num_classes=150))

11
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb4-160k_coco-stuff164k-512x512.py Normal file
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@@ -0,0 +1,11 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
crop_size = (512, 512)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(num_classes=171),
auxiliary_head=dict(num_classes=171))

10
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb4-20k_coco-stuff10k-512x512.py Normal file
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@@ -0,0 +1,10 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/coco-stuff10k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py'
]
crop_size = (512, 512)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(num_classes=171),
auxiliary_head=dict(num_classes=171))

11
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb4-20k_voc12aug-512x512.py Normal file
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@@ -0,0 +1,11 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_20k.py'
]
crop_size = (512, 512)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(num_classes=21),
auxiliary_head=dict(num_classes=21))

11
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py Normal file
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@@ -0,0 +1,11 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_320k.py'
]
crop_size = (512, 512)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(num_classes=171),
auxiliary_head=dict(num_classes=171))

10
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py Normal file
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@@ -0,0 +1,10 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/coco-stuff10k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
crop_size = (512, 512)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(num_classes=171),
auxiliary_head=dict(num_classes=171))

14
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb4-40k_pascal-context-480x480.py Normal file
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@@ -0,0 +1,14 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/pascal_context.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
crop_size = (480, 480)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(num_classes=60),
auxiliary_head=dict(num_classes=60),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer)

14
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb4-40k_pascal-context-59-480x480.py Normal file
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@@ -0,0 +1,14 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/pascal_context_59.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
crop_size = (480, 480)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(num_classes=59),
auxiliary_head=dict(num_classes=59),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer)

11
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py Normal file
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@@ -0,0 +1,11 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
crop_size = (512, 512)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(num_classes=21),
auxiliary_head=dict(num_classes=21))

10
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb4-80k_ade20k-512x512.py Normal file
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@@ -0,0 +1,10 @@
_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
crop_size = (512, 512)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(num_classes=150),
auxiliary_head=dict(num_classes=150))

11
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb4-80k_coco-stuff164k-512x512.py Normal file
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_base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
crop_size = (512, 512)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(num_classes=171),
auxiliary_head=dict(num_classes=171))

10
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb4-80k_isaid-896x896.py Normal file
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_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/isaid.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
crop_size = (896, 896)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(num_classes=16),
auxiliary_head=dict(num_classes=16))

10
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb4-80k_loveda-512x512.py Normal file
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_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/loveda.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
crop_size = (512, 512)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(num_classes=7),
auxiliary_head=dict(num_classes=7))

14
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb4-80k_pascal-context-480x480.py Normal file
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_base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/pascal_context.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
crop_size = (480, 480)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(num_classes=60),
auxiliary_head=dict(num_classes=60),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer)

14
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb4-80k_pascal-context-59-480x480.py Normal file
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_base_ = [
'../_base_/models/pspnet_r50-d8.py',
'../_base_/datasets/pascal_context_59.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
crop_size = (480, 480)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(num_classes=59),
auxiliary_head=dict(num_classes=59),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer)

10
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb4-80k_potsdam-512x512.py Normal file
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_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/potsdam.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
crop_size = (512, 512)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(num_classes=6),
auxiliary_head=dict(num_classes=6))

10
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py Normal file
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_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/vaihingen.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
crop_size = (512, 512)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
decode_head=dict(num_classes=6),
auxiliary_head=dict(num_classes=6))

10
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024.py Normal file
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_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
crop_size = (512, 1024)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
pretrained='torchvision://resnet50',
backbone=dict(type='ResNet', dilations=(1, 1, 2, 4), strides=(1, 2, 2, 2)))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-512x1024.py Normal file
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_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))

2
Seg_All_In_One_MMSeg/configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-769x769.py Normal file
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_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))