diff --git a/configs/upernet/README.md b/configs/upernet/README.md index dc8eadc6c6..d398ddc9f6 100644 --- a/configs/upernet/README.md +++ b/configs/upernet/README.md @@ -40,10 +40,12 @@ Humans recognize the visual world at multiple levels: we effortlessly categorize | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | | ------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| UPerNet | R-18 | 512x1024 | 40000 | 4.8 | 4.47 | 75.39 | 77.0 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r18_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x1024_40k_cityscapes/upernet_r18_512x1024_40k_cityscapes_20220615_113231-12ee861d.pth) \|[log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x1024_40k_cityscapes/upernet_r18_512x1024_40k_cityscapes_20220615_113231.log.json) | | UPerNet | R-50 | 512x1024 | 40000 | 6.4 | 4.25 | 77.10 | 78.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827.log.json) | | UPerNet | R-101 | 512x1024 | 40000 | 7.4 | 3.79 | 78.69 | 80.11 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933.log.json) | | UPerNet | R-50 | 769x769 | 40000 | 7.2 | 1.76 | 77.98 | 79.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048.log.json) | | UPerNet | R-101 | 769x769 | 40000 | 8.4 | 1.56 | 79.03 | 80.77 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819.log.json) | +| UPerNet | R-18 | 512x1024 | 80000 | - | - | 76.02 | 77.38 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r18_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x1024_80k_cityscapes/upernet_r18_512x1024_80k_cityscapes_20220614_110712-c89a9188.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x1024_80k_cityscapes/upernet_r18_512x1024_80k_cityscapes_20220614_110712.log.json) | | UPerNet | R-50 | 512x1024 | 80000 | - | - | 78.19 | 79.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207.log.json) | | UPerNet | R-101 | 512x1024 | 80000 | - | - | 79.40 | 80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403.log.json) | | UPerNet | R-50 | 769x769 | 80000 | - | - | 79.39 | 80.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107.log.json) | @@ -53,8 +55,10 @@ Humans recognize the visual world at multiple levels: we effortlessly categorize | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | | ------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| UPerNet | R-18 | 512x512 | 80000 | 6.6 | 24.76 | 38.76 | 39.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r18_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_80k_ade20k/upernet_r18_512x512_80k_ade20k_20220614_110319-22e81719.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_80k_ade20k/upernet_r18_512x512_80k_ade20k_20220614_110319.log.json) | | UPerNet | R-50 | 512x512 | 80000 | 8.1 | 23.40 | 40.70 | 41.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127.log.json) | | UPerNet | R-101 | 512x512 | 80000 | 9.1 | 20.34 | 42.91 | 43.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117.log.json) | +| UPerNet | R-18 | 512x512 | 160000 | - | - | 39.23 | 39.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r18_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_160k_ade20k/upernet_r18_512x512_160k_ade20k_20220615_113300-791c3f3e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_160k_ade20k/upernet_r18_512x512_160k_ade20k_20220615_113300.log.json) | | UPerNet | R-50 | 512x512 | 160000 | - | - | 42.05 | 42.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328.log.json) | | UPerNet | R-101 | 512x512 | 160000 | - | - | 43.82 | 44.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951.log.json) | @@ -62,7 +66,9 @@ Humans recognize the visual world at multiple levels: we effortlessly categorize | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | | ------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| UPerNet | R-18 | 512x512 | 20000 | 4.8 | 25.80 | 72.9 | 74.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r18_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_20k_voc12aug/upernet_r18_512x512_20k_voc12aug_20220614_123910-ed66e455.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_20k_voc12aug/upernet_r18_512x512_20k_voc12aug_20220614_123910.log.json) | | UPerNet | R-50 | 512x512 | 20000 | 6.4 | 23.17 | 74.82 | 76.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330.log.json) | | UPerNet | R-101 | 512x512 | 20000 | 7.5 | 19.98 | 77.10 | 78.29 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629.log.json) | +| UPerNet | R-18 | 512x512 | 40000 | - | - | 73.71 | 74.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r18_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_40k_voc12aug/upernet_r18_512x512_40k_voc12aug_20220614_153605-fafeb868.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_40k_voc12aug/upernet_r18_512x512_40k_voc12aug_20220614_153605.log.json) | | UPerNet | R-50 | 512x512 | 40000 | - | - | 75.92 | 77.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257.log.json) | | UPerNet | R-101 | 512x512 | 40000 | - | - | 77.43 | 78.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549.log.json) | diff --git a/configs/upernet/upernet.yml b/configs/upernet/upernet.yml index 7c3872a8dd..0d82e7269b 100644 --- a/configs/upernet/upernet.yml +++ b/configs/upernet/upernet.yml @@ -15,6 +15,28 @@ Collections: Converted From: Code: https://github.com/CSAILVision/unifiedparsing Models: +- Name: upernet_r18_512x1024_40k_cityscapes + In Collection: UPerNet + Metadata: + backbone: R-18 + crop size: (512,1024) + lr schd: 40000 + inference time (ms/im): + - value: 223.71 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,1024) + Training Memory (GB): 4.8 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 75.39 + mIoU(ms+flip): 77.0 + Config: configs/upernet/upernet_r18_512x1024_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x1024_40k_cityscapes/upernet_r18_512x1024_40k_cityscapes_20220615_113231-12ee861d.pth - Name: upernet_r50_512x1024_40k_cityscapes In Collection: UPerNet Metadata: @@ -103,6 +125,20 @@ Models: mIoU(ms+flip): 80.77 Config: configs/upernet/upernet_r101_769x769_40k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth +- Name: upernet_r18_512x1024_80k_cityscapes + In Collection: UPerNet + Metadata: + backbone: R-18 + crop size: (512,1024) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.02 + mIoU(ms+flip): 77.38 + Config: configs/upernet/upernet_r18_512x1024_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x1024_80k_cityscapes/upernet_r18_512x1024_80k_cityscapes_20220614_110712-c89a9188.pth - Name: upernet_r50_512x1024_80k_cityscapes In Collection: UPerNet Metadata: @@ -159,6 +195,28 @@ Models: mIoU(ms+flip): 81.49 Config: configs/upernet/upernet_r101_769x769_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth +- Name: upernet_r18_512x512_80k_ade20k + In Collection: UPerNet + Metadata: + backbone: R-18 + crop size: (512,512) + lr schd: 80000 + inference time (ms/im): + - value: 40.39 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 6.6 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 38.76 + mIoU(ms+flip): 39.81 + Config: configs/upernet/upernet_r18_512x512_80k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_80k_ade20k/upernet_r18_512x512_80k_ade20k_20220614_110319-22e81719.pth - Name: upernet_r50_512x512_80k_ade20k In Collection: UPerNet Metadata: @@ -203,6 +261,20 @@ Models: mIoU(ms+flip): 43.96 Config: configs/upernet/upernet_r101_512x512_80k_ade20k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth +- Name: upernet_r18_512x512_160k_ade20k + In Collection: UPerNet + Metadata: + backbone: R-18 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 39.23 + mIoU(ms+flip): 39.97 + Config: configs/upernet/upernet_r18_512x512_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_160k_ade20k/upernet_r18_512x512_160k_ade20k_20220615_113300-791c3f3e.pth - Name: upernet_r50_512x512_160k_ade20k In Collection: UPerNet Metadata: @@ -231,6 +303,28 @@ Models: mIoU(ms+flip): 44.85 Config: configs/upernet/upernet_r101_512x512_160k_ade20k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth +- Name: upernet_r18_512x512_20k_voc12aug + In Collection: UPerNet + Metadata: + backbone: R-18 + crop size: (512,512) + lr schd: 20000 + inference time (ms/im): + - value: 38.76 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 4.8 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 72.9 + mIoU(ms+flip): 74.71 + Config: configs/upernet/upernet_r18_512x512_20k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_20k_voc12aug/upernet_r18_512x512_20k_voc12aug_20220614_123910-ed66e455.pth - Name: upernet_r50_512x512_20k_voc12aug In Collection: UPerNet Metadata: @@ -275,6 +369,20 @@ Models: mIoU(ms+flip): 78.29 Config: configs/upernet/upernet_r101_512x512_20k_voc12aug.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth +- Name: upernet_r18_512x512_40k_voc12aug + In Collection: UPerNet + Metadata: + backbone: R-18 + crop size: (512,512) + lr schd: 40000 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 73.71 + mIoU(ms+flip): 74.61 + Config: configs/upernet/upernet_r18_512x512_40k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_40k_voc12aug/upernet_r18_512x512_40k_voc12aug_20220614_153605-fafeb868.pth - Name: upernet_r50_512x512_40k_voc12aug In Collection: UPerNet Metadata: diff --git a/configs/upernet/upernet_r18_512x1024_40k_cityscapes.py b/configs/upernet/upernet_r18_512x1024_40k_cityscapes.py new file mode 100644 index 0000000000..f5aec1f81d --- /dev/null +++ b/configs/upernet/upernet_r18_512x1024_40k_cityscapes.py @@ -0,0 +1,6 @@ +_base_ = './upernet_r50_512x1024_40k_cityscapes.py' +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict(in_channels=[64, 128, 256, 512]), + auxiliary_head=dict(in_channels=256)) diff --git a/configs/upernet/upernet_r18_512x1024_80k_cityscapes.py b/configs/upernet/upernet_r18_512x1024_80k_cityscapes.py new file mode 100644 index 0000000000..444f3625ca --- /dev/null +++ b/configs/upernet/upernet_r18_512x1024_80k_cityscapes.py @@ -0,0 +1,6 @@ +_base_ = './upernet_r50_512x1024_80k_cityscapes.py' +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict(in_channels=[64, 128, 256, 512]), + auxiliary_head=dict(in_channels=256)) diff --git a/configs/upernet/upernet_r18_512x512_160k_ade20k.py b/configs/upernet/upernet_r18_512x512_160k_ade20k.py new file mode 100644 index 0000000000..9ac6c35527 --- /dev/null +++ b/configs/upernet/upernet_r18_512x512_160k_ade20k.py @@ -0,0 +1,9 @@ +_base_ = [ + '../_base_/models/upernet_r50.py', '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict(in_channels=[64, 128, 256, 512], num_classes=150), + auxiliary_head=dict(in_channels=256, num_classes=150)) diff --git a/configs/upernet/upernet_r18_512x512_20k_voc12aug.py b/configs/upernet/upernet_r18_512x512_20k_voc12aug.py new file mode 100644 index 0000000000..5cae4f5435 --- /dev/null +++ b/configs/upernet/upernet_r18_512x512_20k_voc12aug.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/upernet_r50.py', + '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_20k.py' +] +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict(in_channels=[64, 128, 256, 512], num_classes=21), + auxiliary_head=dict(in_channels=256, num_classes=21)) diff --git a/configs/upernet/upernet_r18_512x512_40k_voc12aug.py b/configs/upernet/upernet_r18_512x512_40k_voc12aug.py new file mode 100644 index 0000000000..652ded7516 --- /dev/null +++ b/configs/upernet/upernet_r18_512x512_40k_voc12aug.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/upernet_r50.py', + '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_40k.py' +] +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict(in_channels=[64, 128, 256, 512], num_classes=21), + auxiliary_head=dict(in_channels=256, num_classes=21)) diff --git a/configs/upernet/upernet_r18_512x512_80k_ade20k.py b/configs/upernet/upernet_r18_512x512_80k_ade20k.py new file mode 100644 index 0000000000..1a7956d71f --- /dev/null +++ b/configs/upernet/upernet_r18_512x512_80k_ade20k.py @@ -0,0 +1,9 @@ +_base_ = [ + '../_base_/models/upernet_r50.py', '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict(in_channels=[64, 128, 256, 512], num_classes=150), + auxiliary_head=dict(in_channels=256, num_classes=150))