diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index f41e4176c..11b788889 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -27,26 +27,23 @@ jobs: strategy: matrix: python-version: [3.7] - torch: [1.3.1, 1.5.1, 1.6.0, 1.7.0, 1.8.0, 1.9.0] + torch: [1.5.1, 1.6.0, 1.7.0, 1.8.0, 1.9.0] include: - - torch: 1.3.1 - torchvision: 0.4.2 - mmcv: "latest+torch1.3.0+cpu" - torch: 1.5.1 torchvision: 0.6.1 - mmcv: "latest+torch1.5.0+cpu" + mmcv: 1.5.0 - torch: 1.6.0 torchvision: 0.7.0 - mmcv: "latest+torch1.6.0+cpu" + mmcv: 1.6.0 - torch: 1.7.0 torchvision: 0.8.1 - mmcv: "latest+torch1.7.0+cpu" + mmcv: 1.7.0 - torch: 1.8.0 torchvision: 0.9.0 - mmcv: "latest+torch1.8.0+cpu" + mmcv: 1.8.0 - torch: 1.9.0 torchvision: 0.10.0 - mmcv: "latest+torch1.9.0+cpu" + mmcv: 1.9.0 steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.python-version }} @@ -62,7 +59,7 @@ jobs: run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html - name: Install MMCV run: | - pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch${{matrix.torch}}/index.html + pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch${{matrix.mmcv}}/index.html python -c 'import mmcv; print(mmcv.__version__)' - name: Install unittest dependencies run: | @@ -93,33 +90,28 @@ jobs: python-version: [3.7] torch: [ - 1.3.1, 1.5.1+cu101, 1.6.0+cu101, 1.7.0+cu101, 1.8.0+cu101 ] include: - - torch: 1.3.1 - torch_version: torch1.3.1 - torchvision: 0.4.2 - mmcv_link: "torch1.3.0" - torch: 1.5.1+cu101 torch_version: torch1.5.1 torchvision: 0.6.1+cu101 - mmcv_link: "torch1.5.0" + mmcv: 1.5.0 - torch: 1.6.0+cu101 torch_version: torch1.6.0 torchvision: 0.7.0+cu101 - mmcv_link: "torch1.6.0" + mmcv: 1.6.0 - torch: 1.7.0+cu101 torch_version: torch1.7.0 torchvision: 0.8.1+cu101 - mmcv_link: "torch1.7.0" + mmcv: 1.7.0 - torch: 1.8.0+cu101 torch_version: torch1.8.0 torchvision: 0.9.0+cu101 - mmcv_link: "torch1.8.0" + mmcv: 1.8.0 steps: - uses: actions/checkout@v2 @@ -140,7 +132,7 @@ jobs: - name: Install mmseg dependencies run: | python -V - python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/${{matrix.mmcv_link}}/index.html + python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch${{matrix.mmcv}}/index.html python -m pip install -r requirements.txt python -c 'import mmcv; print(mmcv.__version__)' - name: Build and install @@ -183,7 +175,7 @@ jobs: - torch: 1.9.0+cu102 torch_version: torch1.9.0 torchvision: 0.10.0+cu102 - mmcv_link: "torch1.9.0" + mmcv_link: 1.9.0 steps: - uses: actions/checkout@v2 @@ -204,7 +196,7 @@ jobs: - name: Install mmseg dependencies run: | python -V - python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/${{matrix.mmcv_link}}/index.html + python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch${{matrix.mmcv_link}}/index.html python -m pip install -r requirements.txt python -c 'import mmcv; print(mmcv.__version__)' - name: Build and install diff --git a/README.md b/README.md index 1148c1e51..8f4d8573c 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,7 @@ English | [简体中文](README_zh-CN.md) MMSegmentation is an open source semantic segmentation toolbox based on PyTorch. It is a part of the OpenMMLab project. -The master branch works with **PyTorch 1.3+**. +The master branch works with **PyTorch 1.5+**. ![demo image](resources/seg_demo.gif) @@ -114,6 +114,7 @@ Supported datasets: - [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#stare) - [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#dark-zurich) - [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#nighttime-driving) +- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#loveda) ## Installation diff --git a/README_zh-CN.md b/README_zh-CN.md index a5206fc9d..fa48aff05 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -20,7 +20,7 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 OpenMMLab 项目的一部分。 -主分支代码目前支持 PyTorch 1.3 以上的版本。 +主分支代码目前支持 PyTorch 1.5 以上的版本。 ![示例图片](resources/seg_demo.gif) @@ -101,18 +101,19 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O 已支持的数据集： -- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#cityscapes) -- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#pascal-voc) -- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#ade20k) -- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#pascal-context) -- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#coco-stuff-10k) -- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#coco-stuff-164k) -- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#chase-db1) -- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#drive) -- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#hrf) -- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#stare) -- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#dark-zurich) -- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#nighttime-driving) +- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#cityscapes) +- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#pascal-voc) +- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#ade20k) +- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#pascal-context) +- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#coco-stuff-10k) +- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#coco-stuff-164k) +- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#chase-db1) +- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#drive) +- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#hrf) +- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#stare) +- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#dark-zurich) +- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#nighttime-driving) +- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#loveda) ## 安装 diff --git a/configs/deeplabv3plus/README.md b/configs/deeplabv3plus/README.md index 7efff620c..ba3a9a6a0 100644 --- a/configs/deeplabv3plus/README.md +++ b/configs/deeplabv3plus/README.md @@ -62,7 +62,7 @@ | DeepLabV3+ | R-50-D8 | 512x512 | 160000 | - | - | 43.95 | 44.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504.log.json) | | DeepLabV3+ | R-101-D8 | 512x512 | 160000 | - | - | 45.47 | 46.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232.log.json) | -#### Pascal VOC 2012 + Aug +### Pascal VOC 2012 + Aug | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | | ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | @@ -71,21 +71,21 @@ | DeepLabV3+ | R-50-D8 | 512x512 | 40000 | - | - | 76.81 | 77.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759.log.json) | | DeepLabV3+ | R-101-D8 | 512x512 | 40000 | - | - | 78.62 | 79.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333.log.json) | -#### Pascal Context +### Pascal Context | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | | ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | DeepLabV3+ | R-101-D8 | 480x480 | 40000 | - | 9.09 | 47.30 | 48.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context-20200911_165459.log.json) | | DeepLabV3+ | R-101-D8 | 480x480 | 80000 | - | - | 47.23 | 48.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context-20200911_155322.log.json) | -#### Pascal Context 59 +### Pascal Context 59 | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | | ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | DeepLabV3+ | R-101-D8 | 480x480 | 40000 | - | - | 52.86 | 54.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59_20210416_111233-ed937f15.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59-20210416_111233.log.json) | | DeepLabV3+ | R-101-D8 | 480x480 | 80000 | - | - | 53.2 | 54.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59-20210416_111127.log.json) | -#### LoveDA +### LoveDA | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | | ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | diff --git a/configs/deeplabv3plus/deeplabv3plus.yml b/configs/deeplabv3plus/deeplabv3plus.yml index 93210ddc3..637b8134e 100644 --- a/configs/deeplabv3plus/deeplabv3plus.yml +++ b/configs/deeplabv3plus/deeplabv3plus.yml @@ -4,6 +4,10 @@ Collections: Training Data: - Cityscapes - ADE20K + - Pascal VOC 2012 + Aug + - Pascal Context + - Pascal Context 59 + - LoveDA Paper: URL: https://arxiv.org/abs/1802.02611 Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation @@ -480,7 +484,7 @@ Models: memory (GB): 7.6 Results: - Task: Semantic Segmentation - Dataset: ADE20K + Dataset: Pascal VOC 2012 + Aug Metrics: mIoU: 75.93 mIoU(ms+flip): 77.5 @@ -502,7 +506,7 @@ Models: memory (GB): 11.0 Results: - Task: Semantic Segmentation - Dataset: ADE20K + Dataset: Pascal VOC 2012 + Aug Metrics: mIoU: 77.22 mIoU(ms+flip): 78.59 @@ -516,7 +520,7 @@ Models: lr schd: 40000 Results: - Task: Semantic Segmentation - Dataset: ADE20K + Dataset: Pascal VOC 2012 + Aug Metrics: mIoU: 76.81 mIoU(ms+flip): 77.57 @@ -530,7 +534,7 @@ Models: lr schd: 40000 Results: - Task: Semantic Segmentation - Dataset: ADE20K + Dataset: Pascal VOC 2012 + Aug Metrics: mIoU: 78.62 mIoU(ms+flip): 79.53 @@ -551,7 +555,7 @@ Models: resolution: (480,480) Results: - Task: Semantic Segmentation - Dataset: ADE20K + Dataset: Pascal Context Metrics: mIoU: 47.3 mIoU(ms+flip): 48.47 @@ -565,7 +569,7 @@ Models: lr schd: 80000 Results: - Task: Semantic Segmentation - Dataset: ADE20K + Dataset: Pascal Context Metrics: mIoU: 47.23 mIoU(ms+flip): 48.26 @@ -579,7 +583,7 @@ Models: lr schd: 40000 Results: - Task: Semantic Segmentation - Dataset: ADE20K + Dataset: Pascal Context 59 Metrics: mIoU: 52.86 mIoU(ms+flip): 54.54 @@ -593,7 +597,7 @@ Models: lr schd: 80000 Results: - Task: Semantic Segmentation - Dataset: ADE20K + Dataset: Pascal Context 59 Metrics: mIoU: 53.2 mIoU(ms+flip): 54.67 @@ -615,7 +619,7 @@ Models: memory (GB): 1.93 Results: - Task: Semantic Segmentation - Dataset: ADE20K + Dataset: LoveDA Metrics: mIoU: 50.28 mIoU(ms+flip): 50.47 @@ -637,7 +641,7 @@ Models: memory (GB): 7.37 Results: - Task: Semantic Segmentation - Dataset: ADE20K + Dataset: LoveDA Metrics: mIoU: 50.99 mIoU(ms+flip): 50.65 @@ -659,7 +663,7 @@ Models: memory (GB): 10.84 Results: - Task: Semantic Segmentation - Dataset: ADE20K + Dataset: LoveDA Metrics: mIoU: 51.47 mIoU(ms+flip): 51.32 diff --git a/configs/hrnet/README.md b/configs/hrnet/README.md index 2babf2f57..13cf7948c 100644 --- a/configs/hrnet/README.md +++ b/configs/hrnet/README.md @@ -74,7 +74,7 @@ | FCN | HRNetV2p-W48 | 480x480 | 40000 | - | - | 50.33 | 52.83 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59_20210410_122738-b808b8b2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59-20210410_122738.log.json) | | FCN | HRNetV2p-W48 | 480x480 | 80000 | - | - | 51.12 | 53.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59-20210411_003240.log.json) | -#### LoveDA +### LoveDA | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | | ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | diff --git a/configs/hrnet/hrnet.yml b/configs/hrnet/hrnet.yml index 885ed3183..01eec76cd 100644 --- a/configs/hrnet/hrnet.yml +++ b/configs/hrnet/hrnet.yml @@ -7,6 +7,7 @@ Collections: - Pascal VOC 2012 + Aug - Pascal Context - Pascal Context 59 + - LoveDA Paper: URL: https://arxiv.org/abs/1908.07919 Title: Deep High-Resolution Representation Learning for Human Pose Estimation @@ -463,7 +464,7 @@ Models: memory (GB): 1.72 Results: - Task: Semantic Segmentation - Dataset: Pascal Context 59 + Dataset: LoveDA Metrics: mIoU: 49.3 mIoU(ms+flip): 49.23 @@ -485,7 +486,7 @@ Models: memory (GB): 2.9 Results: - Task: Semantic Segmentation - Dataset: Pascal Context 59 + Dataset: LoveDA Metrics: mIoU: 50.87 mIoU(ms+flip): 51.24 @@ -507,7 +508,7 @@ Models: memory (GB): 6.25 Results: - Task: Semantic Segmentation - Dataset: Pascal Context 59 + Dataset: LoveDA Metrics: mIoU: 51.04 mIoU(ms+flip): 51.12 diff --git a/configs/pspnet/README.md b/configs/pspnet/README.md index 995777f84..9ca013691 100644 --- a/configs/pspnet/README.md +++ b/configs/pspnet/README.md @@ -114,7 +114,7 @@ We support evaluation results on these two datasets using models above trained o | PSPNet | R-50-D8 | 512x512 | 320000 | - | - | 40.53 | 40.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k.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 | - | - | 41.95 | 42.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k.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 +### LoveDA | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | | ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | diff --git a/configs/pspnet/pspnet.yml b/configs/pspnet/pspnet.yml index c951269ce..2b6834c20 100644 --- a/configs/pspnet/pspnet.yml +++ b/configs/pspnet/pspnet.yml @@ -10,6 +10,7 @@ Collections: - Dark Zurich and Nighttime Driving - COCO-Stuff 10k - COCO-Stuff 164k + - LoveDA Paper: URL: https://arxiv.org/abs/1612.01105 Title: Pyramid Scene Parsing Network @@ -757,7 +758,7 @@ Models: memory (GB): 1.45 Results: - Task: Semantic Segmentation - Dataset: COCO-Stuff 164k + Dataset: LoveDA Metrics: mIoU: 48.62 mIoU(ms+flip): 47.57 @@ -779,7 +780,7 @@ Models: memory (GB): 6.14 Results: - Task: Semantic Segmentation - Dataset: COCO-Stuff 164k + Dataset: LoveDA Metrics: mIoU: 50.46 mIoU(ms+flip): 50.19 @@ -801,7 +802,7 @@ Models: memory (GB): 9.61 Results: - Task: Semantic Segmentation - Dataset: COCO-Stuff 164k + Dataset: LoveDA Metrics: mIoU: 51.86 mIoU(ms+flip): 51.34