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MengzhangLI 2021-11-30 20:54:25 +08:00 committed by GitHub
parent 40b9ebb565
commit 1cf6049758
37 changed files with 313 additions and 313 deletions
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2
.dev/md2yml.py
View File

@ -206,7 +206,7 @@ def parse_md(md_file):
f'({crop_size[0]},{crop_size[1]})'
}]
if mem != -1:
model['Metadata']['memory (GB)'] = float(mem)
model['Metadata']['Training Memory (GB)'] = float(mem)
# Only have semantic segmentation now
if ms_id and els[ms_id] != '-' and els[ms_id] != '':
model['Results'][0]['Metrics'][

16
configs/ann/ann.yml
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@ -28,7 +28,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 6.0
Training Memory (GB): 6.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -50,7 +50,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 9.5
Training Memory (GB): 9.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -72,7 +72,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 6.8
Training Memory (GB): 6.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -94,7 +94,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 10.7
Training Memory (GB): 10.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -172,7 +172,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.1
Training Memory (GB): 9.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -194,7 +194,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 12.5
Training Memory (GB): 12.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -244,7 +244,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.0
Training Memory (GB): 6.0
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -266,7 +266,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.5
Training Memory (GB): 9.5
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug

12
configs/apcnet/apcnet.yml
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@ -27,7 +27,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 7.7
Training Memory (GB): 7.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -49,7 +49,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 11.2
Training Memory (GB): 11.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -71,7 +71,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 8.7
Training Memory (GB): 8.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -93,7 +93,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 12.7
Training Memory (GB): 12.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -171,7 +171,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 10.1
Training Memory (GB): 10.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -193,7 +193,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 13.6
Training Memory (GB): 13.6
Results:
- Task: Semantic Segmentation
Dataset: ADE20K

16
configs/bisenetv1/bisenetv1.yml
View File

@ -27,7 +27,7 @@ Models:
batch size: 1
mode: FP32
resolution: (1024,1024)
memory (GB): 5.69
Training Memory (GB): 5.69
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -49,7 +49,7 @@ Models:
batch size: 1
mode: FP32
resolution: (1024,1024)
memory (GB): 5.69
Training Memory (GB): 5.69
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -71,7 +71,7 @@ Models:
batch size: 1
mode: FP32
resolution: (1024,1024)
memory (GB): 11.17
Training Memory (GB): 11.17
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -93,7 +93,7 @@ Models:
batch size: 1
mode: FP32
resolution: (1024,1024)
memory (GB): 15.39
Training Memory (GB): 15.39
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -115,7 +115,7 @@ Models:
batch size: 1
mode: FP32
resolution: (1024,1024)
memory (GB): 15.39
Training Memory (GB): 15.39
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -151,7 +151,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.33
Training Memory (GB): 6.33
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
@ -187,7 +187,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.28
Training Memory (GB): 9.28
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
@ -223,7 +223,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 10.36
Training Memory (GB): 10.36
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k

8
configs/bisenetv2/bisenetv2.yml
View File

@ -25,7 +25,7 @@ Models:
batch size: 1
mode: FP32
resolution: (1024,1024)
memory (GB): 7.64
Training Memory (GB): 7.64
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -40,7 +40,7 @@ Models:
backbone: BiSeNetV2
crop size: (1024,1024)
lr schd: 160000
memory (GB): 7.64
Training Memory (GB): 7.64
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -55,7 +55,7 @@ Models:
backbone: BiSeNetV2
crop size: (1024,1024)
lr schd: 160000
memory (GB): 15.05
Training Memory (GB): 15.05
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -77,7 +77,7 @@ Models:
batch size: 1
mode: FP16
resolution: (1024,1024)
memory (GB): 5.77
Training Memory (GB): 5.77
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes

16
configs/ccnet/ccnet.yml
View File

@ -28,7 +28,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 6.0
Training Memory (GB): 6.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -50,7 +50,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 9.5
Training Memory (GB): 9.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -72,7 +72,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 6.8
Training Memory (GB): 6.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -94,7 +94,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 10.7
Training Memory (GB): 10.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -172,7 +172,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 8.8
Training Memory (GB): 8.8
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -194,7 +194,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 12.2
Training Memory (GB): 12.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -244,7 +244,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.0
Training Memory (GB): 6.0
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -266,7 +266,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.5
Training Memory (GB): 9.5
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug

4
configs/cgnet/cgnet.yml
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@ -26,7 +26,7 @@ Models:
batch size: 1
mode: FP32
resolution: (680,680)
memory (GB): 7.5
Training Memory (GB): 7.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -48,7 +48,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 8.3
Training Memory (GB): 8.3
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes

16
configs/danet/danet.yml
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@ -28,7 +28,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 7.4
Training Memory (GB): 7.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -49,7 +49,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 10.9
Training Memory (GB): 10.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -70,7 +70,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 8.8
Training Memory (GB): 8.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -92,7 +92,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 12.8
Training Memory (GB): 12.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -168,7 +168,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 11.5
Training Memory (GB): 11.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -190,7 +190,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 15.0
Training Memory (GB): 15.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -240,7 +240,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.5
Training Memory (GB): 6.5
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -262,7 +262,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.9
Training Memory (GB): 9.9
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug

44
configs/deeplabv3/deeplabv3.yml
View File

@ -32,7 +32,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 6.1
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -54,7 +54,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 9.6
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -76,7 +76,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 6.9
Training Memory (GB): 6.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -98,7 +98,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 10.9
Training Memory (GB): 10.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -120,7 +120,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 1.7
Training Memory (GB): 1.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -170,7 +170,7 @@ Models:
batch size: 1
mode: FP16
resolution: (512,1024)
memory (GB): 5.75
Training Memory (GB): 5.75
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -191,7 +191,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 1.9
Training Memory (GB): 1.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -255,7 +255,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 1.6
Training Memory (GB): 1.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -277,7 +277,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 6.0
Training Memory (GB): 6.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -299,7 +299,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 9.5
Training Memory (GB): 9.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -321,7 +321,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 1.8
Training Memory (GB): 1.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -343,7 +343,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 6.8
Training Memory (GB): 6.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -365,7 +365,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 10.7
Training Memory (GB): 10.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -387,7 +387,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 8.9
Training Memory (GB): 8.9
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -409,7 +409,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 12.4
Training Memory (GB): 12.4
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -459,7 +459,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.1
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -481,7 +481,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.6
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -531,7 +531,7 @@ Models:
batch size: 1
mode: FP32
resolution: (480,480)
memory (GB): 9.2
Training Memory (GB): 9.2
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
@ -595,7 +595,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.6
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
@ -617,7 +617,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 13.2
Training Memory (GB): 13.2
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
@ -667,7 +667,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.6
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
@ -689,7 +689,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 13.2
Training Memory (GB): 13.2
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k

44
configs/deeplabv3plus/deeplabv3plus.yml
View File

@ -31,7 +31,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 7.5
Training Memory (GB): 7.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -53,7 +53,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 11.0
Training Memory (GB): 11.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -75,7 +75,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 8.5
Training Memory (GB): 8.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -97,7 +97,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 12.5
Training Memory (GB): 12.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -119,7 +119,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 2.2
Training Memory (GB): 2.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -169,7 +169,7 @@ Models:
batch size: 1
mode: FP16
resolution: (512,1024)
memory (GB): 6.35
Training Memory (GB): 6.35
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -190,7 +190,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 2.5
Training Memory (GB): 2.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -240,7 +240,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 5.8
Training Memory (GB): 5.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -255,7 +255,7 @@ Models:
backbone: R-101-D16-MG124
crop size: (512,1024)
lr schd: 80000
memory (GB): 9.9
Training Memory (GB): 9.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -277,7 +277,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 2.1
Training Memory (GB): 2.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -299,7 +299,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 7.4
Training Memory (GB): 7.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -321,7 +321,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 10.9
Training Memory (GB): 10.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -343,7 +343,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 2.4
Training Memory (GB): 2.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -365,7 +365,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 8.4
Training Memory (GB): 8.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -387,7 +387,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 12.3
Training Memory (GB): 12.3
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -409,7 +409,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 10.6
Training Memory (GB): 10.6
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -431,7 +431,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 14.1
Training Memory (GB): 14.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -481,7 +481,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 7.6
Training Memory (GB): 7.6
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -503,7 +503,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 11.0
Training Memory (GB): 11.0
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -616,7 +616,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 1.93
Training Memory (GB): 1.93
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
@ -638,7 +638,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 7.37
Training Memory (GB): 7.37
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
@ -660,7 +660,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 10.84
Training Memory (GB): 10.84
Results:
- Task: Semantic Segmentation
Dataset: LoveDA

12
configs/dmnet/dmnet.yml
View File

@ -27,7 +27,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 7.0
Training Memory (GB): 7.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -49,7 +49,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 10.6
Training Memory (GB): 10.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -71,7 +71,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 7.9
Training Memory (GB): 7.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -93,7 +93,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 12.0
Training Memory (GB): 12.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -171,7 +171,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.4
Training Memory (GB): 9.4
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -193,7 +193,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 13.0
Training Memory (GB): 13.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K

12
configs/dnlnet/dnlnet.yml
View File

@ -27,7 +27,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 7.3
Training Memory (GB): 7.3
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -48,7 +48,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 10.9
Training Memory (GB): 10.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -69,7 +69,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 9.2
Training Memory (GB): 9.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -91,7 +91,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 12.6
Training Memory (GB): 12.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -167,7 +167,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 8.8
Training Memory (GB): 8.8
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -189,7 +189,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 12.8
Training Memory (GB): 12.8
Results:
- Task: Semantic Segmentation
Dataset: ADE20K

2
configs/dpt/dpt.yml
View File

@ -26,7 +26,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 8.09
Training Memory (GB): 8.09
Results:
- Task: Semantic Segmentation
Dataset: ADE20K

8
configs/emanet/emanet.yml
View File

@ -26,7 +26,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 5.4
Training Memory (GB): 5.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -48,7 +48,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 6.2
Training Memory (GB): 6.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -70,7 +70,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 8.9
Training Memory (GB): 8.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -92,7 +92,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 10.1
Training Memory (GB): 10.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes

12
configs/encnet/encnet.yml
View File

@ -27,7 +27,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 8.6
Training Memory (GB): 8.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -49,7 +49,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 12.1
Training Memory (GB): 12.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -71,7 +71,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 9.8
Training Memory (GB): 9.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -93,7 +93,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 13.7
Training Memory (GB): 13.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -171,7 +171,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 10.1
Training Memory (GB): 10.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -193,7 +193,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 13.6
Training Memory (GB): 13.6
Results:
- Task: Semantic Segmentation
Dataset: ADE20K

18
configs/fastfcn/fastfcn.yml
View File

@ -27,7 +27,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 5.67
Training Memory (GB): 5.67
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -42,7 +42,7 @@ Models:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
memory (GB): 9.79
Training Memory (GB): 9.79
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -64,7 +64,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 5.67
Training Memory (GB): 5.67
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -79,7 +79,7 @@ Models:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
memory (GB): 9.94
Training Memory (GB): 9.94
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -101,7 +101,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 8.15
Training Memory (GB): 8.15
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -116,7 +116,7 @@ Models:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
memory (GB): 15.45
Training Memory (GB): 15.45
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -138,7 +138,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 8.46
Training Memory (GB): 8.46
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -174,7 +174,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 8.02
Training Memory (GB): 8.02
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -210,7 +210,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 9.67
Training Memory (GB): 9.67
Results:
- Task: Semantic Segmentation
Dataset: ADE20K

2
configs/fastscnn/fastscnn.yml
View File

@ -24,7 +24,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 3.3
Training Memory (GB): 3.3
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes

50
configs/fcn/fcn.yml
View File

@ -30,7 +30,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 5.7
Training Memory (GB): 5.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -52,7 +52,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 9.2
Training Memory (GB): 9.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -74,7 +74,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 6.5
Training Memory (GB): 6.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -96,7 +96,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 10.4
Training Memory (GB): 10.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -118,7 +118,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 1.7
Training Memory (GB): 1.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -168,7 +168,7 @@ Models:
batch size: 1
mode: FP16
resolution: (512,1024)
memory (GB): 5.37
Training Memory (GB): 5.37
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -189,7 +189,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 1.9
Training Memory (GB): 1.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -239,7 +239,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 1.6
Training Memory (GB): 1.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -261,7 +261,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 5.6
Training Memory (GB): 5.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -283,7 +283,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 9.1
Training Memory (GB): 9.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -305,7 +305,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 1.7
Training Memory (GB): 1.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -327,7 +327,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 6.3
Training Memory (GB): 6.3
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -349,7 +349,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 10.3
Training Memory (GB): 10.3
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -371,7 +371,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 3.4
Training Memory (GB): 3.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -414,7 +414,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 3.7
Training Memory (GB): 3.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -457,7 +457,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 4.5
Training Memory (GB): 4.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -500,7 +500,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 5.0
Training Memory (GB): 5.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -543,7 +543,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 3.2
Training Memory (GB): 3.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -565,7 +565,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 3.6
Training Memory (GB): 3.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -587,7 +587,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 4.3
Training Memory (GB): 4.3
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -609,7 +609,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 4.8
Training Memory (GB): 4.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -631,7 +631,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 8.5
Training Memory (GB): 8.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -653,7 +653,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 12.0
Training Memory (GB): 12.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -703,7 +703,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 5.7
Training Memory (GB): 5.7
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -725,7 +725,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.2
Training Memory (GB): 9.2
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug

16
configs/gcnet/gcnet.yml
View File

@ -28,7 +28,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 5.8
Training Memory (GB): 5.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -50,7 +50,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 9.2
Training Memory (GB): 9.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -72,7 +72,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 6.5
Training Memory (GB): 6.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -94,7 +94,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 10.5
Training Memory (GB): 10.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -172,7 +172,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 8.5
Training Memory (GB): 8.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -194,7 +194,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 12.0
Training Memory (GB): 12.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -244,7 +244,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 5.8
Training Memory (GB): 5.8
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -266,7 +266,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.2
Training Memory (GB): 9.2
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug

26
configs/hrnet/hrnet.yml
View File

@ -31,7 +31,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 1.7
Training Memory (GB): 1.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -53,7 +53,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 2.9
Training Memory (GB): 2.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -75,7 +75,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 6.2
Training Memory (GB): 6.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -181,7 +181,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 3.8
Training Memory (GB): 3.8
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -203,7 +203,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 4.9
Training Memory (GB): 4.9
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -225,7 +225,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 8.2
Training Memory (GB): 8.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -289,7 +289,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 1.8
Training Memory (GB): 1.8
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -311,7 +311,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 2.9
Training Memory (GB): 2.9
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -333,7 +333,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.2
Training Memory (GB): 6.2
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -397,7 +397,7 @@ Models:
batch size: 1
mode: FP32
resolution: (480,480)
memory (GB): 6.1
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
@ -461,7 +461,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 1.72
Training Memory (GB): 1.72
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
@ -483,7 +483,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 2.9
Training Memory (GB): 2.9
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
@ -505,7 +505,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.25
Training Memory (GB): 6.25
Results:
- Task: Semantic Segmentation
Dataset: LoveDA

6
configs/icnet/icnet.yml
View File

@ -26,7 +26,7 @@ Models:
batch size: 1
mode: FP32
resolution: (832,832)
memory (GB): 1.7
Training Memory (GB): 1.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -90,7 +90,7 @@ Models:
batch size: 1
mode: FP32
resolution: (832,832)
memory (GB): 2.53
Training Memory (GB): 2.53
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -154,7 +154,7 @@ Models:
batch size: 1
mode: FP32
resolution: (832,832)
memory (GB): 3.08
Training Memory (GB): 3.08
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes

32
configs/isanet/isanet.yml
View File

@ -28,7 +28,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 5.869
Training Memory (GB): 5.869
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -50,7 +50,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 5.869
Training Memory (GB): 5.869
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -72,7 +72,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 6.759
Training Memory (GB): 6.759
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -94,7 +94,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 6.759
Training Memory (GB): 6.759
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -116,7 +116,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 9.425
Training Memory (GB): 9.425
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -138,7 +138,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 9.425
Training Memory (GB): 9.425
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -160,7 +160,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 10.815
Training Memory (GB): 10.815
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -182,7 +182,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 10.815
Training Memory (GB): 10.815
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -204,7 +204,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.0
Training Memory (GB): 9.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -226,7 +226,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.0
Training Memory (GB): 9.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -248,7 +248,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 12.562
Training Memory (GB): 12.562
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -270,7 +270,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 12.562
Training Memory (GB): 12.562
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -292,7 +292,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 5.9
Training Memory (GB): 5.9
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -314,7 +314,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 5.9
Training Memory (GB): 5.9
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -336,7 +336,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.465
Training Memory (GB): 9.465
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -358,7 +358,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.465
Training Memory (GB): 9.465
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug

16
configs/mobilenet_v2/mobilenet_v2.yml
View File

@ -27,7 +27,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 3.4
Training Memory (GB): 3.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -48,7 +48,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 3.6
Training Memory (GB): 3.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -69,7 +69,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 3.9
Training Memory (GB): 3.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -90,7 +90,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 5.1
Training Memory (GB): 5.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -111,7 +111,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.5
Training Memory (GB): 6.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20k
@ -132,7 +132,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.5
Training Memory (GB): 6.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20k
@ -153,7 +153,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.8
Training Memory (GB): 6.8
Results:
- Task: Semantic Segmentation
Dataset: ADE20k
@ -174,7 +174,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 8.2
Training Memory (GB): 8.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20k

8
configs/mobilenet_v3/mobilenet_v3.yml
View File

@ -26,7 +26,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 8.9
Training Memory (GB): 8.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -48,7 +48,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 8.9
Training Memory (GB): 8.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -70,7 +70,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 5.3
Training Memory (GB): 5.3
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -92,7 +92,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 5.3
Training Memory (GB): 5.3
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes

16
configs/nonlocal_net/nonlocal_net.yml
View File

@ -28,7 +28,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 7.4
Training Memory (GB): 7.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -49,7 +49,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 10.9
Training Memory (GB): 10.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -70,7 +70,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 8.9
Training Memory (GB): 8.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -92,7 +92,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 12.8
Training Memory (GB): 12.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -168,7 +168,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.1
Training Memory (GB): 9.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -190,7 +190,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 12.6
Training Memory (GB): 12.6
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -240,7 +240,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.4
Training Memory (GB): 6.4
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -262,7 +262,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.8
Training Memory (GB): 9.8
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug

22
configs/ocrnet/ocrnet.yml
View File

@ -28,7 +28,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 3.5
Training Memory (GB): 3.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -50,7 +50,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 4.7
Training Memory (GB): 4.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -72,7 +72,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 8.0
Training Memory (GB): 8.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -191,7 +191,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 8.8
Training Memory (GB): 8.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -212,7 +212,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 8.8
Training Memory (GB): 8.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -233,7 +233,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.7
Training Memory (GB): 6.7
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -255,7 +255,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 7.9
Training Memory (GB): 7.9
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -277,7 +277,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 11.2
Training Memory (GB): 11.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -341,7 +341,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 3.5
Training Memory (GB): 3.5
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -363,7 +363,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 4.7
Training Memory (GB): 4.7
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -385,7 +385,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 8.1
Training Memory (GB): 8.1
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug

8
configs/point_rend/point_rend.yml
View File

@ -27,7 +27,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 3.1
Training Memory (GB): 3.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -49,7 +49,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 4.2
Training Memory (GB): 4.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -71,7 +71,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 5.1
Training Memory (GB): 5.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -93,7 +93,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.1
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K

16
configs/psanet/psanet.yml
View File

@ -28,7 +28,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 7.0
Training Memory (GB): 7.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -50,7 +50,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 10.5
Training Memory (GB): 10.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -72,7 +72,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 7.9
Training Memory (GB): 7.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -94,7 +94,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 11.9
Training Memory (GB): 11.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -172,7 +172,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.0
Training Memory (GB): 9.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -194,7 +194,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 12.5
Training Memory (GB): 12.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -244,7 +244,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.9
Training Memory (GB): 6.9
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -266,7 +266,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 10.4
Training Memory (GB): 10.4
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug

50
configs/pspnet/pspnet.yml
View File

@ -34,7 +34,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 6.1
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -56,7 +56,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 9.6
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -78,7 +78,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 6.9
Training Memory (GB): 6.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -100,7 +100,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 10.9
Training Memory (GB): 10.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -122,7 +122,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 1.7
Training Memory (GB): 1.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -172,7 +172,7 @@ Models:
batch size: 1
mode: FP16
resolution: (512,1024)
memory (GB): 5.34
Training Memory (GB): 5.34
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -193,7 +193,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 1.9
Training Memory (GB): 1.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -243,7 +243,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 1.5
Training Memory (GB): 1.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -265,7 +265,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 6.0
Training Memory (GB): 6.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -287,7 +287,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 9.5
Training Memory (GB): 9.5
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -309,7 +309,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 1.7
Training Memory (GB): 1.7
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -331,7 +331,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 6.8
Training Memory (GB): 6.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -353,7 +353,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 10.8
Training Memory (GB): 10.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -375,7 +375,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 8.5
Training Memory (GB): 8.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -397,7 +397,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 12.0
Training Memory (GB): 12.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -447,7 +447,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.1
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -469,7 +469,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.6
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -519,7 +519,7 @@ Models:
batch size: 1
mode: FP32
resolution: (480,480)
memory (GB): 8.8
Training Memory (GB): 8.8
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
@ -583,7 +583,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.6
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
@ -605,7 +605,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 13.2
Training Memory (GB): 13.2
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 10k
@ -655,7 +655,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.6
Training Memory (GB): 9.6
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
@ -677,7 +677,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 13.2
Training Memory (GB): 13.2
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
@ -755,7 +755,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 1.45
Training Memory (GB): 1.45
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
@ -777,7 +777,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.14
Training Memory (GB): 6.14
Results:
- Task: Semantic Segmentation
Dataset: LoveDA
@ -799,7 +799,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.61
Training Memory (GB): 9.61
Results:
- Task: Semantic Segmentation
Dataset: LoveDA

16
configs/resnest/resnest.yml
View File

@ -27,7 +27,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 11.4
Training Memory (GB): 11.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -49,7 +49,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 11.8
Training Memory (GB): 11.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -71,7 +71,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 11.9
Training Memory (GB): 11.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -93,7 +93,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 13.2
Training Memory (GB): 13.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -115,7 +115,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 14.2
Training Memory (GB): 14.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20k
@ -137,7 +137,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 14.2
Training Memory (GB): 14.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20k
@ -159,7 +159,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 14.6
Training Memory (GB): 14.6
Results:
- Task: Semantic Segmentation
Dataset: ADE20k
@ -181,7 +181,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 16.2
Training Memory (GB): 16.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20k

14
configs/segformer/segformer.yml
View File

@ -26,7 +26,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 2.1
Training Memory (GB): 2.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20k
@ -48,7 +48,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 2.6
Training Memory (GB): 2.6
Results:
- Task: Semantic Segmentation
Dataset: ADE20k
@ -70,7 +70,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 3.6
Training Memory (GB): 3.6
Results:
- Task: Semantic Segmentation
Dataset: ADE20k
@ -92,7 +92,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 4.8
Training Memory (GB): 4.8
Results:
- Task: Semantic Segmentation
Dataset: ADE20k
@ -114,7 +114,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.1
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20k
@ -136,7 +136,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 7.2
Training Memory (GB): 7.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20k
@ -158,7 +158,7 @@ Models:
batch size: 1
mode: FP32
resolution: (640,640)
memory (GB): 11.5
Training Memory (GB): 11.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20k

8
configs/sem_fpn/sem_fpn.yml
View File

@ -27,7 +27,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 2.8
Training Memory (GB): 2.8
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -49,7 +49,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 3.9
Training Memory (GB): 3.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -71,7 +71,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 4.9
Training Memory (GB): 4.9
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -93,7 +93,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 5.9
Training Memory (GB): 5.9
Results:
- Task: Semantic Segmentation
Dataset: ADE20K

8
configs/setr/setr.yml
View File

@ -27,7 +27,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 18.4
Training Memory (GB): 18.4
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -49,7 +49,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 19.54
Training Memory (GB): 19.54
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -64,7 +64,7 @@ Models:
backbone: ViT-L
crop size: (512,512)
lr schd: 160000
memory (GB): 10.96
Training Memory (GB): 10.96
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -86,7 +86,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 17.3
Training Memory (GB): 17.3
Results:
- Task: Semantic Segmentation
Dataset: ADE20K

8
configs/swin/swin.yml
View File

@ -26,7 +26,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 5.02
Training Memory (GB): 5.02
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -48,7 +48,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.17
Training Memory (GB): 6.17
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -70,7 +70,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 7.61
Training Memory (GB): 7.61
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -106,7 +106,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 8.52
Training Memory (GB): 8.52
Results:
- Task: Semantic Segmentation
Dataset: ADE20K

24
configs/unet/unet.yml
View File

@ -22,7 +22,7 @@ Models:
backbone: UNet-S5-D16
crop size: (64,64)
lr schd: 40000
memory (GB): 0.68
Training Memory (GB): 0.68
Results:
- Task: Semantic Segmentation
Dataset: DRIVE
@ -36,7 +36,7 @@ Models:
backbone: UNet-S5-D16
crop size: (64,64)
lr schd: 40000
memory (GB): 0.599
Training Memory (GB): 0.599
Results:
- Task: Semantic Segmentation
Dataset: DRIVE
@ -50,7 +50,7 @@ Models:
backbone: UNet-S5-D16
crop size: (64,64)
lr schd: 40000
memory (GB): 0.596
Training Memory (GB): 0.596
Results:
- Task: Semantic Segmentation
Dataset: DRIVE
@ -64,7 +64,7 @@ Models:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.968
Training Memory (GB): 0.968
Results:
- Task: Semantic Segmentation
Dataset: STARE
@ -78,7 +78,7 @@ Models:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.982
Training Memory (GB): 0.982
Results:
- Task: Semantic Segmentation
Dataset: STARE
@ -92,7 +92,7 @@ Models:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.999
Training Memory (GB): 0.999
Results:
- Task: Semantic Segmentation
Dataset: STARE
@ -106,7 +106,7 @@ Models:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.968
Training Memory (GB): 0.968
Results:
- Task: Semantic Segmentation
Dataset: CHASE_DB1
@ -120,7 +120,7 @@ Models:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.982
Training Memory (GB): 0.982
Results:
- Task: Semantic Segmentation
Dataset: CHASE_DB1
@ -134,7 +134,7 @@ Models:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.999
Training Memory (GB): 0.999
Results:
- Task: Semantic Segmentation
Dataset: CHASE_DB1
@ -148,7 +148,7 @@ Models:
backbone: UNet-S5-D16
crop size: (256,256)
lr schd: 40000
memory (GB): 2.525
Training Memory (GB): 2.525
Results:
- Task: Semantic Segmentation
Dataset: HRF
@ -162,7 +162,7 @@ Models:
backbone: UNet-S5-D16
crop size: (256,256)
lr schd: 40000
memory (GB): 2.588
Training Memory (GB): 2.588
Results:
- Task: Semantic Segmentation
Dataset: HRF
@ -176,7 +176,7 @@ Models:
backbone: UNet-S5-D16
crop size: (256,256)
lr schd: 40000
memory (GB): 2.604
Training Memory (GB): 2.604
Results:
- Task: Semantic Segmentation
Dataset: HRF

16
configs/upernet/upernet.yml
View File

@ -28,7 +28,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 6.4
Training Memory (GB): 6.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -50,7 +50,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 7.4
Training Memory (GB): 7.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -72,7 +72,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 7.2
Training Memory (GB): 7.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -94,7 +94,7 @@ Models:
batch size: 1
mode: FP32
resolution: (769,769)
memory (GB): 8.4
Training Memory (GB): 8.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@ -172,7 +172,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 8.1
Training Memory (GB): 8.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -194,7 +194,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.1
Training Memory (GB): 9.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -244,7 +244,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 6.4
Training Memory (GB): 6.4
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@ -266,7 +266,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 7.5
Training Memory (GB): 7.5
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug

22
configs/vit/vit.yml
View File

@ -26,7 +26,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.2
Training Memory (GB): 9.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -48,7 +48,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.2
Training Memory (GB): 9.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -70,7 +70,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.21
Training Memory (GB): 9.21
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -92,7 +92,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 4.68
Training Memory (GB): 4.68
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -114,7 +114,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 4.68
Training Memory (GB): 4.68
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -136,7 +136,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 5.69
Training Memory (GB): 5.69
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -158,7 +158,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 5.69
Training Memory (GB): 5.69
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -180,7 +180,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 7.75
Training Memory (GB): 7.75
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -202,7 +202,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 7.75
Training Memory (GB): 7.75
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -224,7 +224,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.21
Training Memory (GB): 9.21
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@ -246,7 +246,7 @@ Models:
batch size: 1
mode: FP32
resolution: (512,512)
memory (GB): 9.21
Training Memory (GB): 9.21
Results:
- Task: Semantic Segmentation
Dataset: ADE20K