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Junjun2016Junjun2016
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update metafiles (#661)
* update metafiles * update metafiles
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28 files changed

+2304
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‎configs/ann/metafile.yml

+96-16
Original file line numberDiff line numberDiff line change
@@ -11,7 +11,12 @@ Models:
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- Name: ann_r50-d8_512x1024_40k_cityscapes
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In Collection: ANN
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Metadata:
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inference time (ms/im): 269.54
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inference time (ms/im):
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- value: 269.54
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
@@ -25,7 +30,12 @@ Models:
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- Name: ann_r101-d8_512x1024_40k_cityscapes
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In Collection: ANN
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Metadata:
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inference time (ms/im): 392.16
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inference time (ms/im):
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- value: 392.16
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
@@ -39,7 +49,12 @@ Models:
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- Name: ann_r50-d8_769x769_40k_cityscapes
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In Collection: ANN
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Metadata:
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inference time (ms/im): 588.24
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inference time (ms/im):
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- value: 588.24
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
@@ -53,7 +68,12 @@ Models:
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- Name: ann_r101-d8_769x769_40k_cityscapes
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In Collection: ANN
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Metadata:
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inference time (ms/im): 869.57
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inference time (ms/im):
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- value: 869.57
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
@@ -67,7 +87,12 @@ Models:
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- Name: ann_r50-d8_512x1024_80k_cityscapes
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In Collection: ANN
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Metadata:
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inference time (ms/im): 269.54
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inference time (ms/im):
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- value: 269.54
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
@@ -81,7 +106,12 @@ Models:
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- Name: ann_r101-d8_512x1024_80k_cityscapes
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In Collection: ANN
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Metadata:
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inference time (ms/im): 392.16
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inference time (ms/im):
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- value: 392.16
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
@@ -95,7 +125,12 @@ Models:
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- Name: ann_r50-d8_769x769_80k_cityscapes
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In Collection: ANN
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Metadata:
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inference time (ms/im): 588.24
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inference time (ms/im):
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- value: 588.24
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
@@ -109,7 +144,12 @@ Models:
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- Name: ann_r101-d8_769x769_80k_cityscapes
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In Collection: ANN
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Metadata:
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inference time (ms/im): 869.57
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inference time (ms/im):
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- value: 869.57
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
@@ -123,7 +163,12 @@ Models:
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- Name: ann_r50-d8_512x512_80k_ade20k
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In Collection: ANN
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Metadata:
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inference time (ms/im): 47.6
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inference time (ms/im):
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- value: 47.6
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
@@ -137,7 +182,12 @@ Models:
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- Name: ann_r101-d8_512x512_80k_ade20k
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In Collection: ANN
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Metadata:
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inference time (ms/im): 70.82
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inference time (ms/im):
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- value: 70.82
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
@@ -151,7 +201,12 @@ Models:
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- Name: ann_r50-d8_512x512_160k_ade20k
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In Collection: ANN
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Metadata:
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inference time (ms/im): 47.6
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inference time (ms/im):
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- value: 47.6
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
@@ -165,7 +220,12 @@ Models:
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- Name: ann_r101-d8_512x512_160k_ade20k
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In Collection: ANN
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Metadata:
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inference time (ms/im): 70.82
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inference time (ms/im):
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- value: 70.82
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
@@ -179,7 +239,12 @@ Models:
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- Name: ann_r50-d8_512x512_20k_voc12aug
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In Collection: ANN
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Metadata:
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inference time (ms/im): 47.8
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inference time (ms/im):
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- value: 47.8
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal VOC 2012 + Aug
@@ -193,7 +258,12 @@ Models:
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- Name: ann_r101-d8_512x512_20k_voc12aug
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In Collection: ANN
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Metadata:
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inference time (ms/im): 71.74
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inference time (ms/im):
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- value: 71.74
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal VOC 2012 + Aug
@@ -207,7 +277,12 @@ Models:
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- Name: ann_r50-d8_512x512_40k_voc12aug
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In Collection: ANN
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Metadata:
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inference time (ms/im): 47.8
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inference time (ms/im):
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- value: 47.8
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal VOC 2012 + Aug
@@ -221,7 +296,12 @@ Models:
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- Name: ann_r101-d8_512x512_40k_voc12aug
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In Collection: ANN
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Metadata:
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inference time (ms/im): 71.74
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inference time (ms/im):
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- value: 71.74
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal VOC 2012 + Aug

‎configs/apcnet/metafile.yml

+72-12
Original file line numberDiff line numberDiff line change
@@ -10,7 +10,12 @@ Models:
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- Name: apcnet_r50-d8_512x1024_40k_cityscapes
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In Collection: APCNet
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Metadata:
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inference time (ms/im): 280.11
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inference time (ms/im):
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- value: 280.11
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
@@ -24,7 +29,12 @@ Models:
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- Name: apcnet_r101-d8_512x1024_40k_cityscapes
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In Collection: APCNet
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Metadata:
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inference time (ms/im): 465.12
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inference time (ms/im):
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- value: 465.12
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
@@ -38,7 +48,12 @@ Models:
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- Name: apcnet_r50-d8_769x769_40k_cityscapes
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In Collection: APCNet
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Metadata:
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inference time (ms/im): 657.89
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inference time (ms/im):
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- value: 657.89
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
@@ -52,7 +67,12 @@ Models:
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- Name: apcnet_r101-d8_769x769_40k_cityscapes
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In Collection: APCNet
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Metadata:
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inference time (ms/im): 970.87
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inference time (ms/im):
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- value: 970.87
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
@@ -66,7 +86,12 @@ Models:
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- Name: apcnet_r50-d8_512x1024_80k_cityscapes
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In Collection: APCNet
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Metadata:
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inference time (ms/im): 280.11
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inference time (ms/im):
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- value: 280.11
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
@@ -80,7 +105,12 @@ Models:
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- Name: apcnet_r101-d8_512x1024_80k_cityscapes
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In Collection: APCNet
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Metadata:
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inference time (ms/im): 465.12
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inference time (ms/im):
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- value: 465.12
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
@@ -94,7 +124,12 @@ Models:
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- Name: apcnet_r50-d8_769x769_80k_cityscapes
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In Collection: APCNet
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Metadata:
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inference time (ms/im): 657.89
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inference time (ms/im):
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- value: 657.89
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
@@ -108,7 +143,12 @@ Models:
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- Name: apcnet_r101-d8_769x769_80k_cityscapes
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In Collection: APCNet
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Metadata:
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inference time (ms/im): 970.87
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inference time (ms/im):
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- value: 970.87
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
@@ -122,7 +162,12 @@ Models:
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- Name: apcnet_r50-d8_512x512_80k_ade20k
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In Collection: APCNet
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Metadata:
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inference time (ms/im): 50.99
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inference time (ms/im):
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- value: 50.99
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
@@ -136,7 +181,12 @@ Models:
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- Name: apcnet_r101-d8_512x512_80k_ade20k
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In Collection: APCNet
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Metadata:
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inference time (ms/im): 76.34
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inference time (ms/im):
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- value: 76.34
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
@@ -150,7 +200,12 @@ Models:
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- Name: apcnet_r50-d8_512x512_160k_ade20k
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In Collection: APCNet
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Metadata:
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inference time (ms/im): 50.99
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inference time (ms/im):
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- value: 50.99
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
@@ -164,7 +219,12 @@ Models:
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- Name: apcnet_r101-d8_512x512_160k_ade20k
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In Collection: APCNet
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Metadata:
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inference time (ms/im): 76.34
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inference time (ms/im):
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- value: 76.34
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K

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