From 372646caf5c7b78817e3b29c02e4b2d27f5ac08f Mon Sep 17 00:00:00 2001 From: Junjun2016 Date: Thu, 1 Jul 2021 22:31:00 +0800 Subject: [PATCH] update metafiles (#661) * update metafiles * update metafiles --- configs/ann/metafile.yml | 112 +++++++++++-- configs/apcnet/metafile.yml | 84 ++++++++-- configs/ccnet/metafile.yml | 112 +++++++++++-- configs/cgnet/metafile.yml | 14 +- configs/danet/metafile.yml | 112 +++++++++++-- configs/deeplabv3/metafile.yml | 210 ++++++++++++++++++++---- configs/deeplabv3plus/metafile.yml | 210 ++++++++++++++++++++---- configs/dmnet/metafile.yml | 84 ++++++++-- configs/dnlnet/metafile.yml | 84 ++++++++-- configs/emanet/metafile.yml | 28 +++- configs/encnet/metafile.yml | 84 ++++++++-- configs/fastscnn/metafile.yml | 7 +- configs/fcn/metafile.yml | 252 ++++++++++++++++++++++++----- configs/fp16/metafile.yml | 28 +++- configs/gcnet/metafile.yml | 112 +++++++++++-- configs/hrnet/metafile.yml | 175 +++++++++++++++++--- configs/mobilenet_v2/metafile.yml | 56 ++++++- configs/mobilenet_v3/metafile.yml | 28 +++- configs/nonlocal_net/metafile.yml | 112 +++++++++++-- configs/ocrnet/metafile.yml | 168 ++++++++++++++++--- configs/point_rend/metafile.yml | 28 +++- configs/psanet/metafile.yml | 112 +++++++++++-- configs/pspnet/metafile.yml | 196 ++++++++++++++++++---- configs/resnest/metafile.yml | 56 ++++++- configs/sem_fpn/metafile.yml | 28 +++- configs/unet/metafile.yml | 84 ++++++++-- configs/upernet/metafile.yml | 112 +++++++++++-- model_zoo.yml => model-index.yml | 0 28 files changed, 2304 insertions(+), 384 deletions(-) rename model_zoo.yml => model-index.yml (100%) diff --git a/configs/ann/metafile.yml b/configs/ann/metafile.yml index 03752dde54..485da6c481 100644 --- a/configs/ann/metafile.yml +++ b/configs/ann/metafile.yml @@ -11,7 +11,12 @@ Models: - Name: ann_r50-d8_512x1024_40k_cityscapes In Collection: ANN Metadata: - inference time (ms/im): 269.54 + inference time (ms/im): + - value: 269.54 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -25,7 +30,12 @@ Models: - Name: ann_r101-d8_512x1024_40k_cityscapes In Collection: ANN Metadata: - inference time (ms/im): 392.16 + inference time (ms/im): + - value: 392.16 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -39,7 +49,12 @@ Models: - Name: ann_r50-d8_769x769_40k_cityscapes In Collection: ANN Metadata: - inference time (ms/im): 588.24 + inference time (ms/im): + - value: 588.24 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -53,7 +68,12 @@ Models: - Name: ann_r101-d8_769x769_40k_cityscapes In Collection: ANN Metadata: - inference time (ms/im): 869.57 + inference time (ms/im): + - value: 869.57 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -67,7 +87,12 @@ Models: - Name: ann_r50-d8_512x1024_80k_cityscapes In Collection: ANN Metadata: - inference time (ms/im): 269.54 + inference time (ms/im): + - value: 269.54 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -81,7 +106,12 @@ Models: - Name: ann_r101-d8_512x1024_80k_cityscapes In Collection: ANN Metadata: - inference time (ms/im): 392.16 + inference time (ms/im): + - value: 392.16 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -95,7 +125,12 @@ Models: - Name: ann_r50-d8_769x769_80k_cityscapes In Collection: ANN Metadata: - inference time (ms/im): 588.24 + inference time (ms/im): + - value: 588.24 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -109,7 +144,12 @@ Models: - Name: ann_r101-d8_769x769_80k_cityscapes In Collection: ANN Metadata: - inference time (ms/im): 869.57 + inference time (ms/im): + - value: 869.57 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -123,7 +163,12 @@ Models: - Name: ann_r50-d8_512x512_80k_ade20k In Collection: ANN Metadata: - inference time (ms/im): 47.6 + inference time (ms/im): + - value: 47.6 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -137,7 +182,12 @@ Models: - Name: ann_r101-d8_512x512_80k_ade20k In Collection: ANN Metadata: - inference time (ms/im): 70.82 + inference time (ms/im): + - value: 70.82 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -151,7 +201,12 @@ Models: - Name: ann_r50-d8_512x512_160k_ade20k In Collection: ANN Metadata: - inference time (ms/im): 47.6 + inference time (ms/im): + - value: 47.6 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -165,7 +220,12 @@ Models: - Name: ann_r101-d8_512x512_160k_ade20k In Collection: ANN Metadata: - inference time (ms/im): 70.82 + inference time (ms/im): + - value: 70.82 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -179,7 +239,12 @@ Models: - Name: ann_r50-d8_512x512_20k_voc12aug In Collection: ANN Metadata: - inference time (ms/im): 47.8 + inference time (ms/im): + - value: 47.8 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -193,7 +258,12 @@ Models: - Name: ann_r101-d8_512x512_20k_voc12aug In Collection: ANN Metadata: - inference time (ms/im): 71.74 + inference time (ms/im): + - value: 71.74 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -207,7 +277,12 @@ Models: - Name: ann_r50-d8_512x512_40k_voc12aug In Collection: ANN Metadata: - inference time (ms/im): 47.8 + inference time (ms/im): + - value: 47.8 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -221,7 +296,12 @@ Models: - Name: ann_r101-d8_512x512_40k_voc12aug In Collection: ANN Metadata: - inference time (ms/im): 71.74 + inference time (ms/im): + - value: 71.74 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug diff --git a/configs/apcnet/metafile.yml b/configs/apcnet/metafile.yml index a5eb1012c8..1bf635ef82 100644 --- a/configs/apcnet/metafile.yml +++ b/configs/apcnet/metafile.yml @@ -10,7 +10,12 @@ Models: - Name: apcnet_r50-d8_512x1024_40k_cityscapes In Collection: APCNet Metadata: - inference time (ms/im): 280.11 + inference time (ms/im): + - value: 280.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -24,7 +29,12 @@ Models: - Name: apcnet_r101-d8_512x1024_40k_cityscapes In Collection: APCNet Metadata: - inference time (ms/im): 465.12 + inference time (ms/im): + - value: 465.12 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -38,7 +48,12 @@ Models: - Name: apcnet_r50-d8_769x769_40k_cityscapes In Collection: APCNet Metadata: - inference time (ms/im): 657.89 + inference time (ms/im): + - value: 657.89 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -52,7 +67,12 @@ Models: - Name: apcnet_r101-d8_769x769_40k_cityscapes In Collection: APCNet Metadata: - inference time (ms/im): 970.87 + inference time (ms/im): + - value: 970.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -66,7 +86,12 @@ Models: - Name: apcnet_r50-d8_512x1024_80k_cityscapes In Collection: APCNet Metadata: - inference time (ms/im): 280.11 + inference time (ms/im): + - value: 280.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -80,7 +105,12 @@ Models: - Name: apcnet_r101-d8_512x1024_80k_cityscapes In Collection: APCNet Metadata: - inference time (ms/im): 465.12 + inference time (ms/im): + - value: 465.12 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -94,7 +124,12 @@ Models: - Name: apcnet_r50-d8_769x769_80k_cityscapes In Collection: APCNet Metadata: - inference time (ms/im): 657.89 + inference time (ms/im): + - value: 657.89 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -108,7 +143,12 @@ Models: - Name: apcnet_r101-d8_769x769_80k_cityscapes In Collection: APCNet Metadata: - inference time (ms/im): 970.87 + inference time (ms/im): + - value: 970.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -122,7 +162,12 @@ Models: - Name: apcnet_r50-d8_512x512_80k_ade20k In Collection: APCNet Metadata: - inference time (ms/im): 50.99 + inference time (ms/im): + - value: 50.99 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -136,7 +181,12 @@ Models: - Name: apcnet_r101-d8_512x512_80k_ade20k In Collection: APCNet Metadata: - inference time (ms/im): 76.34 + inference time (ms/im): + - value: 76.34 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -150,7 +200,12 @@ Models: - Name: apcnet_r50-d8_512x512_160k_ade20k In Collection: APCNet Metadata: - inference time (ms/im): 50.99 + inference time (ms/im): + - value: 50.99 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -164,7 +219,12 @@ Models: - Name: apcnet_r101-d8_512x512_160k_ade20k In Collection: APCNet Metadata: - inference time (ms/im): 76.34 + inference time (ms/im): + - value: 76.34 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K diff --git a/configs/ccnet/metafile.yml b/configs/ccnet/metafile.yml index da712342c1..3f3c2dd4fd 100644 --- a/configs/ccnet/metafile.yml +++ b/configs/ccnet/metafile.yml @@ -11,7 +11,12 @@ Models: - Name: ccnet_r50-d8_512x1024_40k_cityscapes In Collection: CCNet Metadata: - inference time (ms/im): 301.2 + inference time (ms/im): + - value: 301.2 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -25,7 +30,12 @@ Models: - Name: ccnet_r101-d8_512x1024_40k_cityscapes In Collection: CCNet Metadata: - inference time (ms/im): 432.9 + inference time (ms/im): + - value: 432.9 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -39,7 +49,12 @@ Models: - Name: ccnet_r50-d8_769x769_40k_cityscapes In Collection: CCNet Metadata: - inference time (ms/im): 699.3 + inference time (ms/im): + - value: 699.3 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -53,7 +68,12 @@ Models: - Name: ccnet_r101-d8_769x769_40k_cityscapes In Collection: CCNet Metadata: - inference time (ms/im): 990.1 + inference time (ms/im): + - value: 990.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -67,7 +87,12 @@ Models: - Name: ccnet_r50-d8_512x1024_80k_cityscapes In Collection: CCNet Metadata: - inference time (ms/im): 301.2 + inference time (ms/im): + - value: 301.2 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -81,7 +106,12 @@ Models: - Name: ccnet_r101-d8_512x1024_80k_cityscapes In Collection: CCNet Metadata: - inference time (ms/im): 432.9 + inference time (ms/im): + - value: 432.9 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -95,7 +125,12 @@ Models: - Name: ccnet_r50-d8_769x769_80k_cityscapes In Collection: CCNet Metadata: - inference time (ms/im): 699.3 + inference time (ms/im): + - value: 699.3 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -109,7 +144,12 @@ Models: - Name: ccnet_r101-d8_769x769_80k_cityscapes In Collection: CCNet Metadata: - inference time (ms/im): 990.1 + inference time (ms/im): + - value: 990.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -123,7 +163,12 @@ Models: - Name: ccnet_r50-d8_512x512_80k_ade20k In Collection: CCNet Metadata: - inference time (ms/im): 47.87 + inference time (ms/im): + - value: 47.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -137,7 +182,12 @@ Models: - Name: ccnet_r101-d8_512x512_80k_ade20k In Collection: CCNet Metadata: - inference time (ms/im): 70.87 + inference time (ms/im): + - value: 70.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -151,7 +201,12 @@ Models: - Name: ccnet_r50-d8_512x512_160k_ade20k In Collection: CCNet Metadata: - inference time (ms/im): 47.87 + inference time (ms/im): + - value: 47.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -165,7 +220,12 @@ Models: - Name: ccnet_r101-d8_512x512_160k_ade20k In Collection: CCNet Metadata: - inference time (ms/im): 70.87 + inference time (ms/im): + - value: 70.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -179,7 +239,12 @@ Models: - Name: ccnet_r50-d8_512x512_20k_voc12aug In Collection: CCNet Metadata: - inference time (ms/im): 48.9 + inference time (ms/im): + - value: 48.9 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -193,7 +258,12 @@ Models: - Name: ccnet_r101-d8_512x512_20k_voc12aug In Collection: CCNet Metadata: - inference time (ms/im): 73.31 + inference time (ms/im): + - value: 73.31 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -207,7 +277,12 @@ Models: - Name: ccnet_r50-d8_512x512_40k_voc12aug In Collection: CCNet Metadata: - inference time (ms/im): 48.9 + inference time (ms/im): + - value: 48.9 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -221,7 +296,12 @@ Models: - Name: ccnet_r101-d8_512x512_40k_voc12aug In Collection: CCNet Metadata: - inference time (ms/im): 73.31 + inference time (ms/im): + - value: 73.31 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug diff --git a/configs/cgnet/metafile.yml b/configs/cgnet/metafile.yml index 65ef6f8586..b138ae68ab 100644 --- a/configs/cgnet/metafile.yml +++ b/configs/cgnet/metafile.yml @@ -9,7 +9,12 @@ Models: - Name: cgnet_680x680_60k_cityscapes In Collection: CGNet Metadata: - inference time (ms/im): 32.78 + inference time (ms/im): + - value: 32.78 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -23,7 +28,12 @@ Models: - Name: cgnet_512x1024_60k_cityscapes In Collection: CGNet Metadata: - inference time (ms/im): 32.11 + inference time (ms/im): + - value: 32.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes diff --git a/configs/danet/metafile.yml b/configs/danet/metafile.yml index 6075fd610c..d4b537c27e 100644 --- a/configs/danet/metafile.yml +++ b/configs/danet/metafile.yml @@ -11,7 +11,12 @@ Models: - Name: danet_r50-d8_512x1024_40k_cityscapes In Collection: DANet Metadata: - inference time (ms/im): 375.94 + inference time (ms/im): + - value: 375.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -25,7 +30,12 @@ Models: - Name: danet_r101-d8_512x1024_40k_cityscapes In Collection: DANet Metadata: - inference time (ms/im): 502.51 + inference time (ms/im): + - value: 502.51 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -39,7 +49,12 @@ Models: - Name: danet_r50-d8_769x769_40k_cityscapes In Collection: DANet Metadata: - inference time (ms/im): 641.03 + inference time (ms/im): + - value: 641.03 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -53,7 +68,12 @@ Models: - Name: danet_r101-d8_769x769_40k_cityscapes In Collection: DANet Metadata: - inference time (ms/im): 934.58 + inference time (ms/im): + - value: 934.58 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -67,7 +87,12 @@ Models: - Name: danet_r50-d8_512x1024_80k_cityscapes In Collection: DANet Metadata: - inference time (ms/im): 375.94 + inference time (ms/im): + - value: 375.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -81,7 +106,12 @@ Models: - Name: danet_r101-d8_512x1024_80k_cityscapes In Collection: DANet Metadata: - inference time (ms/im): 502.51 + inference time (ms/im): + - value: 502.51 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -95,7 +125,12 @@ Models: - Name: danet_r50-d8_769x769_80k_cityscapes In Collection: DANet Metadata: - inference time (ms/im): 641.03 + inference time (ms/im): + - value: 641.03 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -109,7 +144,12 @@ Models: - Name: danet_r101-d8_769x769_80k_cityscapes In Collection: DANet Metadata: - inference time (ms/im): 934.58 + inference time (ms/im): + - value: 934.58 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -123,7 +163,12 @@ Models: - Name: danet_r50-d8_512x512_80k_ade20k In Collection: DANet Metadata: - inference time (ms/im): 47.17 + inference time (ms/im): + - value: 47.17 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -137,7 +182,12 @@ Models: - Name: danet_r101-d8_512x512_80k_ade20k In Collection: DANet Metadata: - inference time (ms/im): 70.52 + inference time (ms/im): + - value: 70.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -151,7 +201,12 @@ Models: - Name: danet_r50-d8_512x512_160k_ade20k In Collection: DANet Metadata: - inference time (ms/im): 47.17 + inference time (ms/im): + - value: 47.17 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -165,7 +220,12 @@ Models: - Name: danet_r101-d8_512x512_160k_ade20k In Collection: DANet Metadata: - inference time (ms/im): 70.52 + inference time (ms/im): + - value: 70.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -179,7 +239,12 @@ Models: - Name: danet_r50-d8_512x512_20k_voc12aug In Collection: DANet Metadata: - inference time (ms/im): 47.76 + inference time (ms/im): + - value: 47.76 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -193,7 +258,12 @@ Models: - Name: danet_r101-d8_512x512_20k_voc12aug In Collection: DANet Metadata: - inference time (ms/im): 72.67 + inference time (ms/im): + - value: 72.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -207,7 +277,12 @@ Models: - Name: danet_r50-d8_512x512_40k_voc12aug In Collection: DANet Metadata: - inference time (ms/im): 47.76 + inference time (ms/im): + - value: 47.76 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -221,7 +296,12 @@ Models: - Name: danet_r101-d8_512x512_40k_voc12aug In Collection: DANet Metadata: - inference time (ms/im): 72.67 + inference time (ms/im): + - value: 72.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug diff --git a/configs/deeplabv3/metafile.yml b/configs/deeplabv3/metafile.yml index c3f154d83b..bf8c490c68 100644 --- a/configs/deeplabv3/metafile.yml +++ b/configs/deeplabv3/metafile.yml @@ -12,7 +12,12 @@ Models: - Name: deeplabv3_r50-d8_512x1024_40k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 389.11 + inference time (ms/im): + - value: 389.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -26,7 +31,12 @@ Models: - Name: deeplabv3_r101-d8_512x1024_40k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 520.83 + inference time (ms/im): + - value: 520.83 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -40,7 +50,12 @@ Models: - Name: deeplabv3_r50-d8_769x769_40k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 900.9 + inference time (ms/im): + - value: 900.9 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -54,7 +69,12 @@ Models: - Name: deeplabv3_r101-d8_769x769_40k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 1204.82 + inference time (ms/im): + - value: 1204.82 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -68,7 +88,12 @@ Models: - Name: deeplabv3_r18-d8_512x1024_80k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 72.57 + inference time (ms/im): + - value: 72.57 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -82,7 +107,12 @@ Models: - Name: deeplabv3_r50-d8_512x1024_80k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 389.11 + inference time (ms/im): + - value: 389.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -96,7 +126,12 @@ Models: - Name: deeplabv3_r101-d8_512x1024_80k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 520.83 + inference time (ms/im): + - value: 520.83 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -110,7 +145,12 @@ Models: - Name: deeplabv3_r18-d8_769x769_80k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 180.18 + inference time (ms/im): + - value: 180.18 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -124,7 +164,12 @@ Models: - Name: deeplabv3_r50-d8_769x769_80k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 900.9 + inference time (ms/im): + - value: 900.9 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -138,7 +183,12 @@ Models: - Name: deeplabv3_r101-d8_769x769_80k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 1204.82 + inference time (ms/im): + - value: 1204.82 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -152,7 +202,12 @@ Models: - Name: deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 143.68 + inference time (ms/im): + - value: 143.68 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -166,7 +221,12 @@ Models: - Name: deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 143.68 + inference time (ms/im): + - value: 143.68 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -180,7 +240,12 @@ Models: - Name: deeplabv3_r18b-d8_512x1024_80k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 71.79 + inference time (ms/im): + - value: 71.79 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -194,7 +259,12 @@ Models: - Name: deeplabv3_r50b-d8_512x1024_80k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 364.96 + inference time (ms/im): + - value: 364.96 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -208,7 +278,12 @@ Models: - Name: deeplabv3_r101b-d8_512x1024_80k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 552.49 + inference time (ms/im): + - value: 552.49 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -222,7 +297,12 @@ Models: - Name: deeplabv3_r18b-d8_769x769_80k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 172.71 + inference time (ms/im): + - value: 172.71 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -236,7 +316,12 @@ Models: - Name: deeplabv3_r50b-d8_769x769_80k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 862.07 + inference time (ms/im): + - value: 862.07 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -250,7 +335,12 @@ Models: - Name: deeplabv3_r101b-d8_769x769_80k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 1219.51 + inference time (ms/im): + - value: 1219.51 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -264,7 +354,12 @@ Models: - Name: deeplabv3_r50-d8_512x512_80k_ade20k In Collection: DeepLabV3 Metadata: - inference time (ms/im): 67.75 + inference time (ms/im): + - value: 67.75 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -278,7 +373,12 @@ Models: - Name: deeplabv3_r101-d8_512x512_80k_ade20k In Collection: DeepLabV3 Metadata: - inference time (ms/im): 98.62 + inference time (ms/im): + - value: 98.62 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -292,7 +392,12 @@ Models: - Name: deeplabv3_r50-d8_512x512_160k_ade20k In Collection: DeepLabV3 Metadata: - inference time (ms/im): 67.75 + inference time (ms/im): + - value: 67.75 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -306,7 +411,12 @@ Models: - Name: deeplabv3_r101-d8_512x512_160k_ade20k In Collection: DeepLabV3 Metadata: - inference time (ms/im): 98.62 + inference time (ms/im): + - value: 98.62 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -320,7 +430,12 @@ Models: - Name: deeplabv3_r50-d8_512x512_20k_voc12aug In Collection: DeepLabV3 Metadata: - inference time (ms/im): 72.05 + inference time (ms/im): + - value: 72.05 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -334,7 +449,12 @@ Models: - Name: deeplabv3_r101-d8_512x512_20k_voc12aug In Collection: DeepLabV3 Metadata: - inference time (ms/im): 101.94 + inference time (ms/im): + - value: 101.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -348,7 +468,12 @@ Models: - Name: deeplabv3_r50-d8_512x512_40k_voc12aug In Collection: DeepLabV3 Metadata: - inference time (ms/im): 72.05 + inference time (ms/im): + - value: 72.05 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -362,7 +487,12 @@ Models: - Name: deeplabv3_r101-d8_512x512_40k_voc12aug In Collection: DeepLabV3 Metadata: - inference time (ms/im): 101.94 + inference time (ms/im): + - value: 101.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -376,7 +506,12 @@ Models: - Name: deeplabv3_r101-d8_480x480_40k_pascal_context In Collection: DeepLabV3 Metadata: - inference time (ms/im): 141.04 + inference time (ms/im): + - value: 141.04 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context @@ -390,7 +525,12 @@ Models: - Name: deeplabv3_r101-d8_480x480_80k_pascal_context In Collection: DeepLabV3 Metadata: - inference time (ms/im): 141.04 + inference time (ms/im): + - value: 141.04 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context @@ -404,7 +544,12 @@ Models: - Name: deeplabv3_r101-d8_480x480_40k_pascal_context In Collection: DeepLabV3 Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context @@ -418,7 +563,12 @@ Models: - Name: deeplabv3_r101-d8_480x480_80k_pascal_context_59 In Collection: DeepLabV3 Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context diff --git a/configs/deeplabv3plus/metafile.yml b/configs/deeplabv3plus/metafile.yml index e515d30223..f2bbc551a3 100644 --- a/configs/deeplabv3plus/metafile.yml +++ b/configs/deeplabv3plus/metafile.yml @@ -12,7 +12,12 @@ Models: - Name: deeplabv3plus_r50-d8_512x1024_40k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 253.81 + inference time (ms/im): + - value: 253.81 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -26,7 +31,12 @@ Models: - Name: deeplabv3plus_r101-d8_512x1024_40k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 384.62 + inference time (ms/im): + - value: 384.62 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -40,7 +50,12 @@ Models: - Name: deeplabv3plus_r50-d8_769x769_40k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 581.4 + inference time (ms/im): + - value: 581.4 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -54,7 +69,12 @@ Models: - Name: deeplabv3plus_r101-d8_769x769_40k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 869.57 + inference time (ms/im): + - value: 869.57 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -68,7 +88,12 @@ Models: - Name: deeplabv3plus_r18-d8_512x1024_80k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 70.08 + inference time (ms/im): + - value: 70.08 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -82,7 +107,12 @@ Models: - Name: deeplabv3plus_r50-d8_512x1024_80k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 253.81 + inference time (ms/im): + - value: 253.81 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -96,7 +126,12 @@ Models: - Name: deeplabv3plus_r101-d8_512x1024_80k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 384.62 + inference time (ms/im): + - value: 384.62 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -110,7 +145,12 @@ Models: - Name: deeplabv3plus_r18-d8_769x769_80k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 174.22 + inference time (ms/im): + - value: 174.22 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -124,7 +164,12 @@ Models: - Name: deeplabv3plus_r50-d8_769x769_80k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 581.4 + inference time (ms/im): + - value: 581.4 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -138,7 +183,12 @@ Models: - Name: deeplabv3plus_r101-d8_769x769_80k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 869.57 + inference time (ms/im): + - value: 869.57 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -152,7 +202,12 @@ Models: - Name: deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 133.69 + inference time (ms/im): + - value: 133.69 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -166,7 +221,12 @@ Models: - Name: deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 133.69 + inference time (ms/im): + - value: 133.69 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -180,7 +240,12 @@ Models: - Name: deeplabv3plus_r18b-d8_512x1024_80k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 66.89 + inference time (ms/im): + - value: 66.89 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -194,7 +259,12 @@ Models: - Name: deeplabv3plus_r50b-d8_512x1024_80k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 253.81 + inference time (ms/im): + - value: 253.81 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -208,7 +278,12 @@ Models: - Name: deeplabv3plus_r101b-d8_512x1024_80k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 384.62 + inference time (ms/im): + - value: 384.62 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -222,7 +297,12 @@ Models: - Name: deeplabv3plus_r18b-d8_769x769_80k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 167.79 + inference time (ms/im): + - value: 167.79 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -236,7 +316,12 @@ Models: - Name: deeplabv3plus_r50b-d8_769x769_80k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 581.4 + inference time (ms/im): + - value: 581.4 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -250,7 +335,12 @@ Models: - Name: deeplabv3plus_r101b-d8_769x769_80k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 909.09 + inference time (ms/im): + - value: 909.09 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -264,7 +354,12 @@ Models: - Name: deeplabv3plus_r50-d8_512x512_80k_ade20k In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 47.6 + inference time (ms/im): + - value: 47.6 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -278,7 +373,12 @@ Models: - Name: deeplabv3plus_r101-d8_512x512_80k_ade20k In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 70.62 + inference time (ms/im): + - value: 70.62 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -292,7 +392,12 @@ Models: - Name: deeplabv3plus_r50-d8_512x512_160k_ade20k In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 47.6 + inference time (ms/im): + - value: 47.6 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -306,7 +411,12 @@ Models: - Name: deeplabv3plus_r101-d8_512x512_160k_ade20k In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 70.62 + inference time (ms/im): + - value: 70.62 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -320,7 +430,12 @@ Models: - Name: deeplabv3plus_r50-d8_512x512_20k_voc12aug In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 47.62 + inference time (ms/im): + - value: 47.62 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -334,7 +449,12 @@ Models: - Name: deeplabv3plus_r101-d8_512x512_20k_voc12aug In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 72.05 + inference time (ms/im): + - value: 72.05 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -348,7 +468,12 @@ Models: - Name: deeplabv3plus_r50-d8_512x512_40k_voc12aug In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 47.62 + inference time (ms/im): + - value: 47.62 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -362,7 +487,12 @@ Models: - Name: deeplabv3plus_r101-d8_512x512_40k_voc12aug In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 72.05 + inference time (ms/im): + - value: 72.05 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -376,7 +506,12 @@ Models: - Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 110.01 + inference time (ms/im): + - value: 110.01 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context @@ -390,7 +525,12 @@ Models: - Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 110.01 + inference time (ms/im): + - value: 110.01 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context @@ -404,7 +544,12 @@ Models: - Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context In Collection: DeepLabV3+ Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context @@ -418,7 +563,12 @@ Models: - Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context In Collection: DeepLabV3+ Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context diff --git a/configs/dmnet/metafile.yml b/configs/dmnet/metafile.yml index fe210d5073..8ab1baa7a1 100644 --- a/configs/dmnet/metafile.yml +++ b/configs/dmnet/metafile.yml @@ -10,7 +10,12 @@ Models: - Name: dmnet_r50-d8_512x1024_40k_cityscapes In Collection: DMNet Metadata: - inference time (ms/im): 273.22 + inference time (ms/im): + - value: 273.22 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -24,7 +29,12 @@ Models: - Name: dmnet_r101-d8_512x1024_40k_cityscapes In Collection: DMNet Metadata: - inference time (ms/im): 393.7 + inference time (ms/im): + - value: 393.7 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -38,7 +48,12 @@ Models: - Name: dmnet_r50-d8_769x769_40k_cityscapes In Collection: DMNet Metadata: - inference time (ms/im): 636.94 + inference time (ms/im): + - value: 636.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -52,7 +67,12 @@ Models: - Name: dmnet_r101-d8_769x769_40k_cityscapes In Collection: DMNet Metadata: - inference time (ms/im): 990.1 + inference time (ms/im): + - value: 990.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -66,7 +86,12 @@ Models: - Name: dmnet_r50-d8_512x1024_80k_cityscapes In Collection: DMNet Metadata: - inference time (ms/im): 273.22 + inference time (ms/im): + - value: 273.22 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -80,7 +105,12 @@ Models: - Name: dmnet_r101-d8_512x1024_80k_cityscapes In Collection: DMNet Metadata: - inference time (ms/im): 393.7 + inference time (ms/im): + - value: 393.7 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -94,7 +124,12 @@ Models: - Name: dmnet_r50-d8_769x769_80k_cityscapes In Collection: DMNet Metadata: - inference time (ms/im): 636.94 + inference time (ms/im): + - value: 636.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -108,7 +143,12 @@ Models: - Name: dmnet_r101-d8_769x769_80k_cityscapes In Collection: DMNet Metadata: - inference time (ms/im): 990.1 + inference time (ms/im): + - value: 990.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -122,7 +162,12 @@ Models: - Name: dmnet_r50-d8_512x512_80k_ade20k In Collection: DMNet Metadata: - inference time (ms/im): 47.73 + inference time (ms/im): + - value: 47.73 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -136,7 +181,12 @@ Models: - Name: dmnet_r101-d8_512x512_80k_ade20k In Collection: DMNet Metadata: - inference time (ms/im): 72.05 + inference time (ms/im): + - value: 72.05 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -150,7 +200,12 @@ Models: - Name: dmnet_r50-d8_512x512_160k_ade20k In Collection: DMNet Metadata: - inference time (ms/im): 47.73 + inference time (ms/im): + - value: 47.73 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -164,7 +219,12 @@ Models: - Name: dmnet_r101-d8_512x512_160k_ade20k In Collection: DMNet Metadata: - inference time (ms/im): 72.05 + inference time (ms/im): + - value: 72.05 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K diff --git a/configs/dnlnet/metafile.yml b/configs/dnlnet/metafile.yml index 7b672e1974..2ae289be14 100644 --- a/configs/dnlnet/metafile.yml +++ b/configs/dnlnet/metafile.yml @@ -10,7 +10,12 @@ Models: - Name: dnl_r50-d8_512x1024_40k_cityscapes In Collection: dnl Metadata: - inference time (ms/im): 390.62 + inference time (ms/im): + - value: 390.62 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -24,7 +29,12 @@ Models: - Name: dnl_r101-d8_512x1024_40k_cityscapes In Collection: dnl Metadata: - inference time (ms/im): 510.2 + inference time (ms/im): + - value: 510.2 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -38,7 +48,12 @@ Models: - Name: dnl_r50-d8_769x769_40k_cityscapes In Collection: dnl Metadata: - inference time (ms/im): 666.67 + inference time (ms/im): + - value: 666.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -52,7 +67,12 @@ Models: - Name: dnl_r101-d8_769x769_40k_cityscapes In Collection: dnl Metadata: - inference time (ms/im): 980.39 + inference time (ms/im): + - value: 980.39 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -66,7 +86,12 @@ Models: - Name: dnl_r50-d8_512x1024_80k_cityscapes In Collection: dnl Metadata: - inference time (ms/im): 390.62 + inference time (ms/im): + - value: 390.62 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -80,7 +105,12 @@ Models: - Name: dnl_r101-d8_512x1024_80k_cityscapes In Collection: dnl Metadata: - inference time (ms/im): 510.2 + inference time (ms/im): + - value: 510.2 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -94,7 +124,12 @@ Models: - Name: dnl_r50-d8_769x769_80k_cityscapes In Collection: dnl Metadata: - inference time (ms/im): 666.67 + inference time (ms/im): + - value: 666.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -108,7 +143,12 @@ Models: - Name: dnl_r101-d8_769x769_80k_cityscapes In Collection: dnl Metadata: - inference time (ms/im): 980.39 + inference time (ms/im): + - value: 980.39 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -122,7 +162,12 @@ Models: - Name: dnl_r50-d8_512x512_80k_ade20k In Collection: dnl Metadata: - inference time (ms/im): 48.4 + inference time (ms/im): + - value: 48.4 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -136,7 +181,12 @@ Models: - Name: dnl_r101-d8_512x512_80k_ade20k In Collection: dnl Metadata: - inference time (ms/im): 79.74 + inference time (ms/im): + - value: 79.74 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -150,7 +200,12 @@ Models: - Name: dnl_r50-d8_512x512_160k_ade20k In Collection: dnl Metadata: - inference time (ms/im): 48.4 + inference time (ms/im): + - value: 48.4 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -164,7 +219,12 @@ Models: - Name: dnl_r101-d8_512x512_160k_ade20k In Collection: dnl Metadata: - inference time (ms/im): 79.74 + inference time (ms/im): + - value: 79.74 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K diff --git a/configs/emanet/metafile.yml b/configs/emanet/metafile.yml index 1a6bee86e9..0fa562abd6 100644 --- a/configs/emanet/metafile.yml +++ b/configs/emanet/metafile.yml @@ -9,7 +9,12 @@ Models: - Name: emanet_r50-d8_512x1024_80k_cityscapes In Collection: EMANet Metadata: - inference time (ms/im): 218.34 + inference time (ms/im): + - value: 218.34 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -23,7 +28,12 @@ Models: - Name: emanet_r101-d8_512x1024_80k_cityscapes In Collection: EMANet Metadata: - inference time (ms/im): 348.43 + inference time (ms/im): + - value: 348.43 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -37,7 +47,12 @@ Models: - Name: emanet_r50-d8_769x769_80k_cityscapes In Collection: EMANet Metadata: - inference time (ms/im): 507.61 + inference time (ms/im): + - value: 507.61 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -51,7 +66,12 @@ Models: - Name: emanet_r101-d8_769x769_80k_cityscapes In Collection: EMANet Metadata: - inference time (ms/im): 819.67 + inference time (ms/im): + - value: 819.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes diff --git a/configs/encnet/metafile.yml b/configs/encnet/metafile.yml index d756507729..1e97baa509 100644 --- a/configs/encnet/metafile.yml +++ b/configs/encnet/metafile.yml @@ -11,7 +11,12 @@ Models: - Name: encnet_r50-d8_512x1024_40k_cityscapes In Collection: encnet Metadata: - inference time (ms/im): 218.34 + inference time (ms/im): + - value: 218.34 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -25,7 +30,12 @@ Models: - Name: encnet_r101-d8_512x1024_40k_cityscapes In Collection: encnet Metadata: - inference time (ms/im): 375.94 + inference time (ms/im): + - value: 375.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -39,7 +49,12 @@ Models: - Name: encnet_r50-d8_769x769_40k_cityscapes In Collection: encnet Metadata: - inference time (ms/im): 549.45 + inference time (ms/im): + - value: 549.45 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -53,7 +68,12 @@ Models: - Name: encnet_r101-d8_769x769_40k_cityscapes In Collection: encnet Metadata: - inference time (ms/im): 793.65 + inference time (ms/im): + - value: 793.65 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -67,7 +87,12 @@ Models: - Name: encnet_r50-d8_512x1024_80k_cityscapes In Collection: encnet Metadata: - inference time (ms/im): 218.34 + inference time (ms/im): + - value: 218.34 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -81,7 +106,12 @@ Models: - Name: encnet_r101-d8_512x1024_80k_cityscapes In Collection: encnet Metadata: - inference time (ms/im): 375.94 + inference time (ms/im): + - value: 375.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -95,7 +125,12 @@ Models: - Name: encnet_r50-d8_769x769_80k_cityscapes In Collection: encnet Metadata: - inference time (ms/im): 549.45 + inference time (ms/im): + - value: 549.45 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -109,7 +144,12 @@ Models: - Name: encnet_r101-d8_769x769_80k_cityscapes In Collection: encnet Metadata: - inference time (ms/im): 793.65 + inference time (ms/im): + - value: 793.65 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -123,7 +163,12 @@ Models: - Name: encnet_r50-d8_512x512_80k_ade20k In Collection: encnet Metadata: - inference time (ms/im): 43.84 + inference time (ms/im): + - value: 43.84 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -137,7 +182,12 @@ Models: - Name: encnet_r101-d8_512x512_80k_ade20k In Collection: encnet Metadata: - inference time (ms/im): 67.25 + inference time (ms/im): + - value: 67.25 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -151,7 +201,12 @@ Models: - Name: encnet_r50-d8_512x512_160k_ade20k In Collection: encnet Metadata: - inference time (ms/im): 43.84 + inference time (ms/im): + - value: 43.84 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -165,7 +220,12 @@ Models: - Name: encnet_r101-d8_512x512_160k_ade20k In Collection: encnet Metadata: - inference time (ms/im): 67.25 + inference time (ms/im): + - value: 67.25 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K diff --git a/configs/fastscnn/metafile.yml b/configs/fastscnn/metafile.yml index f87fb321da..019f1d2fdb 100644 --- a/configs/fastscnn/metafile.yml +++ b/configs/fastscnn/metafile.yml @@ -9,7 +9,12 @@ Models: - Name: fast_scnn_4x8_80k_lr0.12_cityscapes In Collection: Fast-SCNN Metadata: - inference time (ms/im): 15.72 + inference time (ms/im): + - value: 15.72 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes diff --git a/configs/fcn/metafile.yml b/configs/fcn/metafile.yml index e7927e0860..530de45559 100644 --- a/configs/fcn/metafile.yml +++ b/configs/fcn/metafile.yml @@ -19,7 +19,12 @@ Models: - Name: fcn_r50-d8_512x1024_40k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 239.81 + inference time (ms/im): + - value: 239.81 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -33,7 +38,12 @@ Models: - Name: fcn_r101-d8_512x1024_40k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 375.94 + inference time (ms/im): + - value: 375.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -47,7 +57,12 @@ Models: - Name: fcn_r50-d8_769x769_40k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 555.56 + inference time (ms/im): + - value: 555.56 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -61,7 +76,12 @@ Models: - Name: fcn_r101-d8_769x769_40k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 840.34 + inference time (ms/im): + - value: 840.34 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -75,7 +95,12 @@ Models: - Name: fcn_r18-d8_512x1024_80k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 68.26 + inference time (ms/im): + - value: 68.26 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -89,7 +114,12 @@ Models: - Name: fcn_r50-d8_512x1024_80k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 239.81 + inference time (ms/im): + - value: 239.81 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -103,7 +133,12 @@ Models: - Name: fcn_r101-d8_512x1024_80k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 375.94 + inference time (ms/im): + - value: 375.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -117,7 +152,12 @@ Models: - Name: fcn_r18-d8_769x769_80k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 156.25 + inference time (ms/im): + - value: 156.25 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -131,7 +171,12 @@ Models: - Name: fcn_r50-d8_769x769_80k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 555.56 + inference time (ms/im): + - value: 555.56 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -145,7 +190,12 @@ Models: - Name: fcn_r101-d8_769x769_80k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 840.34 + inference time (ms/im): + - value: 840.34 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -159,7 +209,12 @@ Models: - Name: fcn_r18b-d8_512x1024_80k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 59.74 + inference time (ms/im): + - value: 59.74 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -173,7 +228,12 @@ Models: - Name: fcn_r50b-d8_512x1024_80k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 238.1 + inference time (ms/im): + - value: 238.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -187,7 +247,12 @@ Models: - Name: fcn_r101b-d8_512x1024_80k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 366.3 + inference time (ms/im): + - value: 366.3 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -201,7 +266,12 @@ Models: - Name: fcn_r18b-d8_769x769_80k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 149.25 + inference time (ms/im): + - value: 149.25 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -215,7 +285,12 @@ Models: - Name: fcn_r50b-d8_769x769_80k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 549.45 + inference time (ms/im): + - value: 549.45 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -229,7 +304,12 @@ Models: - Name: fcn_r101b-d8_769x769_80k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 869.57 + inference time (ms/im): + - value: 869.57 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -243,7 +323,12 @@ Models: - Name: fcn_d6_r50-d16_512x1024_40k_cityscapes In Collection: FCN-D6 Metadata: - inference time (ms/im): 97.85 + inference time (ms/im): + - value: 97.85 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -257,7 +342,12 @@ Models: - Name: fcn_d6_r50-d16_512x1024_80k_cityscapes In Collection: FCN-D6 Metadata: - inference time (ms/im): 96.62 + inference time (ms/im): + - value: 96.62 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -271,7 +361,12 @@ Models: - Name: fcn_d6_r50-d16_769x769_40k_cityscapes In Collection: FCN-D6 Metadata: - inference time (ms/im): 239.81 + inference time (ms/im): + - value: 239.81 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -285,7 +380,12 @@ Models: - Name: fcn_d6_r50-d16_769x769_80k_cityscapes In Collection: FCN-D6 Metadata: - inference time (ms/im): 240.96 + inference time (ms/im): + - value: 240.96 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -299,7 +399,12 @@ Models: - Name: fcn_d6_r101-d16_512x1024_40k_cityscapes In Collection: FCN-D6 Metadata: - inference time (ms/im): 124.38 + inference time (ms/im): + - value: 124.38 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -313,7 +418,12 @@ Models: - Name: fcn_d6_r101-d16_512x1024_80k_cityscapes In Collection: FCN-D6 Metadata: - inference time (ms/im): 121.07 + inference time (ms/im): + - value: 121.07 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -327,7 +437,12 @@ Models: - Name: fcn_d6_r101-d16_769x769_40k_cityscapes In Collection: FCN-D6 Metadata: - inference time (ms/im): 320.51 + inference time (ms/im): + - value: 320.51 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -341,7 +456,12 @@ Models: - Name: fcn_d6_r101-d16_769x769_80k_cityscapes In Collection: FCN-D6 Metadata: - inference time (ms/im): 311.53 + inference time (ms/im): + - value: 311.53 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -355,7 +475,12 @@ Models: - Name: fcn_r50-d8_512x512_80k_ade20k In Collection: FCN Metadata: - inference time (ms/im): 42.57 + inference time (ms/im): + - value: 42.57 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -369,7 +494,12 @@ Models: - Name: fcn_r101-d8_512x512_80k_ade20k In Collection: FCN Metadata: - inference time (ms/im): 67.66 + inference time (ms/im): + - value: 67.66 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -383,7 +513,12 @@ Models: - Name: fcn_r50-d8_512x512_160k_ade20k In Collection: FCN Metadata: - inference time (ms/im): 42.57 + inference time (ms/im): + - value: 42.57 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -397,7 +532,12 @@ Models: - Name: fcn_r101-d8_512x512_160k_ade20k In Collection: FCN Metadata: - inference time (ms/im): 67.66 + inference time (ms/im): + - value: 67.66 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -411,7 +551,12 @@ Models: - Name: fcn_r50-d8_512x512_20k_voc12aug In Collection: FCN Metadata: - inference time (ms/im): 42.96 + inference time (ms/im): + - value: 42.96 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -425,7 +570,12 @@ Models: - Name: fcn_r101-d8_512x512_20k_voc12aug In Collection: FCN Metadata: - inference time (ms/im): 67.52 + inference time (ms/im): + - value: 67.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -439,7 +589,12 @@ Models: - Name: fcn_r50-d8_512x512_40k_voc12aug In Collection: FCN Metadata: - inference time (ms/im): 42.96 + inference time (ms/im): + - value: 42.96 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -453,7 +608,12 @@ Models: - Name: fcn_r101-d8_512x512_40k_voc12aug In Collection: FCN Metadata: - inference time (ms/im): 67.52 + inference time (ms/im): + - value: 67.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -467,7 +627,12 @@ Models: - Name: fcn_r101-d8_480x480_40k_pascal_context In Collection: FCN Metadata: - inference time (ms/im): 100.7 + inference time (ms/im): + - value: 100.7 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context @@ -481,7 +646,12 @@ Models: - Name: fcn_r101-d8_480x480_80k_pascal_context In Collection: FCN Metadata: - inference time (ms/im): 100.7 + inference time (ms/im): + - value: 100.7 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context @@ -495,7 +665,12 @@ Models: - Name: fcn_r101-d8_480x480_40k_pascal_context_59 In Collection: FCN Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context @@ -509,7 +684,12 @@ Models: - Name: fcn_r101-d8_480x480_80k_pascal_context_59 In Collection: FCN Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context diff --git a/configs/fp16/metafile.yml b/configs/fp16/metafile.yml index f1bf8d3bb0..841429b361 100644 --- a/configs/fp16/metafile.yml +++ b/configs/fp16/metafile.yml @@ -4,7 +4,12 @@ Models: - Name: fcn_r101-d8_512x1024_80k_fp16_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 115.74 + inference time (ms/im): + - value: 115.74 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -18,7 +23,12 @@ Models: - Name: pspnet_r101-d8_512x1024_80k_fp16_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 114.03 + inference time (ms/im): + - value: 114.03 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -32,7 +42,12 @@ Models: - Name: deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 259.07 + inference time (ms/im): + - value: 259.07 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -46,7 +61,12 @@ Models: - Name: deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 127.06 + inference time (ms/im): + - value: 127.06 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes diff --git a/configs/gcnet/metafile.yml b/configs/gcnet/metafile.yml index e5efcb85b1..c1ddc1c0b2 100644 --- a/configs/gcnet/metafile.yml +++ b/configs/gcnet/metafile.yml @@ -11,7 +11,12 @@ Models: - Name: gcnet_r50-d8_512x1024_40k_cityscapes In Collection: GCNet Metadata: - inference time (ms/im): 254.45 + inference time (ms/im): + - value: 254.45 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -25,7 +30,12 @@ Models: - Name: gcnet_r101-d8_512x1024_40k_cityscapes In Collection: GCNet Metadata: - inference time (ms/im): 383.14 + inference time (ms/im): + - value: 383.14 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -39,7 +49,12 @@ Models: - Name: gcnet_r50-d8_769x769_40k_cityscapes In Collection: GCNet Metadata: - inference time (ms/im): 598.8 + inference time (ms/im): + - value: 598.8 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -53,7 +68,12 @@ Models: - Name: gcnet_r101-d8_769x769_40k_cityscapes In Collection: GCNet Metadata: - inference time (ms/im): 884.96 + inference time (ms/im): + - value: 884.96 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -67,7 +87,12 @@ Models: - Name: gcnet_r50-d8_512x1024_80k_cityscapes In Collection: GCNet Metadata: - inference time (ms/im): 254.45 + inference time (ms/im): + - value: 254.45 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -81,7 +106,12 @@ Models: - Name: gcnet_r101-d8_512x1024_80k_cityscapes In Collection: GCNet Metadata: - inference time (ms/im): 383.14 + inference time (ms/im): + - value: 383.14 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -95,7 +125,12 @@ Models: - Name: gcnet_r50-d8_769x769_80k_cityscapes In Collection: GCNet Metadata: - inference time (ms/im): 598.8 + inference time (ms/im): + - value: 598.8 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -109,7 +144,12 @@ Models: - Name: gcnet_r101-d8_769x769_80k_cityscapes In Collection: GCNet Metadata: - inference time (ms/im): 884.96 + inference time (ms/im): + - value: 884.96 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -123,7 +163,12 @@ Models: - Name: gcnet_r50-d8_512x512_80k_ade20k In Collection: GCNet Metadata: - inference time (ms/im): 42.77 + inference time (ms/im): + - value: 42.77 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -137,7 +182,12 @@ Models: - Name: gcnet_r101-d8_512x512_80k_ade20k In Collection: GCNet Metadata: - inference time (ms/im): 65.79 + inference time (ms/im): + - value: 65.79 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -151,7 +201,12 @@ Models: - Name: gcnet_r50-d8_512x512_160k_ade20k In Collection: GCNet Metadata: - inference time (ms/im): 42.77 + inference time (ms/im): + - value: 42.77 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -165,7 +220,12 @@ Models: - Name: gcnet_r101-d8_512x512_160k_ade20k In Collection: GCNet Metadata: - inference time (ms/im): 65.79 + inference time (ms/im): + - value: 65.79 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -179,7 +239,12 @@ Models: - Name: gcnet_r50-d8_512x512_20k_voc12aug In Collection: GCNet Metadata: - inference time (ms/im): 42.83 + inference time (ms/im): + - value: 42.83 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -193,7 +258,12 @@ Models: - Name: gcnet_r101-d8_512x512_20k_voc12aug In Collection: GCNet Metadata: - inference time (ms/im): 67.57 + inference time (ms/im): + - value: 67.57 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -207,7 +277,12 @@ Models: - Name: gcnet_r50-d8_512x512_40k_voc12aug In Collection: GCNet Metadata: - inference time (ms/im): 42.83 + inference time (ms/im): + - value: 42.83 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -221,7 +296,12 @@ Models: - Name: gcnet_r101-d8_512x512_40k_voc12aug In Collection: GCNet Metadata: - inference time (ms/im): 67.57 + inference time (ms/im): + - value: 67.57 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug diff --git a/configs/hrnet/metafile.yml b/configs/hrnet/metafile.yml index e96a05919b..b57776bc56 100644 --- a/configs/hrnet/metafile.yml +++ b/configs/hrnet/metafile.yml @@ -2,7 +2,12 @@ Models: - Name: fcn_hr18s_512x1024_40k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 42.12 + inference time (ms/im): + - value: 42.12 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -16,7 +21,12 @@ Models: - Name: fcn_hr18_512x1024_40k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 77.1 + inference time (ms/im): + - value: 77.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -30,7 +40,12 @@ Models: - Name: fcn_hr48_512x1024_40k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 155.76 + inference time (ms/im): + - value: 155.76 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -44,7 +59,12 @@ Models: - Name: fcn_hr18s_512x1024_80k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 42.12 + inference time (ms/im): + - value: 42.12 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -58,7 +78,12 @@ Models: - Name: fcn_hr18_512x1024_80k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 77.1 + inference time (ms/im): + - value: 77.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -72,7 +97,12 @@ Models: - Name: fcn_hr48_512x1024_80k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 155.76 + inference time (ms/im): + - value: 155.76 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -86,7 +116,12 @@ Models: - Name: fcn_hr18s_512x1024_160k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 42.12 + inference time (ms/im): + - value: 42.12 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -100,7 +135,12 @@ Models: - Name: fcn_hr18_512x1024_160k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 77.1 + inference time (ms/im): + - value: 77.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -114,7 +154,12 @@ Models: - Name: fcn_hr48_512x1024_160k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 155.76 + inference time (ms/im): + - value: 155.76 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -128,7 +173,12 @@ Models: - Name: fcn_hr18s_512x512_80k_ade20k In Collection: FCN Metadata: - inference time (ms/im): 25.87 + inference time (ms/im): + - value: 25.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -142,7 +192,12 @@ Models: - Name: fcn_hr18_512x512_80k_ade20k In Collection: FCN Metadata: - inference time (ms/im): 44.31 + inference time (ms/im): + - value: 44.31 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -156,7 +211,12 @@ Models: - Name: fcn_hr48_512x512_80k_ade20k In Collection: FCN Metadata: - inference time (ms/im): 47.1 + inference time (ms/im): + - value: 47.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -170,7 +230,12 @@ Models: - Name: fcn_hr18s_512x512_160k_ade20k In Collection: FCN Metadata: - inference time (ms/im): 25.87 + inference time (ms/im): + - value: 25.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -184,7 +249,12 @@ Models: - Name: fcn_hr18_512x512_160k_ade20k In Collection: FCN Metadata: - inference time (ms/im): 44.31 + inference time (ms/im): + - value: 44.31 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -198,7 +268,12 @@ Models: - Name: fcn_hr48_512x512_160k_ade20k In Collection: FCN Metadata: - inference time (ms/im): 47.1 + inference time (ms/im): + - value: 47.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -212,7 +287,12 @@ Models: - Name: fcn_hr18s_512x512_20k_voc12aug In Collection: FCN Metadata: - inference time (ms/im): 23.06 + inference time (ms/im): + - value: 23.06 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -226,7 +306,12 @@ Models: - Name: fcn_hr18_512x512_20k_voc12aug In Collection: FCN Metadata: - inference time (ms/im): 42.59 + inference time (ms/im): + - value: 42.59 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -240,7 +325,12 @@ Models: - Name: fcn_hr48_512x512_20k_voc12aug In Collection: FCN Metadata: - inference time (ms/im): 45.35 + inference time (ms/im): + - value: 45.35 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -254,7 +344,12 @@ Models: - Name: fcn_hr18s_512x512_40k_voc12aug In Collection: FCN Metadata: - inference time (ms/im): 23.06 + inference time (ms/im): + - value: 23.06 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -268,7 +363,12 @@ Models: - Name: fcn_hr18_512x512_40k_voc12aug In Collection: FCN Metadata: - inference time (ms/im): 42.59 + inference time (ms/im): + - value: 42.59 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -282,7 +382,12 @@ Models: - Name: fcn_hr48_512x512_40k_voc12aug In Collection: FCN Metadata: - inference time (ms/im): 45.35 + inference time (ms/im): + - value: 45.35 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -296,7 +401,12 @@ Models: - Name: fcn_hr48_480x480_40k_pascal_context In Collection: FCN Metadata: - inference time (ms/im): 112.87 + inference time (ms/im): + - value: 112.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context @@ -310,7 +420,12 @@ Models: - Name: fcn_hr48_480x480_80k_pascal_context In Collection: FCN Metadata: - inference time (ms/im): 112.87 + inference time (ms/im): + - value: 112.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context @@ -324,7 +439,12 @@ Models: - Name: fcn_hr48_480x480_40k_pascal_context_59 In Collection: FCN Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context @@ -338,7 +458,12 @@ Models: - Name: fcn_hr48_480x480_80k_pascal_context In Collection: FCN Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context diff --git a/configs/mobilenet_v2/metafile.yml b/configs/mobilenet_v2/metafile.yml index 1379bbae9c..627a88d5f3 100644 --- a/configs/mobilenet_v2/metafile.yml +++ b/configs/mobilenet_v2/metafile.yml @@ -4,7 +4,12 @@ Models: - Name: fcn_m-v2-d8_512x1024_80k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 70.42 + inference time (ms/im): + - value: 70.42 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -18,7 +23,12 @@ Models: - Name: pspnet_m-v2-d8_512x1024_80k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 89.29 + inference time (ms/im): + - value: 89.29 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -32,7 +42,12 @@ Models: - Name: deeplabv3_m-v2-d8_512x1024_80k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 119.05 + inference time (ms/im): + - value: 119.05 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -46,7 +61,12 @@ Models: - Name: deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 119.05 + inference time (ms/im): + - value: 119.05 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -60,7 +80,12 @@ Models: - Name: fcn_m-v2-d8_512x512_160k_ade20k In Collection: FCN Metadata: - inference time (ms/im): 15.53 + inference time (ms/im): + - value: 15.53 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -74,7 +99,12 @@ Models: - Name: pspnet_m-v2-d8_512x512_160k_ade20k In Collection: PSPNet Metadata: - inference time (ms/im): 17.33 + inference time (ms/im): + - value: 17.33 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -88,7 +118,12 @@ Models: - Name: deeplabv3_m-v2-d8_512x512_160k_ade20k In Collection: DeepLabV3 Metadata: - inference time (ms/im): 25.06 + inference time (ms/im): + - value: 25.06 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -102,7 +137,12 @@ Models: - Name: deeplabv3plus_m-v2-d8_512x512_160k_ade20k In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 23.2 + inference time (ms/im): + - value: 23.2 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K diff --git a/configs/mobilenet_v3/metafile.yml b/configs/mobilenet_v3/metafile.yml index a7134c712e..22da770e92 100644 --- a/configs/mobilenet_v3/metafile.yml +++ b/configs/mobilenet_v3/metafile.yml @@ -9,7 +9,12 @@ Models: - Name: lraspp_m-v3-d8_512x1024_320k_cityscapes In Collection: LRASPP Metadata: - inference time (ms/im): 65.7 + inference time (ms/im): + - value: 65.7 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -23,7 +28,12 @@ Models: - Name: lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes In Collection: LRASPP Metadata: - inference time (ms/im): 67.7 + inference time (ms/im): + - value: 67.7 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -37,7 +47,12 @@ Models: - Name: lraspp_m-v3s-d8_512x1024_320k_cityscapes In Collection: LRASPP Metadata: - inference time (ms/im): 42.3 + inference time (ms/im): + - value: 42.3 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -51,7 +66,12 @@ Models: - Name: lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes In Collection: LRASPP Metadata: - inference time (ms/im): 40.82 + inference time (ms/im): + - value: 40.82 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes diff --git a/configs/nonlocal_net/metafile.yml b/configs/nonlocal_net/metafile.yml index c78fc30594..aae1b54643 100644 --- a/configs/nonlocal_net/metafile.yml +++ b/configs/nonlocal_net/metafile.yml @@ -11,7 +11,12 @@ Models: - Name: nonlocal_r50-d8_512x1024_40k_cityscapes In Collection: NonLocal Metadata: - inference time (ms/im): 367.65 + inference time (ms/im): + - value: 367.65 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -25,7 +30,12 @@ Models: - Name: nonlocal_r101-d8_512x1024_40k_cityscapes In Collection: NonLocal Metadata: - inference time (ms/im): 512.82 + inference time (ms/im): + - value: 512.82 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -39,7 +49,12 @@ Models: - Name: nonlocal_r50-d8_769x769_40k_cityscapes In Collection: NonLocal Metadata: - inference time (ms/im): 657.89 + inference time (ms/im): + - value: 657.89 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -53,7 +68,12 @@ Models: - Name: nonlocal_r101-d8_769x769_40k_cityscapes In Collection: NonLocal Metadata: - inference time (ms/im): 952.38 + inference time (ms/im): + - value: 952.38 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -67,7 +87,12 @@ Models: - Name: nonlocal_r50-d8_512x1024_80k_cityscapes In Collection: NonLocal Metadata: - inference time (ms/im): 367.65 + inference time (ms/im): + - value: 367.65 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -81,7 +106,12 @@ Models: - Name: nonlocal_r101-d8_512x1024_80k_cityscapes In Collection: NonLocal Metadata: - inference time (ms/im): 512.82 + inference time (ms/im): + - value: 512.82 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -95,7 +125,12 @@ Models: - Name: nonlocal_r50-d8_769x769_80k_cityscapes In Collection: NonLocal Metadata: - inference time (ms/im): 657.89 + inference time (ms/im): + - value: 657.89 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -109,7 +144,12 @@ Models: - Name: nonlocal_r101-d8_769x769_80k_cityscapes In Collection: NonLocal Metadata: - inference time (ms/im): 952.38 + inference time (ms/im): + - value: 952.38 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -123,7 +163,12 @@ Models: - Name: nonlocal_r50-d8_512x512_80k_ade20k In Collection: NonLocal Metadata: - inference time (ms/im): 46.79 + inference time (ms/im): + - value: 46.79 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -137,7 +182,12 @@ Models: - Name: nonlocal_r101-d8_512x512_80k_ade20k In Collection: NonLocal Metadata: - inference time (ms/im): 71.58 + inference time (ms/im): + - value: 71.58 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -151,7 +201,12 @@ Models: - Name: nonlocal_r50-d8_512x512_160k_ade20k In Collection: NonLocal Metadata: - inference time (ms/im): 46.79 + inference time (ms/im): + - value: 46.79 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -165,7 +220,12 @@ Models: - Name: nonlocal_r101-d8_512x512_160k_ade20k In Collection: NonLocal Metadata: - inference time (ms/im): 71.58 + inference time (ms/im): + - value: 71.58 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -179,7 +239,12 @@ Models: - Name: nonlocal_r50-d8_512x512_20k_voc12aug In Collection: NonLocal Metadata: - inference time (ms/im): 47.15 + inference time (ms/im): + - value: 47.15 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -193,7 +258,12 @@ Models: - Name: nonlocal_r101-d8_512x512_20k_voc12aug In Collection: NonLocal Metadata: - inference time (ms/im): 71.38 + inference time (ms/im): + - value: 71.38 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -207,7 +277,12 @@ Models: - Name: nonlocal_r50-d8_512x512_40k_voc12aug In Collection: NonLocal Metadata: - inference time (ms/im): 47.15 + inference time (ms/im): + - value: 47.15 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -221,7 +296,12 @@ Models: - Name: nonlocal_r101-d8_512x512_40k_voc12aug In Collection: NonLocal Metadata: - inference time (ms/im): 71.38 + inference time (ms/im): + - value: 71.38 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug diff --git a/configs/ocrnet/metafile.yml b/configs/ocrnet/metafile.yml index 1ba52dee18..b3383776f9 100644 --- a/configs/ocrnet/metafile.yml +++ b/configs/ocrnet/metafile.yml @@ -11,7 +11,12 @@ Models: - Name: ocrnet_hr18s_512x1024_40k_cityscapes In Collection: OCRNet Metadata: - inference time (ms/im): 95.69 + inference time (ms/im): + - value: 95.69 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -25,7 +30,12 @@ Models: - Name: ocrnet_hr18_512x1024_40k_cityscapes In Collection: OCRNet Metadata: - inference time (ms/im): 133.33 + inference time (ms/im): + - value: 133.33 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -39,7 +49,12 @@ Models: - Name: ocrnet_hr48_512x1024_40k_cityscapes In Collection: OCRNet Metadata: - inference time (ms/im): 236.97 + inference time (ms/im): + - value: 236.97 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -53,7 +68,12 @@ Models: - Name: ocrnet_hr18s_512x1024_80k_cityscapes In Collection: OCRNet Metadata: - inference time (ms/im): 95.69 + inference time (ms/im): + - value: 95.69 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -67,7 +87,12 @@ Models: - Name: ocrnet_hr18_512x1024_80k_cityscapes In Collection: OCRNet Metadata: - inference time (ms/im): 133.33 + inference time (ms/im): + - value: 133.33 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -81,7 +106,12 @@ Models: - Name: ocrnet_hr48_512x1024_80k_cityscapes In Collection: OCRNet Metadata: - inference time (ms/im): 236.97 + inference time (ms/im): + - value: 236.97 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -95,7 +125,12 @@ Models: - Name: ocrnet_hr18s_512x1024_160k_cityscapes In Collection: OCRNet Metadata: - inference time (ms/im): 95.69 + inference time (ms/im): + - value: 95.69 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -109,7 +144,12 @@ Models: - Name: ocrnet_hr18_512x1024_160k_cityscapes In Collection: OCRNet Metadata: - inference time (ms/im): 133.33 + inference time (ms/im): + - value: 133.33 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -123,7 +163,12 @@ Models: - Name: ocrnet_hr48_512x1024_160k_cityscapes In Collection: OCRNet Metadata: - inference time (ms/im): 236.97 + inference time (ms/im): + - value: 236.97 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -137,7 +182,12 @@ Models: - Name: ocrnet_r101-d8_512x1024_40k_b8_cityscapes In Collection: OCRNet Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -151,7 +201,12 @@ Models: - Name: ocrnet_r101-d8_512x1024_40k_b16_cityscapes In Collection: OCRNet Metadata: - inference time (ms/im): 113.64 + inference time (ms/im): + - value: 113.64 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -165,7 +220,12 @@ Models: - Name: ocrnet_r101-d8_512x1024_80k_b16_cityscapes In Collection: OCRNet Metadata: - inference time (ms/im): 113.64 + inference time (ms/im): + - value: 113.64 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -179,7 +239,12 @@ Models: - Name: ocrnet_hr18s_512x512_80k_ade20k In Collection: OCRNet Metadata: - inference time (ms/im): 34.51 + inference time (ms/im): + - value: 34.51 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -193,7 +258,12 @@ Models: - Name: ocrnet_hr18_512x512_80k_ade20k In Collection: OCRNet Metadata: - inference time (ms/im): 52.83 + inference time (ms/im): + - value: 52.83 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -207,7 +277,12 @@ Models: - Name: ocrnet_hr48_512x512_80k_ade20k In Collection: OCRNet Metadata: - inference time (ms/im): 58.86 + inference time (ms/im): + - value: 58.86 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -221,7 +296,12 @@ Models: - Name: ocrnet_hr18s_512x512_160k_ade20k In Collection: OCRNet Metadata: - inference time (ms/im): 34.51 + inference time (ms/im): + - value: 34.51 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -235,7 +315,12 @@ Models: - Name: ocrnet_hr18_512x512_160k_ade20k In Collection: OCRNet Metadata: - inference time (ms/im): 52.83 + inference time (ms/im): + - value: 52.83 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -249,7 +334,12 @@ Models: - Name: ocrnet_hr48_512x512_160k_ade20k In Collection: OCRNet Metadata: - inference time (ms/im): 58.86 + inference time (ms/im): + - value: 58.86 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -263,7 +353,12 @@ Models: - Name: ocrnet_hr18s_512x512_20k_voc12aug In Collection: OCRNet Metadata: - inference time (ms/im): 31.7 + inference time (ms/im): + - value: 31.7 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -277,7 +372,12 @@ Models: - Name: ocrnet_hr18_512x512_20k_voc12aug In Collection: OCRNet Metadata: - inference time (ms/im): 50.23 + inference time (ms/im): + - value: 50.23 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -291,7 +391,12 @@ Models: - Name: ocrnet_hr48_512x512_20k_voc12aug In Collection: OCRNet Metadata: - inference time (ms/im): 56.09 + inference time (ms/im): + - value: 56.09 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -305,7 +410,12 @@ Models: - Name: ocrnet_hr18s_512x512_40k_voc12aug In Collection: OCRNet Metadata: - inference time (ms/im): 31.7 + inference time (ms/im): + - value: 31.7 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -319,7 +429,12 @@ Models: - Name: ocrnet_hr18_512x512_40k_voc12aug In Collection: OCRNet Metadata: - inference time (ms/im): 50.23 + inference time (ms/im): + - value: 50.23 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -333,7 +448,12 @@ Models: - Name: ocrnet_hr48_512x512_40k_voc12aug In Collection: OCRNet Metadata: - inference time (ms/im): 56.09 + inference time (ms/im): + - value: 56.09 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug diff --git a/configs/point_rend/metafile.yml b/configs/point_rend/metafile.yml index 6a92fd489d..72682fa081 100644 --- a/configs/point_rend/metafile.yml +++ b/configs/point_rend/metafile.yml @@ -10,7 +10,12 @@ Models: - Name: pointrend_r50_512x1024_80k_cityscapes In Collection: PointRend Metadata: - inference time (ms/im): 117.92 + inference time (ms/im): + - value: 117.92 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -24,7 +29,12 @@ Models: - Name: pointrend_r101_512x1024_80k_cityscapes In Collection: PointRend Metadata: - inference time (ms/im): 142.86 + inference time (ms/im): + - value: 142.86 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -38,7 +48,12 @@ Models: - Name: pointrend_r50_512x512_160k_ade20k In Collection: PointRend Metadata: - inference time (ms/im): 57.77 + inference time (ms/im): + - value: 57.77 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -52,7 +67,12 @@ Models: - Name: pointrend_r101_512x512_160k_ade20k In Collection: PointRend Metadata: - inference time (ms/im): 64.52 + inference time (ms/im): + - value: 64.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K diff --git a/configs/psanet/metafile.yml b/configs/psanet/metafile.yml index 801fcb4e6e..2372494554 100644 --- a/configs/psanet/metafile.yml +++ b/configs/psanet/metafile.yml @@ -11,7 +11,12 @@ Models: - Name: psanet_r50-d8_512x1024_40k_cityscapes In Collection: PSANet Metadata: - inference time (ms/im): 315.46 + inference time (ms/im): + - value: 315.46 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -25,7 +30,12 @@ Models: - Name: psanet_r101-d8_512x1024_40k_cityscapes In Collection: PSANet Metadata: - inference time (ms/im): 454.55 + inference time (ms/im): + - value: 454.55 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -39,7 +49,12 @@ Models: - Name: psanet_r50-d8_769x769_40k_cityscapes In Collection: PSANet Metadata: - inference time (ms/im): 714.29 + inference time (ms/im): + - value: 714.29 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -53,7 +68,12 @@ Models: - Name: psanet_r101-d8_769x769_40k_cityscapes In Collection: PSANet Metadata: - inference time (ms/im): 1020.41 + inference time (ms/im): + - value: 1020.41 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -67,7 +87,12 @@ Models: - Name: psanet_r50-d8_512x1024_80k_cityscapes In Collection: PSANet Metadata: - inference time (ms/im): 315.46 + inference time (ms/im): + - value: 315.46 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -81,7 +106,12 @@ Models: - Name: psanet_r101-d8_512x1024_80k_cityscapes In Collection: PSANet Metadata: - inference time (ms/im): 454.55 + inference time (ms/im): + - value: 454.55 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -95,7 +125,12 @@ Models: - Name: psanet_r50-d8_769x769_80k_cityscapes In Collection: PSANet Metadata: - inference time (ms/im): 714.29 + inference time (ms/im): + - value: 714.29 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -109,7 +144,12 @@ Models: - Name: psanet_r101-d8_769x769_80k_cityscapes In Collection: PSANet Metadata: - inference time (ms/im): 1020.41 + inference time (ms/im): + - value: 1020.41 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -123,7 +163,12 @@ Models: - Name: psanet_r50-d8_512x512_80k_ade20k In Collection: PSANet Metadata: - inference time (ms/im): 52.88 + inference time (ms/im): + - value: 52.88 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -137,7 +182,12 @@ Models: - Name: psanet_r101-d8_512x512_80k_ade20k In Collection: PSANet Metadata: - inference time (ms/im): 76.16 + inference time (ms/im): + - value: 76.16 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -151,7 +201,12 @@ Models: - Name: psanet_r50-d8_512x512_160k_ade20k In Collection: PSANet Metadata: - inference time (ms/im): 52.88 + inference time (ms/im): + - value: 52.88 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -165,7 +220,12 @@ Models: - Name: psanet_r101-d8_512x512_160k_ade20k In Collection: PSANet Metadata: - inference time (ms/im): 76.16 + inference time (ms/im): + - value: 76.16 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -179,7 +239,12 @@ Models: - Name: psanet_r50-d8_512x512_20k_voc12aug In Collection: PSANet Metadata: - inference time (ms/im): 54.82 + inference time (ms/im): + - value: 54.82 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -193,7 +258,12 @@ Models: - Name: psanet_r101-d8_512x512_20k_voc12aug In Collection: PSANet Metadata: - inference time (ms/im): 79.18 + inference time (ms/im): + - value: 79.18 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -207,7 +277,12 @@ Models: - Name: psanet_r50-d8_512x512_40k_voc12aug In Collection: PSANet Metadata: - inference time (ms/im): 54.82 + inference time (ms/im): + - value: 54.82 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -221,7 +296,12 @@ Models: - Name: psanet_r101-d8_512x512_40k_voc12aug In Collection: PSANet Metadata: - inference time (ms/im): 79.18 + inference time (ms/im): + - value: 79.18 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug diff --git a/configs/pspnet/metafile.yml b/configs/pspnet/metafile.yml index d5db5b713a..992708a2eb 100644 --- a/configs/pspnet/metafile.yml +++ b/configs/pspnet/metafile.yml @@ -12,7 +12,12 @@ Models: - Name: pspnet_r50-d8_512x1024_40k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 245.7 + inference time (ms/im): + - value: 245.7 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -26,7 +31,12 @@ Models: - Name: pspnet_r101-d8_512x1024_40k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 373.13 + inference time (ms/im): + - value: 373.13 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -40,7 +50,12 @@ Models: - Name: pspnet_r50-d8_769x769_40k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 568.18 + inference time (ms/im): + - value: 568.18 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -54,7 +69,12 @@ Models: - Name: pspnet_r101-d8_769x769_40k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 869.57 + inference time (ms/im): + - value: 869.57 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -68,7 +88,12 @@ Models: - Name: pspnet_r18-d8_512x1024_80k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 63.65 + inference time (ms/im): + - value: 63.65 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -82,7 +107,12 @@ Models: - Name: pspnet_r50-d8_512x1024_80k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 245.7 + inference time (ms/im): + - value: 245.7 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -96,7 +126,12 @@ Models: - Name: pspnet_r101-d8_512x1024_80k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 373.13 + inference time (ms/im): + - value: 373.13 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -110,7 +145,12 @@ Models: - Name: pspnet_r18-d8_769x769_80k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 161.29 + inference time (ms/im): + - value: 161.29 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -124,7 +164,12 @@ Models: - Name: pspnet_r50-d8_769x769_80k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 568.18 + inference time (ms/im): + - value: 568.18 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -138,7 +183,12 @@ Models: - Name: pspnet_r101-d8_769x769_80k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 869.57 + inference time (ms/im): + - value: 869.57 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -152,7 +202,12 @@ Models: - Name: pspnet_r18b-d8_512x1024_80k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 61.43 + inference time (ms/im): + - value: 61.43 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -166,7 +221,12 @@ Models: - Name: pspnet_r50b-d8_512x1024_80k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 232.56 + inference time (ms/im): + - value: 232.56 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -180,7 +240,12 @@ Models: - Name: pspnet_r101b-d8_512x1024_80k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 362.32 + inference time (ms/im): + - value: 362.32 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -194,7 +259,12 @@ Models: - Name: pspnet_r18b-d8_769x769_80k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 156.01 + inference time (ms/im): + - value: 156.01 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -208,7 +278,12 @@ Models: - Name: pspnet_r50b-d8_769x769_80k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 531.91 + inference time (ms/im): + - value: 531.91 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -222,7 +297,12 @@ Models: - Name: pspnet_r101b-d8_769x769_80k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 854.7 + inference time (ms/im): + - value: 854.7 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -236,7 +316,12 @@ Models: - Name: pspnet_r50-d8_512x512_80k_ade20k In Collection: PSPNet Metadata: - inference time (ms/im): 42.5 + inference time (ms/im): + - value: 42.5 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -250,7 +335,12 @@ Models: - Name: pspnet_r101-d8_512x512_80k_ade20k In Collection: PSPNet Metadata: - inference time (ms/im): 65.36 + inference time (ms/im): + - value: 65.36 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -264,7 +354,12 @@ Models: - Name: pspnet_r50-d8_512x512_160k_ade20k In Collection: PSPNet Metadata: - inference time (ms/im): 42.5 + inference time (ms/im): + - value: 42.5 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -278,7 +373,12 @@ Models: - Name: pspnet_r101-d8_512x512_160k_ade20k In Collection: PSPNet Metadata: - inference time (ms/im): 65.36 + inference time (ms/im): + - value: 65.36 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -292,7 +392,12 @@ Models: - Name: pspnet_r50-d8_512x512_20k_voc12aug In Collection: PSPNet Metadata: - inference time (ms/im): 42.39 + inference time (ms/im): + - value: 42.39 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -306,7 +411,12 @@ Models: - Name: pspnet_r101-d8_512x512_20k_voc12aug In Collection: PSPNet Metadata: - inference time (ms/im): 66.58 + inference time (ms/im): + - value: 66.58 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -320,7 +430,12 @@ Models: - Name: pspnet_r50-d8_512x512_40k_voc12aug In Collection: PSPNet Metadata: - inference time (ms/im): 42.39 + inference time (ms/im): + - value: 42.39 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -334,7 +449,12 @@ Models: - Name: pspnet_r101-d8_512x512_40k_voc12aug In Collection: PSPNet Metadata: - inference time (ms/im): 66.58 + inference time (ms/im): + - value: 66.58 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -348,7 +468,12 @@ Models: - Name: pspnet_r101-d8_480x480_40k_pascal_context In Collection: PSPNet Metadata: - inference time (ms/im): 103.31 + inference time (ms/im): + - value: 103.31 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context @@ -362,7 +487,12 @@ Models: - Name: pspnet_r101-d8_480x480_80k_pascal_context In Collection: PSPNet Metadata: - inference time (ms/im): 103.31 + inference time (ms/im): + - value: 103.31 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context @@ -376,7 +506,12 @@ Models: - Name: pspnet_r101-d8_480x480_40k_pascal_context In Collection: PSPNet Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context @@ -390,7 +525,12 @@ Models: - Name: pspnet_r101-d8_480x480_80k_pascal_context_59 In Collection: PSPNet Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context diff --git a/configs/resnest/metafile.yml b/configs/resnest/metafile.yml index 598d61fb50..a778a85757 100644 --- a/configs/resnest/metafile.yml +++ b/configs/resnest/metafile.yml @@ -10,7 +10,12 @@ Models: - Name: fcn_s101-d8_512x1024_80k_cityscapes In Collection: FCN Metadata: - inference time (ms/im): 418.41 + inference time (ms/im): + - value: 418.41 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -24,7 +29,12 @@ Models: - Name: pspnet_s101-d8_512x1024_80k_cityscapes In Collection: PSPNet Metadata: - inference time (ms/im): 396.83 + inference time (ms/im): + - value: 396.83 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -38,7 +48,12 @@ Models: - Name: deeplabv3_s101-d8_512x1024_80k_cityscapes In Collection: DeepLabV3 Metadata: - inference time (ms/im): 531.91 + inference time (ms/im): + - value: 531.91 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -52,7 +67,12 @@ Models: - Name: deeplabv3plus_s101-d8_512x1024_80k_cityscapes In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 423.73 + inference time (ms/im): + - value: 423.73 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -66,7 +86,12 @@ Models: - Name: fcn_s101-d8_512x512_160k_ade20k In Collection: FCN Metadata: - inference time (ms/im): 77.76 + inference time (ms/im): + - value: 77.76 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -80,7 +105,12 @@ Models: - Name: pspnet_s101-d8_512x512_160k_ade20k In Collection: PSPNet Metadata: - inference time (ms/im): 76.8 + inference time (ms/im): + - value: 76.8 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -94,7 +124,12 @@ Models: - Name: deeplabv3_s101-d8_512x512_160k_ade20k In Collection: DeepLabV3 Metadata: - inference time (ms/im): 107.76 + inference time (ms/im): + - value: 107.76 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -108,7 +143,12 @@ Models: - Name: deeplabv3plus_s101-d8_512x512_160k_ade20k In Collection: DeepLabV3+ Metadata: - inference time (ms/im): 83.61 + inference time (ms/im): + - value: 83.61 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K diff --git a/configs/sem_fpn/metafile.yml b/configs/sem_fpn/metafile.yml index b6a019d582..52cd379797 100644 --- a/configs/sem_fpn/metafile.yml +++ b/configs/sem_fpn/metafile.yml @@ -11,7 +11,12 @@ Models: - Name: fpn_r50_512x1024_80k_cityscapes In Collection: FPN Metadata: - inference time (ms/im): 73.86 + inference time (ms/im): + - value: 73.86 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -25,7 +30,12 @@ Models: - Name: fpn_r101_512x1024_80k_cityscapes In Collection: FPN Metadata: - inference time (ms/im): 97.18 + inference time (ms/im): + - value: 97.18 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -39,7 +49,12 @@ Models: - Name: fpn_r50_512x512_160k_ade20k In Collection: FPN Metadata: - inference time (ms/im): 17.93 + inference time (ms/im): + - value: 17.93 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -53,7 +68,12 @@ Models: - Name: fpn_r101_512x512_160k_ade20k In Collection: FPN Metadata: - inference time (ms/im): 24.64 + inference time (ms/im): + - value: 24.64 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K diff --git a/configs/unet/metafile.yml b/configs/unet/metafile.yml index 8c9bfc3e83..7e22509656 100644 --- a/configs/unet/metafile.yml +++ b/configs/unet/metafile.yml @@ -3,7 +3,12 @@ Models: - Name: fcn_unet_s5-d16_64x64_40k_drive In Collection: FCN Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: DRIVE @@ -17,7 +22,12 @@ Models: - Name: pspnet_unet_s5-d16_64x64_40k_drive In Collection: PSPNet Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: DRIVE @@ -31,7 +41,12 @@ Models: - Name: deeplabv3_unet_s5-d16_64x64_40k_drive In Collection: DeepLabV3 Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: DRIVE @@ -45,7 +60,12 @@ Models: - Name: fcn_unet_s5-d16_128x128_40k_stare In Collection: FCN Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: STARE @@ -59,7 +79,12 @@ Models: - Name: pspnet_unet_s5-d16_128x128_40k_stare In Collection: PSPNet Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: STARE @@ -73,7 +98,12 @@ Models: - Name: deeplabv3_unet_s5-d16_128x128_40k_stare In Collection: DeepLabV3 Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: STARE @@ -87,7 +117,12 @@ Models: - Name: fcn_unet_s5-d16_128x128_40k_chase_db1 In Collection: FCN Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: CHASE_DB1 @@ -101,7 +136,12 @@ Models: - Name: pspnet_unet_s5-d16_128x128_40k_chase_db1 In Collection: PSPNet Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: CHASE_DB1 @@ -115,7 +155,12 @@ Models: - Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1 In Collection: DeepLabV3 Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: CHASE_DB1 @@ -129,7 +174,12 @@ Models: - Name: fcn_unet_s5-d16_256x256_40k_hrf In Collection: FCN Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: HRF @@ -143,7 +193,12 @@ Models: - Name: pspnet_unet_s5-d16_256x256_40k_hrf In Collection: PSPNet Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: HRF @@ -157,7 +212,12 @@ Models: - Name: deeplabv3_unet_s5-d16_256x256_40k_hrf In Collection: DeepLabV3 Metadata: - inference time (ms/im): None + inference time (ms/im): + - value: None + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: HRF diff --git a/configs/upernet/metafile.yml b/configs/upernet/metafile.yml index 3bf226af8c..53361b6290 100644 --- a/configs/upernet/metafile.yml +++ b/configs/upernet/metafile.yml @@ -11,7 +11,12 @@ Models: - Name: upernet_r50_512x1024_40k_cityscapes In Collection: UPerNet Metadata: - inference time (ms/im): 235.29 + inference time (ms/im): + - value: 235.29 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -25,7 +30,12 @@ Models: - Name: upernet_r101_512x1024_40k_cityscapes In Collection: UPerNet Metadata: - inference time (ms/im): 263.85 + inference time (ms/im): + - value: 263.85 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -39,7 +49,12 @@ Models: - Name: upernet_r50_769x769_40k_cityscapes In Collection: UPerNet Metadata: - inference time (ms/im): 568.18 + inference time (ms/im): + - value: 568.18 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -53,7 +68,12 @@ Models: - Name: upernet_r101_769x769_40k_cityscapes In Collection: UPerNet Metadata: - inference time (ms/im): 641.03 + inference time (ms/im): + - value: 641.03 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -67,7 +87,12 @@ Models: - Name: upernet_r50_512x1024_80k_cityscapes In Collection: UPerNet Metadata: - inference time (ms/im): 235.29 + inference time (ms/im): + - value: 235.29 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -81,7 +106,12 @@ Models: - Name: upernet_r101_512x1024_80k_cityscapes In Collection: UPerNet Metadata: - inference time (ms/im): 263.85 + inference time (ms/im): + - value: 263.85 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -95,7 +125,12 @@ Models: - Name: upernet_r50_769x769_80k_cityscapes In Collection: UPerNet Metadata: - inference time (ms/im): 568.18 + inference time (ms/im): + - value: 568.18 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -109,7 +144,12 @@ Models: - Name: upernet_r101_769x769_80k_cityscapes In Collection: UPerNet Metadata: - inference time (ms/im): 641.03 + inference time (ms/im): + - value: 641.03 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes @@ -123,7 +163,12 @@ Models: - Name: upernet_r50_512x512_80k_ade20k In Collection: UPerNet Metadata: - inference time (ms/im): 42.74 + inference time (ms/im): + - value: 42.74 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -137,7 +182,12 @@ Models: - Name: upernet_r101_512x512_80k_ade20k In Collection: UPerNet Metadata: - inference time (ms/im): 49.16 + inference time (ms/im): + - value: 49.16 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -151,7 +201,12 @@ Models: - Name: upernet_r50_512x512_160k_ade20k In Collection: UPerNet Metadata: - inference time (ms/im): 42.74 + inference time (ms/im): + - value: 42.74 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -165,7 +220,12 @@ Models: - Name: upernet_r101_512x512_160k_ade20k In Collection: UPerNet Metadata: - inference time (ms/im): 49.16 + inference time (ms/im): + - value: 49.16 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K @@ -179,7 +239,12 @@ Models: - Name: upernet_r50_512x512_20k_voc12aug In Collection: UPerNet Metadata: - inference time (ms/im): 43.16 + inference time (ms/im): + - value: 43.16 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -193,7 +258,12 @@ Models: - Name: upernet_r101_512x512_20k_voc12aug In Collection: UPerNet Metadata: - inference time (ms/im): 50.05 + inference time (ms/im): + - value: 50.05 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -207,7 +277,12 @@ Models: - Name: upernet_r50_512x512_40k_voc12aug In Collection: UPerNet Metadata: - inference time (ms/im): 43.16 + inference time (ms/im): + - value: 43.16 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug @@ -221,7 +296,12 @@ Models: - Name: upernet_r101_512x512_40k_voc12aug In Collection: UPerNet Metadata: - inference time (ms/im): 50.05 + inference time (ms/im): + - value: 50.05 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug diff --git a/model_zoo.yml b/model-index.yml similarity index 100% rename from model_zoo.yml rename to model-index.yml