update metafiles (#661)

* update metafiles * update metafiles
open-mmlab · Jul 1, 2021 · 36c8144 · 36c8144
1 parent 170a9d1
commit 36c8144
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Showing 28 changed files with 2,304 additions and 384 deletions.
diff --git 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
@@ -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