diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000000..a1a7c9f8f5 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,5 @@ + +include requirements/*.txt +include mmseg/model_zoo.yml +recursive-include mmseg/configs *.py *.yml +recursive-include mmseg/tools *.sh *.py diff --git a/configs/ann/metafile.yml b/configs/ann/metafile.yml new file mode 100644 index 0000000000..17959f4282 --- /dev/null +++ b/configs/ann/metafile.yml @@ -0,0 +1,231 @@ +Collections: + - Name: ANN + Metadata: + Training Data: + - Cityscapes + - Pascal VOC 2012 + Aug + - ADE20K + +Models: + + - Name: ann_r50-d8_512x1024_40k_cityscapes + In Collection: ANN + Metadata: + inference time (fps): 3.71 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.40 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth + Config: configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py + + + + - Name: ann_r101-d8_512x1024_40k_cityscapes + In Collection: ANN + Metadata: + inference time (fps): 2.55 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.55 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth + Config: configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py + + + + - Name: ann_r50-d8_769x769_40k_cityscapes + In Collection: ANN + Metadata: + inference time (fps): 1.70 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.89 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth + Config: configs/ann/ann_r50-d8_769x769_40k_cityscapes.py + + + + - Name: ann_r101-d8_769x769_40k_cityscapes + In Collection: ANN + Metadata: + inference time (fps): 1.15 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.32 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth + Config: configs/ann/ann_r101-d8_769x769_40k_cityscapes.py + + + + - Name: ann_r50-d8_512x1024_80k_cityscapes + In Collection: ANN + Metadata: + inference time (fps): 3.71 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.34 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth + Config: configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py + + + + - Name: ann_r101-d8_512x1024_80k_cityscapes + In Collection: ANN + Metadata: + inference time (fps): 2.55 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.14 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth + Config: configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py + + + + - Name: ann_r50-d8_769x769_80k_cityscapes + In Collection: ANN + Metadata: + inference time (fps): 1.70 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.88 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth + Config: configs/ann/ann_r50-d8_769x769_80k_cityscapes.py + + + + - Name: ann_r101-d8_769x769_80k_cityscapes + In Collection: ANN + Metadata: + inference time (fps): + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.80 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth + Config: configs/ann/ann_r101-d8_769x769_80k_cityscapes.py + + + + - Name: ann_r50-d8_512x512_80k_ade20k + In Collection: ANN + Metadata: + inference time (fps): 21.01 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 41.01 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth + Config: configs/ann/ann_r50-d8_512x512_80k_ade20k.py + + + + - Name: ann_r101-d8_512x512_80k_ade20k + In Collection: ANN + Metadata: + inference time (fps): 14.12 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.94 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth + Config: configs/ann/ann_r101-d8_512x512_80k_ade20k.py + + + + - Name: ann_r50-d8_512x512_160k_ade20k + In Collection: ANN + Metadata: + inference time (fps): 21.01 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 41.74 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth + Config: configs/ann/ann_r50-d8_512x512_160k_ade20k.py + + + + - Name: ann_r101-d8_512x512_160k_ade20k + In Collection: ANN + Metadata: + inference time (fps): 14.12 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.94 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth + Config: configs/ann/ann_r101-d8_512x512_160k_ade20k.py + + + + - Name: ann_r50-d8_512x512_20k_voc12aug + In Collection: ANN + Metadata: + inference time (fps): 20.92 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 74.86 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth + Config: configs/ann/ann_r50-d8_512x512_20k_voc12aug.py + + + + - Name: ann_r101-d8_512x512_20k_voc12aug + In Collection: ANN + Metadata: + inference time (fps): 13.94 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.47 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth + Config: configs/ann/ann_r101-d8_512x512_20k_voc12aug.py + + + + - Name: ann_r50-d8_512x512_40k_voc12aug + In Collection: ANN + Metadata: + inference time (fps): 20.92 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.56 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth + Config: configs/ann/ann_r50-d8_512x512_40k_voc12aug.py + + + + - Name: ann_r101-d8_512x512_40k_voc12aug + In Collection: ANN + Metadata: + inference time (fps): 13.94 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.70 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314-bd205bbe.pth + Config: configs/ann/ann_r101-d8_512x512_40k_voc12aug.py diff --git a/configs/apcnet/metafile.yml b/configs/apcnet/metafile.yml new file mode 100644 index 0000000000..de3ab01729 --- /dev/null +++ b/configs/apcnet/metafile.yml @@ -0,0 +1,174 @@ +Collections: + - Name: APCNet + Metadata: + Training Data: + - Cityscapes + - ADE20K + +Models: + + - Name: apcnet_r50-d8_512x1024_40k_cityscapes + In Collection: APCNet + Metadata: + inference time (fps): 3.57 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.02 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth + Config: configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py + + + + - Name: apcnet_r101-d8_512x1024_40k_cityscapes + In Collection: APCNet + Metadata: + inference time (fps): 2.15 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.08 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth + Config: configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py + + + + - Name: apcnet_r50-d8_769x769_40k_cityscapes + In Collection: APCNet + Metadata: + inference time (fps): 1.52 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.89 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth + Config: configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py + + + + - Name: apcnet_r101-d8_769x769_40k_cityscapes + In Collection: APCNet + Metadata: + inference time (fps): 1.03 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.96 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth + Config: configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py + + + + - Name: apcnet_r50-d8_512x1024_80k_cityscapes + In Collection: APCNet + Metadata: + inference time (fps): 3.57 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.96 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth + Config: configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py + + + + - Name: apcnet_r101-d8_512x1024_80k_cityscapes + In Collection: APCNet + Metadata: + inference time (fps): 2.15 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.64 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth + Config: configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py + + + + - Name: apcnet_r50-d8_769x769_80k_cityscapes + In Collection: APCNet + Metadata: + inference time (fps): 1.52 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.79 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth + Config: configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py + + + + - Name: apcnet_r101-d8_769x769_80k_cityscapes + In Collection: APCNet + Metadata: + inference time (fps): 1.03 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.45 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth + Config: configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py + + + + - Name: apcnet_r50-d8_512x512_80k_ade20k + In Collection: APCNet + Metadata: + inference time (fps): 19.61 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.20 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth + Config: configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py + + + + - Name: apcnet_r101-d8_512x512_80k_ade20k + In Collection: APCNet + Metadata: + inference time (fps): 13.10 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 45.54 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth + Config: configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py + + + + - Name: apcnet_r50-d8_512x512_160k_ade20k + In Collection: APCNet + Metadata: + inference time (fps): 19.61 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.40 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth + Config: configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py + + + + - Name: apcnet_r101-d8_512x512_160k_ade20k + In Collection: APCNet + Metadata: + inference time (fps): 13.10 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 45.41 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth + Config: configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py diff --git a/configs/ccnet/metafile.yml b/configs/ccnet/metafile.yml new file mode 100644 index 0000000000..e9babb5b44 --- /dev/null +++ b/configs/ccnet/metafile.yml @@ -0,0 +1,231 @@ +Collections: + - Name: CCNet + Metadata: + Training Data: + - Cityscapes + - Pascal VOC 2012 + Aug + - ADE20K + +Models: + + - Name: ccnet_r50-d8_512x1024_40k_cityscapes + In Collection: CCNet + Metadata: + inference time (fps): 3.32 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.76 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth + Config: configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py + + + + - Name: ccnet_r101-d8_512x1024_40k_cityscapes + In Collection: CCNet + Metadata: + inference time (fps): 2.31 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.35 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth + Config: configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py + + + + - Name: ccnet_r50-d8_769x769_40k_cityscapes + In Collection: CCNet + Metadata: + inference time (fps): 1.43 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.46 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth + Config: configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py + + + + - Name: ccnet_r101-d8_769x769_40k_cityscapes + In Collection: CCNet + Metadata: + inference time (fps): 1.01 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.94 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth + Config: configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py + + + + - Name: ccnet_r50-d8_512x1024_80k_cityscapes + In Collection: CCNet + Metadata: + inference time (fps): 3.32 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.03 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth + Config: configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py + + + + - Name: ccnet_r101-d8_512x1024_80k_cityscapes + In Collection: CCNet + Metadata: + inference time (fps): 2.31 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.87 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth + Config: configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py + + + + - Name: ccnet_r50-d8_769x769_80k_cityscapes + In Collection: CCNet + Metadata: + inference time (fps): 1.43 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.29 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth + Config: configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py + + + + - Name: ccnet_r101-d8_769x769_80k_cityscapes + In Collection: CCNet + Metadata: + inference time (fps): 1.01 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.45 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth + Config: configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py + + + + - Name: ccnet_r50-d8_512x512_80k_ade20k + In Collection: CCNet + Metadata: + inference time (fps): 20.89 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 41.78 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth + Config: configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py + + + + - Name: ccnet_r101-d8_512x512_80k_ade20k + In Collection: CCNet + Metadata: + inference time (fps): 14.11 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.97 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth + Config: configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py + + + + - Name: ccnet_r50-d8_512x512_160k_ade20k + In Collection: CCNet + Metadata: + inference time (fps): 20.89 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.08 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth + Config: configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py + + + + - Name: ccnet_r101-d8_512x512_160k_ade20k + In Collection: CCNet + Metadata: + inference time (fps): 14.11 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.71 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth + Config: configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py + + + + - Name: ccnet_r50-d8_512x512_20k_voc12aug + In Collection: CCNet + Metadata: + inference time (fps): 20.45 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.17 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth + Config: configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py + + + + - Name: ccnet_r101-d8_512x512_20k_voc12aug + In Collection: CCNet + Metadata: + inference time (fps): 13.64 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.27 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth + Config: configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py + + + + - Name: ccnet_r50-d8_512x512_40k_voc12aug + In Collection: CCNet + Metadata: + inference time (fps): 20.45 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 75.96 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth + Config: configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py + + + + - Name: ccnet_r101-d8_512x512_40k_voc12aug + In Collection: CCNet + Metadata: + inference time (fps): 13.64 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.87 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth + Config: configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py diff --git a/configs/cgnet/metafile.yml b/configs/cgnet/metafile.yml new file mode 100644 index 0000000000..29f1fbb416 --- /dev/null +++ b/configs/cgnet/metafile.yml @@ -0,0 +1,33 @@ +Collections: + - Name: CGNet + Metadata: + Training Data: + - Cityscapes + +Models: + + - Name: cgnet_680x680_60k_cityscapes + In Collection: CGNet + Metadata: + inference time (fps): 30.51 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 65.63 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes_20201101_110253-4c0b2f2d.pth + Config: configs/cgnet/cgnet_680x680_60k_cityscapes.py + + + + - Name: cgnet_512x1024_60k_cityscapes + In Collection: CGNet + Metadata: + inference time (fps): 31.14 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 68.27 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes_20201101_110254-124ea03b.pth + Config: configs/cgnet/cgnet_512x1024_60k_cityscapes.py diff --git a/configs/danet/metafile.yml b/configs/danet/metafile.yml new file mode 100644 index 0000000000..233cf19a15 --- /dev/null +++ b/configs/danet/metafile.yml @@ -0,0 +1,231 @@ +Collections: + - Name: DANet + Metadata: + Training Data: + - Cityscapes + - Pascal VOC 2012 + Aug + - ADE20K + +Models: + + - Name: danet_r50-d8_512x1024_40k_cityscapes + In Collection: DANet + Metadata: + inference time (fps): 2.66 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.74 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth + Config: configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py + + + + - Name: danet_r101-d8_512x1024_40k_cityscapes + In Collection: DANet + Metadata: + inference time (fps): 1.99 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.52 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth + Config: configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py + + + + - Name: danet_r50-d8_769x769_40k_cityscapes + In Collection: DANet + Metadata: + inference time (fps): 1.56 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.88 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth + Config: configs/danet/danet_r50-d8_769x769_40k_cityscapes.py + + + + - Name: danet_r101-d8_769x769_40k_cityscapes + In Collection: DANet + Metadata: + inference time (fps): 1.07 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.88 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth + Config: configs/danet/danet_r101-d8_769x769_40k_cityscapes.py + + + + - Name: danet_r50-d8_512x1024_80k_cityscapes + In Collection: DANet + Metadata: + inference time (fps): 2.66 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.34 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth + Config: configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py + + + + - Name: danet_r101-d8_512x1024_80k_cityscapes + In Collection: DANet + Metadata: + inference time (fps): 1.99 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.41 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth + Config: configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py + + + + - Name: danet_r50-d8_769x769_80k_cityscapes + In Collection: DANet + Metadata: + inference time (fps): 1.56 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.27 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth + Config: configs/danet/danet_r50-d8_769x769_80k_cityscapes.py + + + + - Name: danet_r101-d8_769x769_80k_cityscapes + In Collection: DANet + Metadata: + inference time (fps): 1.07 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.47 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth + Config: configs/danet/danet_r101-d8_769x769_80k_cityscapes.py + + + + - Name: danet_r50-d8_512x512_80k_ade20k + In Collection: DANet + Metadata: + inference time (fps): 21.20 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 41.66 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth + Config: configs/danet/danet_r50-d8_512x512_80k_ade20k.py + + + + - Name: danet_r101-d8_512x512_80k_ade20k + In Collection: DANet + Metadata: + inference time (fps): 14.18 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.64 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth + Config: configs/danet/danet_r101-d8_512x512_80k_ade20k.py + + + + - Name: danet_r50-d8_512x512_160k_ade20k + In Collection: DANet + Metadata: + inference time (fps): 21.20 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.45 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth + Config: configs/danet/danet_r50-d8_512x512_160k_ade20k.py + + + + - Name: danet_r101-d8_512x512_160k_ade20k + In Collection: DANet + Metadata: + inference time (fps): 14.18 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 44.17 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth + Config: configs/danet/danet_r101-d8_512x512_160k_ade20k.py + + + + - Name: danet_r50-d8_512x512_20k_voc12aug + In Collection: DANet + Metadata: + inference time (fps): 20.94 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 74.45 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth + Config: configs/danet/danet_r50-d8_512x512_20k_voc12aug.py + + + + - Name: danet_r101-d8_512x512_20k_voc12aug + In Collection: DANet + Metadata: + inference time (fps): 13.76 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.02 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth + Config: configs/danet/danet_r101-d8_512x512_20k_voc12aug.py + + + + - Name: danet_r50-d8_512x512_40k_voc12aug + In Collection: DANet + Metadata: + inference time (fps): 20.94 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.37 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth + Config: configs/danet/danet_r50-d8_512x512_40k_voc12aug.py + + + + - Name: danet_r101-d8_512x512_40k_voc12aug + In Collection: DANet + Metadata: + inference time (fps): 13.76 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.51 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth + Config: configs/danet/danet_r101-d8_512x512_40k_voc12aug.py diff --git a/configs/deeplabv3/metafile.yml b/configs/deeplabv3/metafile.yml new file mode 100644 index 0000000000..8c7e416d36 --- /dev/null +++ b/configs/deeplabv3/metafile.yml @@ -0,0 +1,428 @@ +Collections: + - Name: DeepLabV3 + Metadata: + Training Data: + - Cityscapes + - Pascal Context + - Pascal VOC 2012 + Aug + - ADE20K + +Models: + + - Name: deeplabv3_r50-d8_512x1024_40k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 2.57 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.09 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth + Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py + + + + - Name: deeplabv3_r101-d8_512x1024_40k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 1.92 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.12 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth + Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py + + + + - Name: deeplabv3_r50-d8_769x769_40k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 1.11 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.58 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth + Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py + + + + - Name: deeplabv3_r101-d8_769x769_40k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 0.83 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.27 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth + Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py + + + + - Name: deeplabv3_r18-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 13.78 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.70 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth + Config: configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3_r50-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 2.57 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.32 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth + Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3_r101-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 1.92 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.20 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth + Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3_r18-d8_769x769_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 5.55 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.60 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth + Config: configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py + + + + - Name: deeplabv3_r50-d8_769x769_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 1.11 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.89 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth + Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py + + + + - Name: deeplabv3_r101-d8_769x769_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 0.83 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.67 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth + Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py + + + + - Name: deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 6.96 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.71 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-67b0c992.pth + Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py + + + + - Name: deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 6.96 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.36 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth + Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3_r18b-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 13.93 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.26 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth + Config: configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3_r50b-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 2.74 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.63 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth + Config: configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3_r101b-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 1.81 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.01 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth + Config: configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3_r18b-d8_769x769_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 5.79 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.63 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth + Config: configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py + + + + - Name: deeplabv3_r50b-d8_769x769_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 1.16 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.80 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth + Config: configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py + + + + - Name: deeplabv3_r101b-d8_769x769_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 0.82 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.41 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth + Config: configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py + + + + - Name: deeplabv3_r50-d8_512x512_80k_ade20k + In Collection: DeepLabV3 + Metadata: + inference time (fps): 14.76 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.42 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth + Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py + + + + - Name: deeplabv3_r101-d8_512x512_80k_ade20k + In Collection: DeepLabV3 + Metadata: + inference time (fps): 10.14 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 44.08 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth + Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py + + + + - Name: deeplabv3_r50-d8_512x512_160k_ade20k + In Collection: DeepLabV3 + Metadata: + inference time (fps): 14.76 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.66 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth + Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py + + + + - Name: deeplabv3_r101-d8_512x512_160k_ade20k + In Collection: DeepLabV3 + Metadata: + inference time (fps): 10.14 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 45.00 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth + Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py + + + + - Name: deeplabv3_r50-d8_512x512_20k_voc12aug + In Collection: DeepLabV3 + Metadata: + inference time (fps): 13.88 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.17 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth + Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py + + + + - Name: deeplabv3_r101-d8_512x512_20k_voc12aug + In Collection: DeepLabV3 + Metadata: + inference time (fps): 9.81 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 78.70 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth + Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py + + + + - Name: deeplabv3_r50-d8_512x512_40k_voc12aug + In Collection: DeepLabV3 + Metadata: + inference time (fps): 13.88 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.68 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth + Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py + + + + - Name: deeplabv3_r101-d8_512x512_40k_voc12aug + In Collection: DeepLabV3 + Metadata: + inference time (fps): 9.81 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.92 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth + Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py + + + + - Name: deeplabv3_r101-d8_480x480_40k_pascal_context + In Collection: DeepLabV3 + Metadata: + inference time (fps): 7.09 + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 46.55 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth + Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py + + + + - Name: deeplabv3_r101-d8_480x480_80k_pascal_context + In Collection: DeepLabV3 + Metadata: + inference time (fps): 7.09 + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 46.42 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth + Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py + + + + - Name: deeplabv3_r101-d8_480x480_40k_pascal_context + In Collection: DeepLabV3 + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 52.61 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth + Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py + + + + - Name: deeplabv3_r101-d8_480x480_80k_pascal_context_59 + In Collection: DeepLabV3 + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 52.46 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth + Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py diff --git a/configs/deeplabv3plus/metafile.yml b/configs/deeplabv3plus/metafile.yml new file mode 100644 index 0000000000..d5256b7894 --- /dev/null +++ b/configs/deeplabv3plus/metafile.yml @@ -0,0 +1,428 @@ +Collections: + - Name: DeepLabV3+ + Metadata: + Training Data: + - Cityscapes + - Pascal Context + - Pascal VOC 2012 + Aug + - ADE20K + +Models: + + - Name: deeplabv3plus_r50-d8_512x1024_40k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 3.94 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.61 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth + Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py + + + + - Name: deeplabv3plus_r101-d8_512x1024_40k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 2.60 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.21 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth + Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py + + + + - Name: deeplabv3plus_r50-d8_769x769_40k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 1.72 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.97 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth + Config: configs/deeplabv3+/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py + + + + - Name: deeplabv3plus_r101-d8_769x769_40k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 1.15 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.46 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth + Config: configs/deeplabv3+/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py + + + + - Name: deeplabv3plus_r18-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 14.27 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.89 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth + Config: configs/deeplabv3+/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3plus_r50-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 3.94 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.09 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth + Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3plus_r101-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 2.60 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.97 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth + Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3plus_r18-d8_769x769_80k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 5.74 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.26 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth + Config: configs/deeplabv3+/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py + + + + - Name: deeplabv3plus_r50-d8_769x769_80k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 1.72 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.83 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth + Config: configs/deeplabv3+/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py + + + + - Name: deeplabv3plus_r101-d8_769x769_80k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 1.15 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.98 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20200607_000405-a7573d20.pth + Config: configs/deeplabv3+/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py + + + + - Name: deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 7.48 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.09 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth + Config: configs/deeplabv3+/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py + + + + - Name: deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 7.48 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.90 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth + Config: configs/deeplabv3+/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3plus_r18b-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 14.95 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 75.87 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth + Config: configs/deeplabv3+/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3plus_r50b-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 3.94 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.28 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth + Config: configs/deeplabv3+/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3plus_r101b-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 2.60 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.16 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth + Config: configs/deeplabv3+/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3plus_r18b-d8_769x769_80k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 5.96 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.36 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth + Config: configs/deeplabv3+/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py + + + + - Name: deeplabv3plus_r50b-d8_769x769_80k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 1.72 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.41 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth + Config: configs/deeplabv3+/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py + + + + - Name: deeplabv3plus_r101b-d8_769x769_80k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 1.10 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.88 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth + Config: configs/deeplabv3+/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py + + + + - Name: deeplabv3plus_r50-d8_512x512_80k_ade20k + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 21.01 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.72 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth + Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x512_80k_ade20k.py + + + + - Name: deeplabv3plus_r101-d8_512x512_80k_ade20k + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 14.16 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 44.60 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth + Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x512_80k_ade20k.py + + + + - Name: deeplabv3plus_r50-d8_512x512_160k_ade20k + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 21.01 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.95 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth + Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x512_160k_ade20k.py + + + + - Name: deeplabv3plus_r101-d8_512x512_160k_ade20k + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 14.16 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 45.47 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth + Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x512_160k_ade20k.py + + + + - Name: deeplabv3plus_r50-d8_512x512_20k_voc12aug + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 21 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 75.93 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth + Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py + + + + - Name: deeplabv3plus_r101-d8_512x512_20k_voc12aug + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 13.88 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.22 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth + Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py + + + + - Name: deeplabv3plus_r50-d8_512x512_40k_voc12aug + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 21 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.81 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth + Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py + + + + - Name: deeplabv3plus_r101-d8_512x512_40k_voc12aug + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 13.88 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 78.62 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth + Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py + + + + - Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 9.09 + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 47.30 + Weights: 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 + Config: configs/deeplabv3+/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py + + + + - Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 9.09 + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 47.23 + Weights: 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 + Config: configs/deeplabv3+/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py + + + + - Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context + In Collection: DeepLabV3+ + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 52.86 + Weights: 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 + Config: configs/deeplabv3+/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py + + + + - Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context + In Collection: DeepLabV3+ + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 53.2 + Weights: 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 + Config: configs/deeplabv3+/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py diff --git a/configs/dmnet/metafile.yml b/configs/dmnet/metafile.yml new file mode 100644 index 0000000000..936b2e2d36 --- /dev/null +++ b/configs/dmnet/metafile.yml @@ -0,0 +1,174 @@ +Collections: + - Name: DMNet + Metadata: + Training Data: + - Cityscapes + - ADE20K + +Models: + + - Name: dmnet_r50-d8_512x1024_40k_cityscapes + In Collection: DMNet + Metadata: + inference time (fps): 3.66 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.78 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth + Config: configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py + + + + - Name: dmnet_r101-d8_512x1024_40k_cityscapes + In Collection: DMNet + Metadata: + inference time (fps): 2.54 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.37 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth + Config: configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py + + + + - Name: dmnet_r50-d8_769x769_40k_cityscapes + In Collection: DMNet + Metadata: + inference time (fps): 1.57 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.49 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth + Config: configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py + + + + - Name: dmnet_r101-d8_769x769_40k_cityscapes + In Collection: DMNet + Metadata: + inference time (fps): 1.01 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.62 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth + Config: configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py + + + + - Name: dmnet_r50-d8_512x1024_80k_cityscapes + In Collection: DMNet + Metadata: + inference time (fps): 3.66 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.07 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth + Config: configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py + + + + - Name: dmnet_r101-d8_512x1024_80k_cityscapes + In Collection: DMNet + Metadata: + inference time (fps): 2.54 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.64 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth + Config: configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py + + + + - Name: dmnet_r50-d8_769x769_80k_cityscapes + In Collection: DMNet + Metadata: + inference time (fps): 1.57 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.22 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth + Config: configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py + + + + - Name: dmnet_r101-d8_769x769_80k_cityscapes + In Collection: DMNet + Metadata: + inference time (fps): 1.01 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.19 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth + Config: configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py + + + + - Name: dmnet_r50-d8_512x512_80k_ade20k + In Collection: DMNet + Metadata: + inference time (fps): 20.95 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.37 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth + Config: configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py + + + + - Name: dmnet_r101-d8_512x512_80k_ade20k + In Collection: DMNet + Metadata: + inference time (fps): 13.88 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 45.34 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth + Config: configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py + + + + - Name: dmnet_r50-d8_512x512_160k_ade20k + In Collection: DMNet + Metadata: + inference time (fps): 20.95 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.15 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth + Config: configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py + + + + - Name: dmnet_r101-d8_512x512_160k_ade20k + In Collection: DMNet + Metadata: + inference time (fps): 13.88 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 45.42 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth + Config: configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py diff --git a/configs/dnlnet/metafile.yml b/configs/dnlnet/metafile.yml new file mode 100644 index 0000000000..e4df52fa1c --- /dev/null +++ b/configs/dnlnet/metafile.yml @@ -0,0 +1,174 @@ +Collections: + - Name: dnl + Metadata: + Training Data: + - Cityscapes + - ADE20K + +Models: + + - Name: dnl_r50-d8_512x1024_40k_cityscapes + In Collection: dnl + Metadata: + inference time (fps): 2.56 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.61 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth + Config: configs/dnl/dnl_r50-d8_512x1024_40k_cityscapes.py + + + + - Name: dnl_r101-d8_512x1024_40k_cityscapes + In Collection: dnl + Metadata: + inference time (fps): 1.96 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.31 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth + Config: configs/dnl/dnl_r101-d8_512x1024_40k_cityscapes.py + + + + - Name: dnl_r50-d8_769x769_40k_cityscapes + In Collection: dnl + Metadata: + inference time (fps): 1.50 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.44 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth + Config: configs/dnl/dnl_r50-d8_769x769_40k_cityscapes.py + + + + - Name: dnl_r101-d8_769x769_40k_cityscapes + In Collection: dnl + Metadata: + inference time (fps): 1.02 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.39 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth + Config: configs/dnl/dnl_r101-d8_769x769_40k_cityscapes.py + + + + - Name: dnl_r50-d8_512x1024_80k_cityscapes + In Collection: dnl + Metadata: + inference time (fps): 2.56 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.33 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth + Config: configs/dnl/dnl_r50-d8_512x1024_80k_cityscapes.py + + + + - Name: dnl_r101-d8_512x1024_80k_cityscapes + In Collection: dnl + Metadata: + inference time (fps): 1.96 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.41 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth + Config: configs/dnl/dnl_r101-d8_512x1024_80k_cityscapes.py + + + + - Name: dnl_r50-d8_769x769_80k_cityscapes + In Collection: dnl + Metadata: + inference time (fps): 1.50 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.36 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth + Config: configs/dnl/dnl_r50-d8_769x769_80k_cityscapes.py + + + + - Name: dnl_r101-d8_769x769_80k_cityscapes + In Collection: dnl + Metadata: + inference time (fps): 1.02 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.41 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth + Config: configs/dnl/dnl_r101-d8_769x769_80k_cityscapes.py + + + + - Name: dnl_r50-d8_512x512_80k_ade20k + In Collection: dnl + Metadata: + inference time (fps): 20.66 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 41.76 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth + Config: configs/dnl/dnl_r50-d8_512x512_80k_ade20k.py + + + + - Name: dnl_r101-d8_512x512_80k_ade20k + In Collection: dnl + Metadata: + inference time (fps): 12.54 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.76 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth + Config: configs/dnl/dnl_r101-d8_512x512_80k_ade20k.py + + + + - Name: dnl_r50-d8_512x512_160k_ade20k + In Collection: dnl + Metadata: + inference time (fps): 20.66 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 41.87 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth + Config: configs/dnl/dnl_r50-d8_512x512_160k_ade20k.py + + + + - Name: dnl_r101-d8_512x512_160k_ade20k + In Collection: dnl + Metadata: + inference time (fps): 12.54 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 44.25 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth + Config: configs/dnl/dnl_r101-d8_512x512_160k_ade20k.py diff --git a/configs/emanet/metafile.yml b/configs/emanet/metafile.yml new file mode 100644 index 0000000000..f37dcec6d6 --- /dev/null +++ b/configs/emanet/metafile.yml @@ -0,0 +1,61 @@ +Collections: + - Name: EMANet + Metadata: + Training Data: + - Cityscapes + +Models: + + - Name: emanet_r50-d8_512x1024_80k_cityscapes + In Collection: EMANet + Metadata: + inference time (fps): 4.58 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.59 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth + Config: configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py + + + + - Name: emanet_r101-d8_512x1024_80k_cityscapes + In Collection: EMANet + Metadata: + inference time (fps): 2.87 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.10 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth + Config: configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py + + + + - Name: emanet_r50-d8_769x769_80k_cityscapes + In Collection: EMANet + Metadata: + inference time (fps): 1.97 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.33 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth + Config: configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py + + + + - Name: emanet_r101-d8_769x769_80k_cityscapes + In Collection: EMANet + Metadata: + inference time (fps): 1.22 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.62 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth + Config: configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py diff --git a/configs/encnet/metafile.yml b/configs/encnet/metafile.yml new file mode 100644 index 0000000000..df8bc20074 --- /dev/null +++ b/configs/encnet/metafile.yml @@ -0,0 +1,175 @@ +Collections: + - Name: encnet + Metadata: + Training Data: + - Cityscapes + - Pascal VOC 2012 + Aug + - ADE20K + +Models: + + - Name: encnet_r50-d8_512x1024_40k_cityscapes + In Collection: encnet + Metadata: + inference time (fps): 4.58 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 75.67 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth + Config: configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py + + + + - Name: encnet_r101-d8_512x1024_40k_cityscapes + In Collection: encnet + Metadata: + inference time (fps): 2.66 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 75.81 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth + Config: configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py + + + + - Name: encnet_r50-d8_769x769_40k_cityscapes + In Collection: encnet + Metadata: + inference time (fps): 1.82 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.24 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth + Config: configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py + + + + - Name: encnet_r101-d8_769x769_40k_cityscapes + In Collection: encnet + Metadata: + inference time (fps): 1.26 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 74.25 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth + Config: configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py + + + + - Name: encnet_r50-d8_512x1024_80k_cityscapes + In Collection: encnet + Metadata: + inference time (fps): 4.58 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.94 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth + Config: configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py + + + + - Name: encnet_r101-d8_512x1024_80k_cityscapes + In Collection: encnet + Metadata: + inference time (fps): 2.66 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.55 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth + Config: configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py + + + + - Name: encnet_r50-d8_769x769_80k_cityscapes + In Collection: encnet + Metadata: + inference time (fps): 1.82 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.44 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth + Config: configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py + + + + - Name: encnet_r101-d8_769x769_80k_cityscapes + In Collection: encnet + Metadata: + inference time (fps): 1.26 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.10 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth + Config: configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py + + + + - Name: encnet_r50-d8_512x512_80k_ade20k + In Collection: encnet + Metadata: + inference time (fps): 22.81 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 39.53 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth + Config: configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py + + + + - Name: encnet_r101-d8_512x512_80k_ade20k + In Collection: encnet + Metadata: + inference time (fps): 14.87 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.11 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth + Config: configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py + + + + - Name: encnet_r50-d8_512x512_160k_ade20k + In Collection: encnet + Metadata: + inference time (fps): 22.81 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 40.10 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth + Config: configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py + + + + - Name: encnet_r101-d8_512x512_160k_ade20k + In Collection: encnet + Metadata: + inference time (fps): 14.87 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.61 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth + Config: configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py diff --git a/configs/fastscnn/metafile.yml b/configs/fastscnn/metafile.yml new file mode 100644 index 0000000000..edae6f6aa3 --- /dev/null +++ b/configs/fastscnn/metafile.yml @@ -0,0 +1,19 @@ +Collections: + - Name: Fast-SCNN + Metadata: + Training Data: + - Cityscapes + +Models: + + - Name: fast_scnn_4x8_80k_lr0.12_cityscapes + In Collection: Fast-SCNN + Metadata: + inference time (fps): 63.61 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 69.06 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_4x8_80k_lr0.12_cityscapes-f5096c79.pth + Config: configs/fast-scnn/fast_scnn_4x8_80k_lr0.12_cityscapes.py diff --git a/configs/fcn/metafile.yml b/configs/fcn/metafile.yml new file mode 100644 index 0000000000..6419a40aa4 --- /dev/null +++ b/configs/fcn/metafile.yml @@ -0,0 +1,519 @@ +Collections: + - Name: FCN + Metadata: + Training Data: + - Cityscapes + - Pascal Context + - Pascal VOC 2012 + Aug + - ADE20K + - Name: FCN-D6 + Metadata: + Training Data: + - Cityscapes + - Pascal Context + - Pascal VOC 2012 + Aug + - ADE20K + +Models: + + - Name: fcn_r50-d8_512x1024_40k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 4.17 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 72.25 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth + Config: configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py + + + + - Name: fcn_r101-d8_512x1024_40k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 2.66 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 75.45 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth + Config: configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py + + + + - Name: fcn_r50-d8_769x769_40k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 1.80 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 71.47 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth + Config: configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.py + + + + - Name: fcn_r101-d8_769x769_40k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 1.19 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 73.93 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth + Config: configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py + + + + - Name: fcn_r18-d8_512x1024_80k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 14.65 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 71.11 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth + Config: configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py + + + + - Name: fcn_r50-d8_512x1024_80k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 4.17 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 73.61 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth + Config: configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py + + + + - Name: fcn_r101-d8_512x1024_80k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 2.66 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 75.13 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth + Config: configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py + + + + - Name: fcn_r18-d8_769x769_80k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 6.40 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 70.80 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth + Config: configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py + + + + - Name: fcn_r50-d8_769x769_80k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 1.80 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 72.64 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth + Config: configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.py + + + + - Name: fcn_r101-d8_769x769_80k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 1.19 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 75.52 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth + Config: configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py + + + + - Name: fcn_r18b-d8_512x1024_80k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 16.74 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 70.24 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth + Config: configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py + + + + - Name: fcn_r50b-d8_512x1024_80k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 4.20 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 75.65 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth + Config: configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py + + + + - Name: fcn_r101b-d8_512x1024_80k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 2.73 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.37 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth + Config: configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py + + + + - Name: fcn_r18b-d8_769x769_80k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 6.70 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 69.66 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth + Config: configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py + + + + - Name: fcn_r50b-d8_769x769_80k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 1.82 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 73.83 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth + Config: configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py + + + + - Name: fcn_r101b-d8_769x769_80k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 1.15 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.02 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth + Config: configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py + + + + - Name: fcn_d6_r50-d16_512x1024_40k_cityscapes + In Collection: FCN-D6 + Metadata: + inference time (fps): 10.22 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.06 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth + Config: configs/fcn-d6/fcn_d6_r50-d16_512x1024_40k_cityscapes.py + + + + - Name: fcn_d6_r50-d16_512x1024_80k_cityscapes + In Collection: FCN-D6 + Metadata: + inference time (fps): 10.35 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.27 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth + Config: configs/fcn-d6/fcn_d6_r50-d16_512x1024_80k_cityscapes.py + + + + - Name: fcn_d6_r50-d16_769x769_40k_cityscapes + In Collection: FCN-D6 + Metadata: + inference time (fps): 4.17 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.82 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-1aab18ed.pth + Config: configs/fcn-d6/fcn_d6_r50-d16_769x769_40k_cityscapes.py + + + + - Name: fcn_d6_r50-d16_769x769_80k_cityscapes + In Collection: FCN-D6 + Metadata: + inference time (fps): 4.15 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.04 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-109d88eb.pth + Config: configs/fcn-d6/fcn_d6_r50-d16_769x769_80k_cityscapes.py + + + + - Name: fcn_d6_r101-d16_512x1024_40k_cityscapes + In Collection: FCN-D6 + Metadata: + inference time (fps): 8.04 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.36 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-9cf2b450.pth + Config: configs/fcn-d6/fcn_d6_r101-d16_512x1024_40k_cityscapes.py + + + + - Name: fcn_d6_r101-d16_512x1024_80k_cityscapes + In Collection: FCN-D6 + Metadata: + inference time (fps): 8.26 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.46 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-cb336445.pth + Config: configs/fcn-d6/fcn_d6_r101-d16_512x1024_80k_cityscapes.py + + + + - Name: fcn_d6_r101-d16_769x769_40k_cityscapes + In Collection: FCN-D6 + Metadata: + inference time (fps): 3.12 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.28 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-60b114e9.pth + Config: configs/fcn-d6/fcn_d6_r101-d16_769x769_40k_cityscapes.py + + + + - Name: fcn_d6_r101-d16_769x769_80k_cityscapes + In Collection: FCN-D6 + Metadata: + inference time (fps): 3.21 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.06 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-e33adc4f.pth + Config: configs/fcn-d6/fcn_d6_r101-d16_769x769_80k_cityscapes.py + + + + - Name: fcn_r50-d8_512x512_80k_ade20k + In Collection: FCN + Metadata: + inference time (fps): 23.49 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 35.94 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth + Config: configs/fcn/fcn_r50-d8_512x512_80k_ade20k.py + + + + - Name: fcn_r101-d8_512x512_80k_ade20k + In Collection: FCN + Metadata: + inference time (fps): 14.78 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 39.61 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth + Config: configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py + + + + - Name: fcn_r50-d8_512x512_160k_ade20k + In Collection: FCN + Metadata: + inference time (fps): 23.49 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 36.10 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth + Config: configs/fcn/fcn_r50-d8_512x512_160k_ade20k.py + + + + - Name: fcn_r101-d8_512x512_160k_ade20k + In Collection: FCN + Metadata: + inference time (fps): 14.78 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 39.91 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth + Config: configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py + + + + - Name: fcn_r50-d8_512x512_20k_voc12aug + In Collection: FCN + Metadata: + inference time (fps): 23.28 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 67.08 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth + Config: configs/fcn/fcn_r50-d8_512x512_20k_voc12aug.py + + + + - Name: fcn_r101-d8_512x512_20k_voc12aug + In Collection: FCN + Metadata: + inference time (fps): 14.81 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 71.16 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth + Config: configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py + + + + - Name: fcn_r50-d8_512x512_40k_voc12aug + In Collection: FCN + Metadata: + inference time (fps): 23.28 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 66.97 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth + Config: configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py + + + + - Name: fcn_r101-d8_512x512_40k_voc12aug + In Collection: FCN + Metadata: + inference time (fps): 14.81 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 69.91 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth + Config: configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py + + + + - Name: fcn_r101-d8_480x480_40k_pascal_context + In Collection: FCN + Metadata: + inference time (fps): 9.93 + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 44.43 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20210421_154757-b5e97937.pth + Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py + + + + - Name: fcn_r101-d8_480x480_80k_pascal_context + In Collection: FCN + Metadata: + inference time (fps): 9.93 + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 44.13 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20210421_163310-4711813f.pth + Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py + + + + - Name: fcn_r101-d8_480x480_40k_pascal_context_59 + In Collection: FCN + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 48.42 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59_20210415_230724-8cf83682.pth + Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.py + + + + - Name: fcn_r101-d8_480x480_80k_pascal_context_59 + In Collection: FCN + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 49.35 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59_20210416_110804-9a6f2c94.pth + Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.py diff --git a/configs/fp16/metafile.yml b/configs/fp16/metafile.yml new file mode 100644 index 0000000000..e4187bdad2 --- /dev/null +++ b/configs/fp16/metafile.yml @@ -0,0 +1,56 @@ + +Models: + + - Name: fcn_r101-d8_512x1024_80k_fp16_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 8.64 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.80 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes/fcn_r101-d8_512x1024_80k_fp16_cityscapes-50245227.pth + Config: configs/fcn/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py + + + + - Name: pspnet_r101-d8_512x1024_80k_fp16_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 8.77 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.46 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes/pspnet_r101-d8_512x1024_80k_fp16_cityscapes-ade37931.pth + Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_fp16_cityscapes.py + + + + - Name: deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 3.86 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.48 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes-bc86dc84.pth + Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes.py + + + + - Name: deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 7.87 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.46 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes-cc58bc8d.pth + Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes.py diff --git a/configs/gcnet/metafile.yml b/configs/gcnet/metafile.yml new file mode 100644 index 0000000000..c10c918a4e --- /dev/null +++ b/configs/gcnet/metafile.yml @@ -0,0 +1,231 @@ +Collections: + - Name: GCNet + Metadata: + Training Data: + - Cityscapes + - Pascal VOC 2012 + Aug + - ADE20K + +Models: + + - Name: gcnet_r50-d8_512x1024_40k_cityscapes + In Collection: GCNet + Metadata: + inference time (fps): 3.93 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.69 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436-4b0fd17b.pth + Config: configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py + + + + - Name: gcnet_r101-d8_512x1024_40k_cityscapes + In Collection: GCNet + Metadata: + inference time (fps): 2.61 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.28 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436-5e62567f.pth + Config: configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py + + + + - Name: gcnet_r50-d8_769x769_40k_cityscapes + In Collection: GCNet + Metadata: + inference time (fps): 1.67 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.12 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814-a26f4471.pth + Config: configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py + + + + - Name: gcnet_r101-d8_769x769_40k_cityscapes + In Collection: GCNet + Metadata: + inference time (fps): 1.13 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.95 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550-ca4f0a84.pth + Config: configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py + + + + - Name: gcnet_r50-d8_512x1024_80k_cityscapes + In Collection: GCNet + Metadata: + inference time (fps): 3.93 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.48 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450-ef8f069b.pth + Config: configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py + + + + - Name: gcnet_r101-d8_512x1024_80k_cityscapes + In Collection: GCNet + Metadata: + inference time (fps): 2.61 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.03 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450-778ebf69.pth + Config: configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py + + + + - Name: gcnet_r50-d8_769x769_80k_cityscapes + In Collection: GCNet + Metadata: + inference time (fps): 1.67 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.68 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516-4839565b.pth + Config: configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py + + + + - Name: gcnet_r101-d8_769x769_80k_cityscapes + In Collection: GCNet + Metadata: + inference time (fps): 1.13 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.18 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628-8e043423.pth + Config: configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py + + + + - Name: gcnet_r50-d8_512x512_80k_ade20k + In Collection: GCNet + Metadata: + inference time (fps): 23.38 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 41.47 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146-91a6da41.pth + Config: configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py + + + + - Name: gcnet_r101-d8_512x512_80k_ade20k + In Collection: GCNet + Metadata: + inference time (fps): 15.20 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.82 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811-c3fcb6dd.pth + Config: configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py + + + + - Name: gcnet_r50-d8_512x512_160k_ade20k + In Collection: GCNet + Metadata: + inference time (fps): 23.38 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.37 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122-d95f3e1f.pth + Config: configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py + + + + - Name: gcnet_r101-d8_512x512_160k_ade20k + In Collection: GCNet + Metadata: + inference time (fps): 15.20 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.69 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406-615528d7.pth + Config: configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py + + + + - Name: gcnet_r50-d8_512x512_20k_voc12aug + In Collection: GCNet + Metadata: + inference time (fps): 23.35 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.42 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701-3cbfdab1.pth + Config: configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py + + + + - Name: gcnet_r101-d8_512x512_20k_voc12aug + In Collection: GCNet + Metadata: + inference time (fps): 14.80 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.41 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713-6c720aa9.pth + Config: configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py + + + + - Name: gcnet_r50-d8_512x512_40k_voc12aug + In Collection: GCNet + Metadata: + inference time (fps): 23.35 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.24 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105-9797336d.pth + Config: configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py + + + + - Name: gcnet_r101-d8_512x512_40k_voc12aug + In Collection: GCNet + Metadata: + inference time (fps): 14.80 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.84 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806-1e38208d.pth + Config: configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py diff --git a/configs/hrnet/metafile.yml b/configs/hrnet/metafile.yml new file mode 100644 index 0000000000..d2ac3bfa47 --- /dev/null +++ b/configs/hrnet/metafile.yml @@ -0,0 +1,348 @@ +Models: + - Name: fcn_hr18s_512x1024_40k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 23.74 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 73.86 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth + Config: configs/fcn/fcn_hr18s_512x1024_40k_cityscapes.py + + + + - Name: fcn_hr18_512x1024_40k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 12.97 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.19 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth + Config: configs/fcn/fcn_hr18_512x1024_40k_cityscapes.py + + + + - Name: fcn_hr48_512x1024_40k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 6.42 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.48 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth + Config: configs/fcn/fcn_hr48_512x1024_40k_cityscapes.py + + + + - Name: fcn_hr18s_512x1024_80k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 23.74 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 75.31 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth + Config: configs/fcn/fcn_hr18s_512x1024_80k_cityscapes.py + + + + - Name: fcn_hr18_512x1024_80k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 12.97 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.65 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth + Config: configs/fcn/fcn_hr18_512x1024_80k_cityscapes.py + + + + - Name: fcn_hr48_512x1024_80k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 6.42 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.93 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth + Config: configs/fcn/fcn_hr48_512x1024_80k_cityscapes.py + + + + - Name: fcn_hr18s_512x1024_160k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 23.74 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.31 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth + Config: configs/fcn/fcn_hr18s_512x1024_160k_cityscapes.py + + + + - Name: fcn_hr18_512x1024_160k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 12.97 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.80 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth + Config: configs/fcn/fcn_hr18_512x1024_160k_cityscapes.py + + + + - Name: fcn_hr48_512x1024_160k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 6.42 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.65 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth + Config: configs/fcn/fcn_hr48_512x1024_160k_cityscapes.py + + + + - Name: fcn_hr18s_512x512_80k_ade20k + In Collection: FCN + Metadata: + inference time (fps): 38.66 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 31.38 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth + Config: configs/fcn/fcn_hr18s_512x512_80k_ade20k.py + + + + - Name: fcn_hr18_512x512_80k_ade20k + In Collection: FCN + Metadata: + inference time (fps): 22.57 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 35.51 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20200614_185145-66f20cb7.pth + Config: configs/fcn/fcn_hr18_512x512_80k_ade20k.py + + + + - Name: fcn_hr48_512x512_80k_ade20k + In Collection: FCN + Metadata: + inference time (fps): 21.23 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 41.90 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth + Config: configs/fcn/fcn_hr48_512x512_80k_ade20k.py + + + + - Name: fcn_hr18s_512x512_160k_ade20k + In Collection: FCN + Metadata: + inference time (fps): 38.66 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 33.00 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20200614_214413-870f65ac.pth + Config: configs/fcn/fcn_hr18s_512x512_160k_ade20k.py + + + + - Name: fcn_hr18_512x512_160k_ade20k + In Collection: FCN + Metadata: + inference time (fps): 22.57 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 36.79 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth + Config: configs/fcn/fcn_hr18_512x512_160k_ade20k.py + + + + - Name: fcn_hr48_512x512_160k_ade20k + In Collection: FCN + Metadata: + inference time (fps): 21.23 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.02 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth + Config: configs/fcn/fcn_hr48_512x512_160k_ade20k.py + + + + - Name: fcn_hr18s_512x512_20k_voc12aug + In Collection: FCN + Metadata: + inference time (fps): 43.36 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 65.20 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20200617_224503-56e36088.pth + Config: configs/fcn/fcn_hr18s_512x512_20k_voc12aug.py + + + + - Name: fcn_hr18_512x512_20k_voc12aug + In Collection: FCN + Metadata: + inference time (fps): 23.48 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 72.30 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth + Config: configs/fcn/fcn_hr18_512x512_20k_voc12aug.py + + + + - Name: fcn_hr48_512x512_20k_voc12aug + In Collection: FCN + Metadata: + inference time (fps): 22.05 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 75.87 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth + Config: configs/fcn/fcn_hr48_512x512_20k_voc12aug.py + + + + - Name: fcn_hr18s_512x512_40k_voc12aug + In Collection: FCN + Metadata: + inference time (fps): 43.36 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 66.61 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth + Config: configs/fcn/fcn_hr18s_512x512_40k_voc12aug.py + + + + - Name: fcn_hr18_512x512_40k_voc12aug + In Collection: FCN + Metadata: + inference time (fps): 23.48 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 72.90 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth + Config: configs/fcn/fcn_hr18_512x512_40k_voc12aug.py + + + + - Name: fcn_hr48_512x512_40k_voc12aug + In Collection: FCN + Metadata: + inference time (fps): 22.05 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.24 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth + Config: configs/fcn/fcn_hr48_512x512_40k_voc12aug.py + + + + - Name: fcn_hr48_480x480_40k_pascal_context + In Collection: FCN + Metadata: + inference time (fps): 8.86 + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 45.14 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth + Config: configs/fcn/fcn_hr48_480x480_40k_pascal_context.py + + + + - Name: fcn_hr48_480x480_80k_pascal_context + In Collection: FCN + Metadata: + inference time (fps): 8.86 + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 45.84 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth + Config: configs/fcn/fcn_hr48_480x480_80k_pascal_context.py + + + + - Name: fcn_hr48_480x480_40k_pascal_context_59 + In Collection: FCN + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 50.33 + Weights: 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 + Config: configs/fcn/fcn_hr48_480x480_40k_pascal_context_59.py + + + + - Name: fcn_hr48_480x480_80k_pascal_context + In Collection: FCN + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 51.12 + Weights: 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 + Config: configs/fcn/fcn_hr48_480x480_80k_pascal_context.py diff --git a/configs/mobilenet_v2/metafile.yml b/configs/mobilenet_v2/metafile.yml new file mode 100644 index 0000000000..7146869385 --- /dev/null +++ b/configs/mobilenet_v2/metafile.yml @@ -0,0 +1,112 @@ + +Models: + + - Name: fcn_m-v2-d8_512x1024_80k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 14.2 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 61.54 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-d24c28c1.pth + Config: configs/fcn/fcn_m-v2-d8_512x1024_80k_cityscapes.py + + + + - Name: pspnet_m-v2-d8_512x1024_80k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 11.2 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 70.23 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth + Config: configs/pspnet/pspnet_m-v2-d8_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3_m-v2-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 8.4 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 73.84 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth + Config: configs/deeplabv3/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 8.4 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 75.20 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth + Config: configs/deeplabv3+/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py + + + + - Name: fcn_m-v2-d8_512x512_160k_ade20k + In Collection: FCN + Metadata: + inference time (fps): 64.4 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 19.71 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth + Config: configs/fcn/fcn_m-v2-d8_512x512_160k_ade20k.py + + + + - Name: pspnet_m-v2-d8_512x512_160k_ade20k + In Collection: PSPNet + Metadata: + inference time (fps): 57.7 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 29.68 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth + Config: configs/pspnet/pspnet_m-v2-d8_512x512_160k_ade20k.py + + + + - Name: deeplabv3_m-v2-d8_512x512_160k_ade20k + In Collection: DeepLabV3 + Metadata: + inference time (fps): 39.9 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 34.08 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth + Config: configs/deeplabv3/deeplabv3_m-v2-d8_512x512_160k_ade20k.py + + + + - Name: deeplabv3plus_m-v2-d8_512x512_160k_ade20k + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 43.1 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 34.02 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth + Config: configs/deeplabv3+/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py diff --git a/configs/mobilenet_v3/metafile.yml b/configs/mobilenet_v3/metafile.yml new file mode 100644 index 0000000000..efd700058e --- /dev/null +++ b/configs/mobilenet_v3/metafile.yml @@ -0,0 +1,61 @@ +Collections: + - Name: LRASPP + Metadata: + Training Data: + - Cityscapes + +Models: + + - Name: lraspp_m-v3-d8_512x1024_320k_cityscapes + In Collection: LRASPP + Metadata: + inference time (fps): 15.22 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 69.54 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes_20201224_220337-cfe8fb07.pth + Config: configs/lraspp/lraspp_m-v3-d8_512x1024_320k_cityscapes.py + + + + - Name: lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes + In Collection: LRASPP + Metadata: + inference time (fps): 14.77 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 67.87 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes_20201224_220337-9f29cd72.pth + Config: configs/lraspp/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py + + + + - Name: lraspp_m-v3s-d8_512x1024_320k_cityscapes + In Collection: LRASPP + Metadata: + inference time (fps): 23.64 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 64.11 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes_20201224_223935-61565b34.pth + Config: configs/lraspp/lraspp_m-v3s-d8_512x1024_320k_cityscapes.py + + + + - Name: lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes + In Collection: LRASPP + Metadata: + inference time (fps): 24.50 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 62.74 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes_20201224_223935-03daeabb.pth + Config: configs/lraspp/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes.py diff --git a/configs/nonlocal_net/metafile.yml b/configs/nonlocal_net/metafile.yml new file mode 100644 index 0000000000..0f41ac015e --- /dev/null +++ b/configs/nonlocal_net/metafile.yml @@ -0,0 +1,231 @@ +Collections: + - Name: NonLocal + Metadata: + Training Data: + - Cityscapes + - Pascal VOC 2012 + Aug + - ADE20K + +Models: + + - Name: nonlocal_r50-d8_512x1024_40k_cityscapes + In Collection: NonLocal + Metadata: + inference time (fps): 2.72 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.24 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748-c75e81e3.pth + Config: configs/nonlocal/nonlocal_r50-d8_512x1024_40k_cityscapes.py + + + + - Name: nonlocal_r101-d8_512x1024_40k_cityscapes + In Collection: NonLocal + Metadata: + inference time (fps): 1.95 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.66 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748-d63729fa.pth + Config: configs/nonlocal/nonlocal_r101-d8_512x1024_40k_cityscapes.py + + + + - Name: nonlocal_r50-d8_769x769_40k_cityscapes + In Collection: NonLocal + Metadata: + inference time (fps): 1.52 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.33 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243-82ef6749.pth + Config: configs/nonlocal/nonlocal_r50-d8_769x769_40k_cityscapes.py + + + + - Name: nonlocal_r101-d8_769x769_40k_cityscapes + In Collection: NonLocal + Metadata: + inference time (fps): 1.05 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.57 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348-8fe9a9dc.pth + Config: configs/nonlocal/nonlocal_r101-d8_769x769_40k_cityscapes.py + + + + - Name: nonlocal_r50-d8_512x1024_80k_cityscapes + In Collection: NonLocal + Metadata: + inference time (fps): 2.72 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.01 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518-d6839fae.pth + Config: configs/nonlocal/nonlocal_r50-d8_512x1024_80k_cityscapes.py + + + + - Name: nonlocal_r101-d8_512x1024_80k_cityscapes + In Collection: NonLocal + Metadata: + inference time (fps): 1.95 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.93 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411-32700183.pth + Config: configs/nonlocal/nonlocal_r101-d8_512x1024_80k_cityscapes.py + + + + - Name: nonlocal_r50-d8_769x769_80k_cityscapes + In Collection: NonLocal + Metadata: + inference time (fps): 1.52 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.05 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506-1f9792f6.pth + Config: configs/nonlocal/nonlocal_r50-d8_769x769_80k_cityscapes.py + + + + - Name: nonlocal_r101-d8_769x769_80k_cityscapes + In Collection: NonLocal + Metadata: + inference time (fps): 1.05 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.40 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428-0e1fa4f9.pth + Config: configs/nonlocal/nonlocal_r101-d8_769x769_80k_cityscapes.py + + + + - Name: nonlocal_r50-d8_512x512_80k_ade20k + In Collection: NonLocal + Metadata: + inference time (fps): 21.37 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 40.75 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801-5ae0aa33.pth + Config: configs/nonlocal/nonlocal_r50-d8_512x512_80k_ade20k.py + + + + - Name: nonlocal_r101-d8_512x512_80k_ade20k + In Collection: NonLocal + Metadata: + inference time (fps): 13.97 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.90 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758-24105919.pth + Config: configs/nonlocal/nonlocal_r101-d8_512x512_80k_ade20k.py + + + + - Name: nonlocal_r50-d8_512x512_160k_ade20k + In Collection: NonLocal + Metadata: + inference time (fps): 21.37 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.03 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410-baef45e3.pth + Config: configs/nonlocal/nonlocal_r50-d8_512x512_160k_ade20k.py + + + + - Name: nonlocal_r101-d8_512x512_160k_ade20k + In Collection: NonLocal + Metadata: + inference time (fps): 13.97 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.36 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20200616_003422-affd0f8d.pth + Config: configs/nonlocal/nonlocal_r101-d8_512x512_160k_ade20k.py + + + + - Name: nonlocal_r50-d8_512x512_20k_voc12aug + In Collection: NonLocal + Metadata: + inference time (fps): 21.21 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.20 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613-07f2a57c.pth + Config: configs/nonlocal/nonlocal_r50-d8_512x512_20k_voc12aug.py + + + + - Name: nonlocal_r101-d8_512x512_20k_voc12aug + In Collection: NonLocal + Metadata: + inference time (fps): 14.01 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 78.15 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615-948c68ab.pth + Config: configs/nonlocal/nonlocal_r101-d8_512x512_20k_voc12aug.py + + + + - Name: nonlocal_r50-d8_512x512_40k_voc12aug + In Collection: NonLocal + Metadata: + inference time (fps): 21.21 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.65 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028-0139d4a9.pth + Config: configs/nonlocal/nonlocal_r50-d8_512x512_40k_voc12aug.py + + + + - Name: nonlocal_r101-d8_512x512_40k_voc12aug + In Collection: NonLocal + Metadata: + inference time (fps): 14.01 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 78.27 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028-7e5ff470.pth + Config: configs/nonlocal/nonlocal_r101-d8_512x512_40k_voc12aug.py diff --git a/configs/ocrnet/metafile.yml b/configs/ocrnet/metafile.yml new file mode 100644 index 0000000000..fcdf72d791 --- /dev/null +++ b/configs/ocrnet/metafile.yml @@ -0,0 +1,343 @@ +Collections: + - Name: OCRNet + Metadata: + Training Data: + - Cityscapes + - Pascal VOC 2012 + Aug + - ADE20K + +Models: + + - Name: ocrnet_hr18s_512x1024_40k_cityscapes + In Collection: OCRNet + Metadata: + inference time (fps): 10.45 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 74.30 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304-fa2436c2.pth + Config: configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py + + + + - Name: ocrnet_hr18_512x1024_40k_cityscapes + In Collection: OCRNet + Metadata: + inference time (fps): 7.50 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.72 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320-401c5bdd.pth + Config: configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py + + + + - Name: ocrnet_hr48_512x1024_40k_cityscapes + In Collection: OCRNet + Metadata: + inference time (fps): 4.22 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.58 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336-55b32491.pth + Config: configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py + + + + - Name: ocrnet_hr18s_512x1024_80k_cityscapes + In Collection: OCRNet + Metadata: + inference time (fps): 10.45 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.16 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735-55979e63.pth + Config: configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py + + + + - Name: ocrnet_hr18_512x1024_80k_cityscapes + In Collection: OCRNet + Metadata: + inference time (fps): 7.50 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.57 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521-c2e1dd4a.pth + Config: configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py + + + + - Name: ocrnet_hr48_512x1024_80k_cityscapes + In Collection: OCRNet + Metadata: + inference time (fps): 4.22 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.70 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752-9076bcdf.pth + Config: configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py + + + + - Name: ocrnet_hr18s_512x1024_160k_cityscapes + In Collection: OCRNet + Metadata: + inference time (fps): 10.45 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.45 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005-f4a7af28.pth + Config: configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py + + + + - Name: ocrnet_hr18_512x1024_160k_cityscapes + In Collection: OCRNet + Metadata: + inference time (fps): 7.50 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.47 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001-b9172d0c.pth + Config: configs/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py + + + + - Name: ocrnet_hr48_512x1024_160k_cityscapes + In Collection: OCRNet + Metadata: + inference time (fps): 4.22 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 81.35 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037-dfbf1b0c.pth + Config: configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py + + + + - Name: ocrnet_r101-d8_512x1024_40k_b8_cityscapes + In Collection: OCRNet + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: + Weights: https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py + Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py + + + + - Name: ocrnet_r101-d8_512x1024_40k_b16_cityscapes + In Collection: OCRNet + Metadata: + inference time (fps): 8.8 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 3.02 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes-02ac0f13.pth + Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py + + + + - Name: ocrnet_r101-d8_512x1024_80k_b16_cityscapes + In Collection: OCRNet + Metadata: + inference time (fps): 8.8 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 3.02 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes-db500f80.pth + Config: configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py + + + + - Name: ocrnet_hr18s_512x512_80k_ade20k + In Collection: OCRNet + Metadata: + inference time (fps): 28.98 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 35.06 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600-e80b62af.pth + Config: configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py + + + + - Name: ocrnet_hr18_512x512_80k_ade20k + In Collection: OCRNet + Metadata: + inference time (fps): 18.93 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 37.79 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157-d173d83b.pth + Config: configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py + + + + - Name: ocrnet_hr48_512x512_80k_ade20k + In Collection: OCRNet + Metadata: + inference time (fps): 16.99 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.00 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518-d168c2d1.pth + Config: configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py + + + + - Name: ocrnet_hr18s_512x512_160k_ade20k + In Collection: OCRNet + Metadata: + inference time (fps): 28.98 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 37.19 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505-8e913058.pth + Config: configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py + + + + - Name: ocrnet_hr18_512x512_160k_ade20k + In Collection: OCRNet + Metadata: + inference time (fps): 18.93 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 39.32 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940-d8fcd9d1.pth + Config: configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py + + + + - Name: ocrnet_hr48_512x512_160k_ade20k + In Collection: OCRNet + Metadata: + inference time (fps): 16.99 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.25 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705-a073726d.pth + Config: configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py + + + + - Name: ocrnet_hr18s_512x512_20k_voc12aug + In Collection: OCRNet + Metadata: + inference time (fps): 31.55 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 71.70 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913-02b04fcb.pth + Config: configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py + + + + - Name: ocrnet_hr18_512x512_20k_voc12aug + In Collection: OCRNet + Metadata: + inference time (fps): 19.91 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 74.75 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932-8954cbb7.pth + Config: configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py + + + + - Name: ocrnet_hr48_512x512_20k_voc12aug + In Collection: OCRNet + Metadata: + inference time (fps): 17.83 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.72 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932-9e82080a.pth + Config: configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py + + + + - Name: ocrnet_hr18s_512x512_40k_voc12aug + In Collection: OCRNet + Metadata: + inference time (fps): 31.55 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 72.76 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025-42b587ac.pth + Config: configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py + + + + - Name: ocrnet_hr18_512x512_40k_voc12aug + In Collection: OCRNet + Metadata: + inference time (fps): 19.91 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 74.98 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958-714302be.pth + Config: configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py + + + + - Name: ocrnet_hr48_512x512_40k_voc12aug + In Collection: OCRNet + Metadata: + inference time (fps): 17.83 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.14 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958-255bc5ce.pth + Config: configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py diff --git a/configs/point_rend/metafile.yml b/configs/point_rend/metafile.yml new file mode 100644 index 0000000000..aba00e0931 --- /dev/null +++ b/configs/point_rend/metafile.yml @@ -0,0 +1,62 @@ +Collections: + - Name: PointRend + Metadata: + Training Data: + - Cityscapes + - ADE20K + +Models: + + - Name: pointrend_r50_512x1024_80k_cityscapes + In Collection: PointRend + Metadata: + inference time (fps): 8.48 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.47 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth + Config: configs/pointrend/pointrend_r50_512x1024_80k_cityscapes.py + + + + - Name: pointrend_r101_512x1024_80k_cityscapes + In Collection: PointRend + Metadata: + inference time (fps): 7.00 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.30 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth + Config: configs/pointrend/pointrend_r101_512x1024_80k_cityscapes.py + + + + - Name: pointrend_r50_512x512_160k_ade20k + In Collection: PointRend + Metadata: + inference time (fps): 17.31 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 37.64 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth + Config: configs/pointrend/pointrend_r50_512x512_160k_ade20k.py + + + + - Name: pointrend_r101_512x512_160k_ade20k + In Collection: PointRend + Metadata: + inference time (fps): 15.50 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 40.02 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth + Config: configs/pointrend/pointrend_r101_512x512_160k_ade20k.py diff --git a/configs/psanet/metafile.yml b/configs/psanet/metafile.yml new file mode 100644 index 0000000000..7e2b3138ba --- /dev/null +++ b/configs/psanet/metafile.yml @@ -0,0 +1,231 @@ +Collections: + - Name: PSANet + Metadata: + Training Data: + - Cityscapes + - Pascal VOC 2012 + Aug + - ADE20K + +Models: + + - Name: psanet_r50-d8_512x1024_40k_cityscapes + In Collection: PSANet + Metadata: + inference time (fps): 3.17 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.63 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117-99fac37c.pth + Config: configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py + + + + - Name: psanet_r101-d8_512x1024_40k_cityscapes + In Collection: PSANet + Metadata: + inference time (fps): 2.20 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.14 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418-27b9cfa7.pth + Config: configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py + + + + - Name: psanet_r50-d8_769x769_40k_cityscapes + In Collection: PSANet + Metadata: + inference time (fps): 1.40 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.99 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717-d5365506.pth + Config: configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py + + + + - Name: psanet_r101-d8_769x769_40k_cityscapes + In Collection: PSANet + Metadata: + inference time (fps): 0.98 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.43 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107-997da1e6.pth + Config: configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py + + + + - Name: psanet_r50-d8_512x1024_80k_cityscapes + In Collection: PSANet + Metadata: + inference time (fps): 3.17 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.24 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842-ab60a24f.pth + Config: configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py + + + + - Name: psanet_r101-d8_512x1024_80k_cityscapes + In Collection: PSANet + Metadata: + inference time (fps): 2.20 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.31 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823-0f73a169.pth + Config: configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py + + + + - Name: psanet_r50-d8_769x769_80k_cityscapes + In Collection: PSANet + Metadata: + inference time (fps): 1.40 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.31 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134-fe42f49e.pth + Config: configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py + + + + - Name: psanet_r101-d8_769x769_80k_cityscapes + In Collection: PSANet + Metadata: + inference time (fps): 0.98 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.69 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550-7665827b.pth + Config: configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py + + + + - Name: psanet_r50-d8_512x512_80k_ade20k + In Collection: PSANet + Metadata: + inference time (fps): 18.91 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 41.14 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141-835e4b97.pth + Config: configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py + + + + - Name: psanet_r101-d8_512x512_80k_ade20k + In Collection: PSANet + Metadata: + inference time (fps): 13.13 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.80 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117-1fab60d4.pth + Config: configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py + + + + - Name: psanet_r50-d8_512x512_160k_ade20k + In Collection: PSANet + Metadata: + inference time (fps): 18.91 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 41.67 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258-148077dd.pth + Config: configs/psanet/psanet_r50-d8_512x512_160k_ade20k.py + + + + - Name: psanet_r101-d8_512x512_160k_ade20k + In Collection: PSANet + Metadata: + inference time (fps): 13.13 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.74 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537-dbfa564c.pth + Config: configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py + + + + - Name: psanet_r50-d8_512x512_20k_voc12aug + In Collection: PSANet + Metadata: + inference time (fps): 18.24 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.39 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413-2f1bbaa1.pth + Config: configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py + + + + - Name: psanet_r101-d8_512x512_20k_voc12aug + In Collection: PSANet + Metadata: + inference time (fps): 12.63 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.91 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624-946fef11.pth + Config: configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py + + + + - Name: psanet_r50-d8_512x512_40k_voc12aug + In Collection: PSANet + Metadata: + inference time (fps): 18.24 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.30 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946-f596afb5.pth + Config: configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py + + + + - Name: psanet_r101-d8_512x512_40k_voc12aug + In Collection: PSANet + Metadata: + inference time (fps): 12.63 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.73 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946-1f560f9e.pth + Config: configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py diff --git a/configs/pspnet/metafile.yml b/configs/pspnet/metafile.yml new file mode 100644 index 0000000000..4981f02c32 --- /dev/null +++ b/configs/pspnet/metafile.yml @@ -0,0 +1,400 @@ +Collections: + - Name: PSPNet + Metadata: + Training Data: + - Cityscapes + - Pascal Context + - Pascal VOC 2012 + Aug + - ADE20K + +Models: + + - Name: pspnet_r50-d8_512x1024_40k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 4.07 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.85 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth + Config: configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py + + + + - Name: pspnet_r101-d8_512x1024_40k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 2.68 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.34 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth + Config: configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py + + + + - Name: pspnet_r50-d8_769x769_40k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 1.76 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.26 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725-86638686.pth + Config: configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py + + + + - Name: pspnet_r101-d8_769x769_40k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 1.15 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.08 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753-61c6f5be.pth + Config: configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py + + + + - Name: pspnet_r18-d8_512x1024_80k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 15.71 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 74.87 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes_20201225_021458-09ffa746.pth + Config: configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py + + + + - Name: pspnet_r50-d8_512x1024_80k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 4.07 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.55 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth + Config: configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py + + + + - Name: pspnet_r101-d8_512x1024_80k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 2.68 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.76 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth + Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py + + + + - Name: pspnet_r18-d8_769x769_80k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 6.20 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 75.90 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes_20201225_021458-3deefc62.pth + Config: configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py + + + + - Name: pspnet_r50-d8_769x769_80k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 1.76 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.59 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121-5ccf03dd.pth + Config: configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py + + + + - Name: pspnet_r101-d8_769x769_80k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 1.15 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.77 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055-dba412fa.pth + Config: configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py + + + + - Name: pspnet_r18b-d8_512x1024_80k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 16.28 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 74.23 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes_20201226_063116-26928a60.pth + Config: configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py + + + + - Name: pspnet_r50b-d8_512x1024_80k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 4.30 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.22 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes_20201225_094315-6344287a.pth + Config: configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py + + + + - Name: pspnet_r101b-d8_512x1024_80k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 2.76 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.69 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth + Config: configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py + + + + - Name: pspnet_r18b-d8_769x769_80k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 6.41 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 74.92 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes_20201226_080942-bf98d186.pth + Config: configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py + + + + - Name: pspnet_r50b-d8_769x769_80k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 1.88 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.50 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes_20201225_094316-4c643cf6.pth + Config: configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py + + + + - Name: pspnet_r101b-d8_769x769_80k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 1.17 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.87 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth + Config: configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py + + + + - Name: pspnet_r50-d8_512x512_80k_ade20k + In Collection: PSPNet + Metadata: + inference time (fps): 23.53 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 41.13 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128-15a8b914.pth + Config: configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py + + + + - Name: pspnet_r101-d8_512x512_80k_ade20k + In Collection: PSPNet + Metadata: + inference time (fps): 15.30 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.57 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423-b6e782f0.pth + Config: configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py + + + + - Name: pspnet_r50-d8_512x512_160k_ade20k + In Collection: PSPNet + Metadata: + inference time (fps): 23.53 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.48 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth + Config: configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py + + + + - Name: pspnet_r101-d8_512x512_160k_ade20k + In Collection: PSPNet + Metadata: + inference time (fps): 15.30 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 44.39 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth + Config: configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py + + + + - Name: pspnet_r50-d8_512x512_20k_voc12aug + In Collection: PSPNet + Metadata: + inference time (fps): 23.59 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.78 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958-ed5dfbd9.pth + Config: configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.py + + + + - Name: pspnet_r101-d8_512x512_20k_voc12aug + In Collection: PSPNet + Metadata: + inference time (fps): 15.02 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 78.47 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003-4aef3c9a.pth + Config: configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py + + + + - Name: pspnet_r50-d8_512x512_40k_voc12aug + In Collection: PSPNet + Metadata: + inference time (fps): 23.59 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.29 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222-ae9c1b8c.pth + Config: configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.py + + + + - Name: pspnet_r101-d8_512x512_40k_voc12aug + In Collection: PSPNet + Metadata: + inference time (fps): 15.02 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 78.52 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222-bc933b18.pth + Config: configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py + + + + - Name: pspnet_r101-d8_480x480_40k_pascal_context + In Collection: PSPNet + Metadata: + inference time (fps): 9.68 + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 46.60 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context_20200911_211210-bf0f5d7c.pth + Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py + + + + - Name: pspnet_r101-d8_480x480_80k_pascal_context + In Collection: PSPNet + Metadata: + inference time (fps): 9.68 + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 46.03 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context_20200911_190530-c86d6233.pth + Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py + + + + - Name: pspnet_r101-d8_480x480_40k_pascal_context + In Collection: PSPNet + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 52.02 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59_20210416_114524-86d44cd4.pth + Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py + + + + - Name: pspnet_r101-d8_480x480_80k_pascal_context_59 + In Collection: PSPNet + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 52.47 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59_20210416_114418-fa6caaa2.pth + Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py diff --git a/configs/resnest/metafile.yml b/configs/resnest/metafile.yml new file mode 100644 index 0000000000..d6775ac9d5 --- /dev/null +++ b/configs/resnest/metafile.yml @@ -0,0 +1,118 @@ +Collections: + - Name: ResNeSt + Metadata: + Training Data: + - Cityscapes + - ADE20K + +Models: + + - Name: fcn_s101-d8_512x1024_80k_cityscapes + In Collection: FCN + Metadata: + inference time (fps): 2.39 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.56 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes_20200807_140631-f8d155b3.pth + Config: configs/fcn/fcn_s101-d8_512x1024_80k_cityscapes.py + + + + - Name: pspnet_s101-d8_512x1024_80k_cityscapes + In Collection: PSPNet + Metadata: + inference time (fps): 2.52 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.57 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth + Config: configs/pspnet/pspnet_s101-d8_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3_s101-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + inference time (fps): 1.88 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.67 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth + Config: configs/deeplabv3/deeplabv3_s101-d8_512x1024_80k_cityscapes.py + + + + - Name: deeplabv3plus_s101-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 2.36 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.62 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth + Config: configs/deeplabv3+/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py + + + + - Name: fcn_s101-d8_512x512_160k_ade20k + In Collection: FCN + Metadata: + inference time (fps): 12.86 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 45.62 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k_20200807_145416-d3160329.pth + Config: configs/fcn/fcn_s101-d8_512x512_160k_ade20k.py + + + + - Name: pspnet_s101-d8_512x512_160k_ade20k + In Collection: PSPNet + Metadata: + inference time (fps): 13.02 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 45.44 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth + Config: configs/pspnet/pspnet_s101-d8_512x512_160k_ade20k.py + + + + - Name: deeplabv3_s101-d8_512x512_160k_ade20k + In Collection: DeepLabV3 + Metadata: + inference time (fps): 9.28 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 45.71 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k_20200807_144503-17ecabe5.pth + Config: configs/deeplabv3/deeplabv3_s101-d8_512x512_160k_ade20k.py + + + + - Name: deeplabv3plus_s101-d8_512x512_160k_ade20k + In Collection: DeepLabV3+ + Metadata: + inference time (fps): 11.96 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 46.47 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k_20200807_144503-27b26226.pth + Config: configs/deeplabv3+/deeplabv3plus_s101-d8_512x512_160k_ade20k.py diff --git a/configs/sem_fpn/metafile.yml b/configs/sem_fpn/metafile.yml new file mode 100644 index 0000000000..781589ac0b --- /dev/null +++ b/configs/sem_fpn/metafile.yml @@ -0,0 +1,63 @@ +Collections: + - Name: FPN + Metadata: + Training Data: + - Cityscapes + - Pascal VOC 2012 + Aug + - ADE20K + +Models: + + - Name: fpn_r50_512x1024_80k_cityscapes + In Collection: FPN + Metadata: + inference time (fps): 13.54 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 74.52 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes_20200717_021437-94018a0d.pth + Config: configs/fpn/fpn_r50_512x1024_80k_cityscapes.py + + + + - Name: fpn_r101_512x1024_80k_cityscapes + In Collection: FPN + Metadata: + inference time (fps): 10.29 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 75.80 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes_20200717_012416-c5800d4c.pth + Config: configs/fpn/fpn_r101_512x1024_80k_cityscapes.py + + + + - Name: fpn_r50_512x512_160k_ade20k + In Collection: FPN + Metadata: + inference time (fps): 55.77 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 37.49 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth + Config: configs/fpn/fpn_r50_512x512_160k_ade20k.py + + + + - Name: fpn_r101_512x512_160k_ade20k + In Collection: FPN + Metadata: + inference time (fps): 40.58 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 39.35 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth + Config: configs/fpn/fpn_r101_512x512_160k_ade20k.py diff --git a/configs/unet/metafile.yml b/configs/unet/metafile.yml new file mode 100644 index 0000000000..51058d00af --- /dev/null +++ b/configs/unet/metafile.yml @@ -0,0 +1,167 @@ +Models: + + - Name: fcn_unet_s5-d16_64x64_40k_drive + In Collection: FCN + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: DRIVE + Metrics: + mIoU: 0.680 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-26cee593.pth + Config: configs/unet-s5-d16/fcn_unet_s5-d16_64x64_40k_drive.py + + + + - Name: pspnet_unet_s5-d16_64x64_40k_drive + In Collection: PSPNet + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: DRIVE + Metrics: + mIoU: 0.599 + 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 + Config: configs/unet-s5-d16/pspnet_unet_s5-d16_64x64_40k_drive.py + + + + - Name: deeplabv3_unet_s5-d16_64x64_40k_drive + In Collection: DeepLabV3 + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: DRIVE + Metrics: + mIoU: 0.596 + 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 + Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_64x64_40k_drive.py + + + + - Name: fcn_unet_s5-d16_128x128_40k_stare + In Collection: FCN + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: STARE + Metrics: + mIoU: 0.968 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-6ea7cfda.pth + Config: configs/unet-s5-d16/fcn_unet_s5-d16_128x128_40k_stare.py + + + + - Name: pspnet_unet_s5-d16_128x128_40k_stare + In Collection: PSPNet + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: STARE + Metrics: + mIoU: 0.982 + 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 + Config: configs/unet-s5-d16/pspnet_unet_s5-d16_128x128_40k_stare.py + + + + - Name: deeplabv3_unet_s5-d16_128x128_40k_stare + In Collection: DeepLabV3 + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: STARE + Metrics: + mIoU: 0.999 + 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 + Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_128x128_40k_stare.py + + + + - Name: fcn_unet_s5-d16_128x128_40k_chase_db1 + In Collection: FCN + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: CHASE_DB1 + Metrics: + mIoU: 0.968 + 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_191051-95852f45.pth + Config: configs/unet-s5-d16/fcn_unet_s5-d16_128x128_40k_chase_db1.py + + + + - Name: pspnet_unet_s5-d16_128x128_40k_chase_db1 + In Collection: PSPNet + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: CHASE_DB1 + Metrics: + mIoU: 0.982 + 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 + Config: configs/unet-s5-d16/pspnet_unet_s5-d16_128x128_40k_chase_db1.py + + + + - Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1 + In Collection: DeepLabV3 + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: CHASE_DB1 + Metrics: + mIoU: 0.999 + 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 + Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py + + + + - Name: fcn_unet_s5-d16_256x256_40k_hrf + In Collection: FCN + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: HRF + Metrics: + mIoU: 2.525 + 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-df3ec8c4.pth + Config: configs/unet-s5-d16/fcn_unet_s5-d16_256x256_40k_hrf.py + + + + - Name: pspnet_unet_s5-d16_256x256_40k_hrf + In Collection: PSPNet + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: HRF + Metrics: + mIoU: 2.588 + 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 + Config: configs/unet-s5-d16/pspnet_unet_s5-d16_256x256_40k_hrf.py + + + + - Name: deeplabv3_unet_s5-d16_256x256_40k_hrf + In Collection: DeepLabV3 + Metadata: + inference time (fps): None + Results: + - Task: Semantic Segmentation + Dataset: HRF + Metrics: + mIoU: 2.604 + 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 + Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_256x256_40k_hrf.py diff --git a/configs/upernet/metafile.yml b/configs/upernet/metafile.yml new file mode 100644 index 0000000000..315c25568e --- /dev/null +++ b/configs/upernet/metafile.yml @@ -0,0 +1,231 @@ +Collections: + - Name: UPerNet + Metadata: + Training Data: + - Cityscapes + - Pascal VOC 2012 + Aug + - ADE20K + +Models: + + - Name: upernet_r50_512x1024_40k_cityscapes + In Collection: UPerNet + Metadata: + inference time (fps): 4.25 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.10 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth + Config: configs/upernet/upernet_r50_512x1024_40k_cityscapes.py + + + + - Name: upernet_r101_512x1024_40k_cityscapes + In Collection: UPerNet + Metadata: + inference time (fps): 3.79 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.69 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth + Config: configs/upernet/upernet_r101_512x1024_40k_cityscapes.py + + + + - Name: upernet_r50_769x769_40k_cityscapes + In Collection: UPerNet + Metadata: + inference time (fps): 1.76 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.98 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth + Config: configs/upernet/upernet_r50_769x769_40k_cityscapes.py + + + + - Name: upernet_r101_769x769_40k_cityscapes + In Collection: UPerNet + Metadata: + inference time (fps): 1.56 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.03 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth + Config: configs/upernet/upernet_r101_769x769_40k_cityscapes.py + + + + - Name: upernet_r50_512x1024_80k_cityscapes + In Collection: UPerNet + Metadata: + inference time (fps): 4.25 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.19 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth + Config: configs/upernet/upernet_r50_512x1024_80k_cityscapes.py + + + + - Name: upernet_r101_512x1024_80k_cityscapes + In Collection: UPerNet + Metadata: + inference time (fps): 3.79 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.40 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth + Config: configs/upernet/upernet_r101_512x1024_80k_cityscapes.py + + + + - Name: upernet_r50_769x769_80k_cityscapes + In Collection: UPerNet + Metadata: + inference time (fps): 1.76 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.39 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth + Config: configs/upernet/upernet_r50_769x769_80k_cityscapes.py + + + + - Name: upernet_r101_769x769_80k_cityscapes + In Collection: UPerNet + Metadata: + inference time (fps): 1.56 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.10 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth + Config: configs/upernet/upernet_r101_769x769_80k_cityscapes.py + + + + - Name: upernet_r50_512x512_80k_ade20k + In Collection: UPerNet + Metadata: + inference time (fps): 23.40 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 40.70 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth + Config: configs/upernet/upernet_r50_512x512_80k_ade20k.py + + + + - Name: upernet_r101_512x512_80k_ade20k + In Collection: UPerNet + Metadata: + inference time (fps): 20.34 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.91 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth + Config: configs/upernet/upernet_r101_512x512_80k_ade20k.py + + + + - Name: upernet_r50_512x512_160k_ade20k + In Collection: UPerNet + Metadata: + inference time (fps): 23.40 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.05 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth + Config: configs/upernet/upernet_r50_512x512_160k_ade20k.py + + + + - Name: upernet_r101_512x512_160k_ade20k + In Collection: UPerNet + Metadata: + inference time (fps): 20.34 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.82 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth + Config: configs/upernet/upernet_r101_512x512_160k_ade20k.py + + + + - Name: upernet_r50_512x512_20k_voc12aug + In Collection: UPerNet + Metadata: + inference time (fps): 23.17 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 74.82 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth + Config: configs/upernet/upernet_r50_512x512_20k_voc12aug.py + + + + - Name: upernet_r101_512x512_20k_voc12aug + In Collection: UPerNet + Metadata: + inference time (fps): 19.98 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.10 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth + Config: configs/upernet/upernet_r101_512x512_20k_voc12aug.py + + + + - Name: upernet_r50_512x512_40k_voc12aug + In Collection: UPerNet + Metadata: + inference time (fps): 23.17 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 75.92 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth + Config: configs/upernet/upernet_r50_512x512_40k_voc12aug.py + + + + - Name: upernet_r101_512x512_40k_voc12aug + In Collection: UPerNet + Metadata: + inference time (fps): 19.98 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.43 + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth + Config: configs/upernet/upernet_r101_512x512_40k_voc12aug.py diff --git a/model_zoo.yml b/model_zoo.yml new file mode 100644 index 0000000000..6a95f49c32 --- /dev/null +++ b/model_zoo.yml @@ -0,0 +1,27 @@ +Import: + - configs/ann/metafile.yml + - configs/apcnet/metafile.yml + - configs/ccnet/metafile.yml + - configs/cgnet/metafile.yml + - configs/danet/metafile.yml + - configs/deeplabv3/metafile.yml + - configs/deeplabv3plus/metafile.yml + - configs/dnlnet/metafile.yml + - configs/emanet/metafile.yml + - configs/encnet/metafile.yml + - configs/fastscnn/metafile.yml + - configs/fcn/metafile.yml + - configs/fp16/metafile.yml + - configs/gcnet/metafile.yml + - configs/hrnet/metafile.yml + - configs/mobilenet_v2/metafile.yml + - configs/mobilenet_v3/metafile.yml + - configs/nonlocal_net/metafile.yml + - configs/ocrnet/metafile.yml + - configs/point_rend/metafile.yml + - configs/psanet/metafile.yml + - configs/pspnet/metafile.yml + - configs/resnest/metafile.yml + - configs/sem_fpn/metafile.yml + - configs/unet/metafile.yml + - configs/upernet/metafile.yml diff --git a/requirements/mminstall.txt b/requirements/mminstall.txt new file mode 100644 index 0000000000..b1c42eb464 --- /dev/null +++ b/requirements/mminstall.txt @@ -0,0 +1 @@ +mmcv-full>=1.3.1,<=1.4.0 diff --git a/setup.py b/setup.py index 2e69551b8f..321664bcdd 100755 --- a/setup.py +++ b/setup.py @@ -104,6 +104,7 @@ def gen_packages_items(): keywords='computer vision, semantic segmentation', url='http://github.com/open-mmlab/mmsegmentation', packages=find_packages(exclude=('configs', 'tools', 'demo')), + include_package_data=True, classifiers=[ 'Development Status :: 4 - Beta', 'License :: OSI Approved :: Apache Software License',