From 7f06e01d37493a0e958fbbf34636f685b8dbaf05 Mon Sep 17 00:00:00 2001 From: MengzhangLI Date: Thu, 23 Dec 2021 21:38:51 +0800 Subject: [PATCH] [Feature] Add UNet benchmark with multiple losses supervision (#1143) * upload models and new configs * fix hrf readme error * fix hrf readme error * add mDice of old models * refactor configs --- configs/unet/README.md | 59 ++++-- ...6_ce-1.0-dice-3.0_128x128_40k_chase-db1.py | 6 + ...5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py | 6 + ..._s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py | 6 + ..._s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py | 6 + ...net_s5-d16_4x4_512x1024_160k_cityscapes.py | 16 ++ ...6_ce-1.0-dice-3.0_128x128_40k_chase-db1.py | 6 + ...5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py | 6 + ..._s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py | 6 + ..._s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py | 6 + ...6_ce-1.0-dice-3.0_128x128_40k_chase-db1.py | 6 + ...5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py | 6 + ..._s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py | 6 + ..._s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py | 6 + configs/unet/unet.yml | 191 ++++++++++++++++++ 15 files changed, 318 insertions(+), 20 deletions(-) create mode 100644 configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py create mode 100644 configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py create mode 100644 configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py create mode 100644 configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py create mode 100644 configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py create mode 100644 configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py create mode 100644 configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py create mode 100644 configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py create mode 100644 configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py create mode 100644 configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py create mode 100644 configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py create mode 100644 configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py create mode 100644 configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py diff --git a/configs/unet/README.md b/configs/unet/README.md index ae61b7451..17b12eef1 100644 --- a/configs/unet/README.md +++ b/configs/unet/README.md @@ -37,34 +37,53 @@ There is large consent that successful training of deep networks requires many t ## Results and models +### Cityscapes + +| Method | Backbone | Loss | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | --------- | --- |--------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| FCN | UNet-S5-D16 | Cross Entropy | 512x1024 | 160000 | 17.91 | 3.05 | 69.10 | 71.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204-6860854e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204.log.json) | + + ### DRIVE -| Method | Backbone | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Dice | config | download | -| ----------- | --------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | ------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| FCN | UNet-S5-D16 | 584x565 | 64x64 | 42x42 | 40000 | 0.680 | - | 78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_64x64_40k_drive/unet_s5-d16_64x64_40k_drive-20201223_191051.log.json) | -| PSPNet | UNet-S5-D16 | 584x565 | 64x64 | 42x42 | 40000 | 0.599 | - | 78.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive-20201227_181818.log.json) | -| DeepLabV3 | UNet-S5-D16 | 584x565 | 64x64 | 42x42 | 40000 | 0.596 | - | 78.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive-20201226_094047.log.json) | +| Method | Backbone | Loss | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | mDice | Dice | config | download | +| ----------- | --------- | -------------------- |---------- | --------- | -----: | ------- | -------- | -------------: | --: |----: | ------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| FCN | UNet-S5-D16 | Cross Entropy | 584x565 | 64x64 | 42x42 | 40000 | 0.680 | - | 88.38 | 78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_64x64_40k_drive/unet_s5-d16_64x64_40k_drive-20201223_191051.log.json) | +| FCN | UNet-S5-D16 | Cross Entropy + Dice | 584x565 | 64x64 | 42x42 | 40000 | 0.582 | - | 88.71 | 79.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820-785de5c2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820.log.json) | +| PSPNet | UNet-S5-D16 | Cross Entropy | 584x565 | 64x64 | 42x42 | 40000 | 0.599 | - | 88.35 | 78.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive-20201227_181818.log.json) | +| PSPNet | UNet-S5-D16 | Cross Entropy + Dice | 584x565 | 64x64 | 42x42 | 40000 | 0.585 | - | 88.76 | 79.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821-22b3e3ba.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821.log.json) | +| DeepLabV3 | UNet-S5-D16 | Cross Entropy | 584x565 | 64x64 | 42x42 | 40000 | 0.596 | - | 88.38 |78.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive-20201226_094047.log.json) | +| DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 584x565 | 64x64 | 42x42 | 40000 | 0.582 | - | 88.84 | 79.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825-6bf0efd7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825.log.json) | ### STARE -| Method | Backbone | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Dice | config | download | -| ----------- | --------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| FCN | UNet-S5-D16 | 605x700 | 128x128 | 85x85 | 40000 | 0.968 | - | 81.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_stare/unet_s5-d16_128x128_40k_stare-20201223_191051.log.json) | -| PSPNet | UNet-S5-D16 | 605x700 | 128x128 | 85x85 | 40000 | 0.982 | - | 81.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare-20201227_181818.log.json) | -| DeepLabV3 | UNet-S5-D16 | 605x700 | 128x128 | 85x85 | 40000 | 0.999 | - | 80.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare-20201226_094047.log.json) | +| Method | Backbone | Loss | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | mDice | Dice | config | download | +| ----------- | --------| --------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | --: |----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| FCN | UNet-S5-D16 | Cross Entropy | 605x700 | 128x128 | 85x85 | 40000 | 0.968 | - | 89.78 | 81.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_stare/unet_s5-d16_128x128_40k_stare-20201223_191051.log.json) | +| FCN | UNet-S5-D16 | Cross Entropy + Dice | 605x700 | 128x128 | 85x85 | 40000 | 0.986 | - | 90.65 | 82.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821-f75705a9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821.log.json) | +| PSPNet | UNet-S5-D16 | Cross Entropy | 605x700 | 128x128 | 85x85 | 40000 | 0.982 | - | 89.89 | 81.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare-20201227_181818.log.json) | +| PSPNet | UNet-S5-D16 | Cross Entropy + Dice | 605x700 | 128x128 | 85x85 | 40000 | 1.028 | - | 90.72 | 82.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823-f1063ef7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823.log.json) | +| DeepLabV3 | UNet-S5-D16 | Cross Entropy | 605x700 | 128x128 | 85x85 | 40000 | 0.999 | - | 89.73 | 80.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare-20201226_094047.log.json) | +| DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 605x700 | 128x128 | 85x85 | 40000 | 1.010 | - | 90.65 | 82.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825-21db614c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825.log.json) | ### CHASE_DB1 -| Method | Backbone | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Dice | config | download | -| ----------- | --------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| FCN | UNet-S5-D16 | 960x999 | 128x128 | 85x85 | 40000 | 0.968 | - | 80.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_chase_db1/unet_s5-d16_128x128_40k_chase_db1-20201223_191051.log.json) | -| PSPNet | UNet-S5-D16 | 960x999 | 128x128 | 85x85 | 40000 | 0.982 | - | 80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1-20201227_181818.log.json) | -| DeepLabV3 | UNet-S5-D16 | 960x999 | 128x128 | 85x85 | 40000 | 0.999 | - | 80.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1-20201226_094047.log.json) | +| Method | Backbone | Loss | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | mDice | Dice | config | download | +| ----------- | --------- | --------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | --: |----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| FCN | UNet-S5-D16 | Cross Entropy | 960x999 | 128x128 | 85x85 | 40000 | 0.968 | - | 89.46 |80.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_chase_db1/unet_s5-d16_128x128_40k_chase_db1-20201223_191051.log.json) | +| FCN | UNet-S5-D16 | Cross Entropy + Dice | 960x999 | 128x128 | 85x85 | 40000 | 0.986 | - | 89.52 | 80.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821-1c4eb7cf.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821.log.json) | +| PSPNet | UNet-S5-D16 | Cross Entropy | 960x999 | 128x128 | 85x85 | 40000 | 0.982 | - | 89.52 |80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1-20201227_181818.log.json) | +| PSPNet | UNet-S5-D16 | Cross Entropy + Dice | 960x999 | 128x128 | 85x85 | 40000 | 1.028 | - | 89.45 | 80.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823-c0802c4d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823.log.json) | +| DeepLabV3 | UNet-S5-D16 | Cross Entropy | 960x999 | 128x128 | 85x85 | 40000 | 0.999 | - | 89.57 |80.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1-20201226_094047.log.json) | +| DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 960x999 | 128x128 | 85x85 | 40000 | 1.010 | - | 89.49 | 80.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825-4ef29df5.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825.log.json) | ### HRF -| Method | Backbone | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Dice | config | download | -| ----------- | --------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| FCN | UNet-S5-D16 | 2336x3504 | 256x256 | 170x170 | 40000 | 2.525 | - | 79.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_256x256_40k_hrf/unet_s5-d16_256x256_40k_hrf-20201223_173724.log.json) | -| PSPNet | UNet-S5-D16 | 2336x3504 | 256x256 | 170x170 | 40000 | 2.588 | - | 80.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf-20201227_181818.log.json) | -| DeepLabV3 | UNet-S5-D16 | 2336x3504 | 256x256 | 170x170 | 40000 | 2.604 | - | 80.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf-20201226_094047.log.json) | +| Method | Backbone | Loss | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | mDice | Dice | config | download | +| ----------- | --------- | --------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | --: |----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| FCN | UNet-S5-D16 | Cross Entropy | 2336x3504 | 256x256 | 170x170 | 40000 | 2.525 | - | 88.92 |79.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_256x256_40k_hrf/unet_s5-d16_256x256_40k_hrf-20201223_173724.log.json) | +| FCN | UNet-S5-D16 | Cross Entropy + Dice | 2336x3504 | 256x256 | 170x170 | 40000 | 2.623 | - | 89.64 | 80.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821-c314da8a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821.log.json) | +| PSPNet | UNet-S5-D16 | Cross Entropy | 2336x3504 | 256x256 | 170x170 | 40000 | 2.588 | - | 89.24 |80.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf-20201227_181818.log.json) | +| PSPNet | UNet-S5-D16 | Cross Entropy + Dice | 2336x3504 | 256x256 | 170x170 | 40000 | 2.798 | - | 89.69 | 80.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823-53d492fa.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823.log.json) | +| DeepLabV3 | UNet-S5-D16| Cross Entropy | 2336x3504 | 256x256 | 170x170 | 40000 | 2.604 | - | 89.32 |80.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf-20201226_094047.log.json) | +| DeepLabV3 | UNet-S5-D16| Cross Entropy + Dice | 2336x3504 | 256x256 | 170x170 | 40000 | 2.607 | - | 89.56 | 80.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032-59daf7a4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032.log.json) | diff --git a/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py b/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py new file mode 100644 index 000000000..1c48cbc22 --- /dev/null +++ b/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py @@ -0,0 +1,6 @@ +_base_ = './deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py' +model = dict( + decode_head=dict(loss_decode=[ + dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) + ])) diff --git a/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py b/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py new file mode 100644 index 000000000..1022edee3 --- /dev/null +++ b/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py @@ -0,0 +1,6 @@ +_base_ = './deeplabv3_unet_s5-d16_128x128_40k_stare.py' +model = dict( + decode_head=dict(loss_decode=[ + dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) + ])) diff --git a/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py b/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py new file mode 100644 index 000000000..fc17da71e --- /dev/null +++ b/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py @@ -0,0 +1,6 @@ +_base_ = './deeplabv3_unet_s5-d16_256x256_40k_hrf.py' +model = dict( + decode_head=dict(loss_decode=[ + dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) + ])) diff --git a/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py b/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py new file mode 100644 index 000000000..3f1f12e61 --- /dev/null +++ b/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py @@ -0,0 +1,6 @@ +_base_ = './deeplabv3_unet_s5-d16_64x64_40k_drive.py' +model = dict( + decode_head=dict(loss_decode=[ + dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) + ])) diff --git a/configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py b/configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py new file mode 100644 index 000000000..a2f7dbe3f --- /dev/null +++ b/configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py @@ -0,0 +1,16 @@ +_base_ = [ + '../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/cityscapes.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] + +model = dict( + decode_head=dict(num_classes=19), + auxiliary_head=dict(num_classes=19), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + +data = dict( + samples_per_gpu=4, + workers_per_gpu=4, +) diff --git a/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py b/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py new file mode 100644 index 000000000..526486629 --- /dev/null +++ b/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py @@ -0,0 +1,6 @@ +_base_ = './fcn_unet_s5-d16_128x128_40k_chase_db1.py' +model = dict( + decode_head=dict(loss_decode=[ + dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) + ])) diff --git a/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py b/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py new file mode 100644 index 000000000..cf5fa1f0d --- /dev/null +++ b/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py @@ -0,0 +1,6 @@ +_base_ = './fcn_unet_s5-d16_128x128_40k_stare.py' +model = dict( + decode_head=dict(loss_decode=[ + dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) + ])) diff --git a/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py b/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py new file mode 100644 index 000000000..a154d7e68 --- /dev/null +++ b/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py @@ -0,0 +1,6 @@ +_base_ = './fcn_unet_s5-d16_256x256_40k_hrf.py' +model = dict( + decode_head=dict(loss_decode=[ + dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) + ])) diff --git a/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py b/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py new file mode 100644 index 000000000..1b8f860bf --- /dev/null +++ b/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py @@ -0,0 +1,6 @@ +_base_ = './fcn_unet_s5-d16_64x64_40k_drive.py' +model = dict( + decode_head=dict(loss_decode=[ + dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) + ])) diff --git a/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py b/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py new file mode 100644 index 000000000..a63dc11d5 --- /dev/null +++ b/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py @@ -0,0 +1,6 @@ +_base_ = './pspnet_unet_s5-d16_128x128_40k_chase_db1.py' +model = dict( + decode_head=dict(loss_decode=[ + dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) + ])) diff --git a/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py b/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py new file mode 100644 index 000000000..1a3b66582 --- /dev/null +++ b/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py @@ -0,0 +1,6 @@ +_base_ = './pspnet_unet_s5-d16_128x128_40k_stare.py' +model = dict( + decode_head=dict(loss_decode=[ + dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) + ])) diff --git a/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py b/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py new file mode 100644 index 000000000..e19d6cf42 --- /dev/null +++ b/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py @@ -0,0 +1,6 @@ +_base_ = './pspnet_unet_s5-d16_256x256_40k_hrf.py' +model = dict( + decode_head=dict(loss_decode=[ + dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) + ])) diff --git a/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py b/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py new file mode 100644 index 000000000..793492375 --- /dev/null +++ b/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py @@ -0,0 +1,6 @@ +_base_ = './pspnet_unet_s5-d16_64x64_40k_drive.py' +model = dict( + decode_head=dict(loss_decode=[ + dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) + ])) diff --git a/configs/unet/unet.yml b/configs/unet/unet.yml index be81163dc..4edafc546 100644 --- a/configs/unet/unet.yml +++ b/configs/unet/unet.yml @@ -2,6 +2,7 @@ Collections: - Name: unet Metadata: Training Data: + - Cityscapes - DRIVE - STARE - CHASE_DB1 @@ -16,6 +17,28 @@ Collections: Converted From: Code: http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net Models: +- Name: fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes + In Collection: unet + Metadata: + backbone: UNet-S5-D16 + crop size: (512,1024) + lr schd: 160000 + inference time (ms/im): + - value: 327.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,1024) + Training Memory (GB): 17.91 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 69.1 + mIoU(ms+flip): 71.05 + Config: configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204-6860854e.pth - Name: fcn_unet_s5-d16_64x64_40k_drive In Collection: unet Metadata: @@ -30,6 +53,20 @@ Models: Dice: 78.67 Config: configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth +- Name: fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive + In Collection: unet + Metadata: + backbone: UNet-S5-D16 + crop size: (64,64) + lr schd: 40000 + Training Memory (GB): 0.582 + Results: + - Task: Semantic Segmentation + Dataset: DRIVE + Metrics: + Dice: 79.32 + Config: configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820-785de5c2.pth - Name: pspnet_unet_s5-d16_64x64_40k_drive In Collection: unet Metadata: @@ -44,6 +81,20 @@ Models: Dice: 78.62 Config: configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth +- Name: pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive + In Collection: unet + Metadata: + backbone: UNet-S5-D16 + crop size: (64,64) + lr schd: 40000 + Training Memory (GB): 0.585 + Results: + - Task: Semantic Segmentation + Dataset: DRIVE + Metrics: + Dice: 79.42 + Config: configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821-22b3e3ba.pth - Name: deeplabv3_unet_s5-d16_64x64_40k_drive In Collection: unet Metadata: @@ -58,6 +109,20 @@ Models: Dice: 78.69 Config: configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth +- Name: deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive + In Collection: unet + Metadata: + backbone: UNet-S5-D16 + crop size: (64,64) + lr schd: 40000 + Training Memory (GB): 0.582 + Results: + - Task: Semantic Segmentation + Dataset: DRIVE + Metrics: + Dice: 79.56 + Config: configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825-6bf0efd7.pth - Name: fcn_unet_s5-d16_128x128_40k_stare In Collection: unet Metadata: @@ -72,6 +137,20 @@ Models: Dice: 81.02 Config: configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth +- Name: fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare + In Collection: unet + Metadata: + backbone: UNet-S5-D16 + crop size: (128,128) + lr schd: 40000 + Training Memory (GB): 0.986 + Results: + - Task: Semantic Segmentation + Dataset: STARE + Metrics: + Dice: 82.7 + Config: configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821-f75705a9.pth - Name: pspnet_unet_s5-d16_128x128_40k_stare In Collection: unet Metadata: @@ -86,6 +165,20 @@ Models: Dice: 81.22 Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth +- Name: pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare + In Collection: unet + Metadata: + backbone: UNet-S5-D16 + crop size: (128,128) + lr schd: 40000 + Training Memory (GB): 1.028 + Results: + - Task: Semantic Segmentation + Dataset: STARE + Metrics: + Dice: 82.84 + Config: configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823-f1063ef7.pth - Name: deeplabv3_unet_s5-d16_128x128_40k_stare In Collection: unet Metadata: @@ -100,6 +193,20 @@ Models: Dice: 80.93 Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth +- Name: deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare + In Collection: unet + Metadata: + backbone: UNet-S5-D16 + crop size: (128,128) + lr schd: 40000 + Training Memory (GB): 1.01 + Results: + - Task: Semantic Segmentation + Dataset: STARE + Metrics: + Dice: 82.71 + Config: configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825-21db614c.pth - Name: fcn_unet_s5-d16_128x128_40k_chase_db1 In Collection: unet Metadata: @@ -114,6 +221,20 @@ Models: Dice: 80.24 Config: configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth +- Name: fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1 + In Collection: unet + Metadata: + backbone: UNet-S5-D16 + crop size: (128,128) + lr schd: 40000 + Training Memory (GB): 0.986 + Results: + - Task: Semantic Segmentation + Dataset: CHASE_DB1 + Metrics: + Dice: 80.4 + Config: configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821-1c4eb7cf.pth - Name: pspnet_unet_s5-d16_128x128_40k_chase_db1 In Collection: unet Metadata: @@ -128,6 +249,20 @@ Models: Dice: 80.36 Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth +- Name: pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1 + In Collection: unet + Metadata: + backbone: UNet-S5-D16 + crop size: (128,128) + lr schd: 40000 + Training Memory (GB): 1.028 + Results: + - Task: Semantic Segmentation + Dataset: CHASE_DB1 + Metrics: + Dice: 80.28 + Config: configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823-c0802c4d.pth - Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1 In Collection: unet Metadata: @@ -142,6 +277,20 @@ Models: Dice: 80.47 Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth +- Name: deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1 + In Collection: unet + Metadata: + backbone: UNet-S5-D16 + crop size: (128,128) + lr schd: 40000 + Training Memory (GB): 1.01 + Results: + - Task: Semantic Segmentation + Dataset: CHASE_DB1 + Metrics: + Dice: 80.37 + Config: configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825-4ef29df5.pth - Name: fcn_unet_s5-d16_256x256_40k_hrf In Collection: unet Metadata: @@ -156,6 +305,20 @@ Models: Dice: 79.45 Config: configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth +- Name: fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf + In Collection: unet + Metadata: + backbone: UNet-S5-D16 + crop size: (256,256) + lr schd: 40000 + Training Memory (GB): 2.623 + Results: + - Task: Semantic Segmentation + Dataset: HRF + Metrics: + Dice: 80.87 + Config: configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py + Weights: https://download.openmmlab.com/mmsegmentation/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821-c314da8a.pth - Name: pspnet_unet_s5-d16_256x256_40k_hrf In Collection: unet Metadata: @@ -170,6 +333,20 @@ Models: Dice: 80.07 Config: configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth +- Name: pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf + In Collection: unet + Metadata: + backbone: UNet-S5-D16 + crop size: (256,256) + lr schd: 40000 + Training Memory (GB): 2.798 + Results: + - Task: Semantic Segmentation + Dataset: HRF + Metrics: + Dice: 80.96 + Config: configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823-53d492fa.pth - Name: deeplabv3_unet_s5-d16_256x256_40k_hrf In Collection: unet Metadata: @@ -184,3 +361,17 @@ Models: Dice: 80.21 Config: configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth +- Name: deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf + In Collection: unet + Metadata: + backbone: UNet-S5-D16 + crop size: (256,256) + lr schd: 40000 + Training Memory (GB): 2.607 + Results: + - Task: Semantic Segmentation + Dataset: HRF + Metrics: + Dice: 80.71 + Config: configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032-59daf7a4.pth