osmr / imgclsmob
run from
| TOP 1 ACCURACY | TOP 5 ACCURACY |
SPEED
|
|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MODEL | CODE | PAPER |
ε-REPR
|
CODE | PAPER |
ε-REPR
|
PAPER |
GLOBAL RANK
|
|||||
|
108-MENet-8x1
(g=3) |
56.1% | -- | 79.2% | -- | #416 | ||||||||
|
1.0-SqNxt-23
|
57.5% | -- | 80.9% | -- | #414 | ||||||||
|
1.0-SqNxt-23v5
|
59.2% | -- | 82.2% | -- | #402 | ||||||||
|
128-MENet-8x1
(g=4) |
57.6% | -- | 80.4% | -- | #412 | ||||||||
|
1.5-SqNxt-23
|
65.1% | -- | 86.5% | -- | #388 | ||||||||
|
1.5-SqNxt-23v5
|
66.2% | -- | 87.0% | -- | #372 | ||||||||
|
160-MENet-8x1
(g=8) |
56.2% | -- | 79.2% | -- | #418 | ||||||||
|
2.0-SqNxt-23
|
69.4% | -- | 89.0% | -- | #347 | ||||||||
|
2.0-SqNxt-23v5
|
70.4% | -- | 89.3% | -- | #340 | ||||||||
|
228-MENet-12x1
(g=3) |
65.9% | -- | 86.8% | -- | #379 | ||||||||
|
256-MENet-12x1
(g=4) |
67.3% | -- | 87.5% | -- | #368 | ||||||||
|
348-MENet-12x1
(g=3) |
71.8% | -- | 90.4% | -- | #324 | ||||||||
|
352-MENet-12x1
(g=8) |
68.4% | -- | 88.0% | -- | #359 | ||||||||
|
456-MENet-24x1
(g=3) |
74.7% | -- | 92.0% | -- | #283 | ||||||||
|
AlexNet
|
59.0% | -- | 81.8% | -- | 417.5 | #406 | |||||||
|
AlexNet-b
|
58.4% | -- | 81.0% | -- | 418.1 | #409 | |||||||
|
BAM-ResNet-50
|
76.9% | -- | 93.4% | -- | #224 | ||||||||
|
BN-Inception
|
74.6% | -- | 92.3% | -- | 426.1 | #279 | |||||||
|
BN-VGG-11
|
71.0% | -- | 90.4% | -- | 420.9 | #323 | |||||||
|
BN-VGG-11b
|
70.4% | -- | 90.0% | -- | 415.6 | #336 | |||||||
|
BN-VGG-13
|
72.2% | -- | 90.9% | -- | 406.0 | #318 | |||||||
|
BN-VGG-13b
|
71.6% | -- | 90.4% | -- | 394.5 | #324 | |||||||
|
BN-VGG-16
|
74.3% | -- | 92.2% | -- | 400.7 | #288 | |||||||
|
BN-VGG-16b
|
72.8% | -- | 91.3% | -- | 394.4 | #307 | |||||||
|
BN-VGG-19
|
75.9% | -- | 92.9% | -- | 395.8 | #250 | |||||||
|
BN-VGG-19b
|
73.9% | -- | 91.6% | -- | 385.3 | #296 | |||||||
|
CBAM-ResNet-50
|
77.6% | -- | 93.9% | -- | #190 | ||||||||
|
CondenseNet-74
(C=G=4) |
73.8% | -- | 91.7% | -- | #297 | ||||||||
|
CondenseNet-74
(C=G=8) |
71.1% | -- | 89.9% | -- | #333 | ||||||||
|
DarkNet-53
|
78.3% | -- | 94.4% | -- | #179 | ||||||||
|
DARTS
|
73.3% | -- | 91.3% | -- | #308 | ||||||||
|
DenseNet-121
|
76.5% | 76.4% | 93.0% | 93.3% | #240 | ||||||||
|
DenseNet-161
|
77.1% | -- | 93.6% | -- | #212 | ||||||||
|
DenseNet-169
|
77.6% | 77.9% | 93.7% | 94.1% | #202 | ||||||||
|
DenseNet-201
|
76.9% | 78.5% | 93.4% | 94.5% | #228 | ||||||||
|
DiracNetV2-18
|
68.5% | -- | 88.3% | -- | #359 | ||||||||
|
DiracNetV2-34
|
71.2% | -- | 90.1% | -- | #333 | ||||||||
|
DLA-102
|
78.0% | -- | 94.0% | -- | #189 | ||||||||
|
DLA-169
|
78.7% | -- | 94.3% | -- | #149 | ||||||||
|
DLA-34
|
74.6% | -- | 92.1% | -- | #281 | ||||||||
|
DLA-46-C
|
65.7% | -- | 86.8% | -- | #374 | ||||||||
|
DLA-60
|
77.0% | -- | 93.3% | -- | #226 | ||||||||
|
DLA-X-102
|
78.5% | -- | 94.2% | -- | #166 | ||||||||
|
DLA-X2-102
|
79.5% | -- | 94.6% | -- | #109 | ||||||||
|
DLA-X-46-C
|
66.7% | -- | 87.3% | -- | #368 | ||||||||
|
DLA-X-60
|
78.2% | -- | 94.0% | -- | #182 | ||||||||
|
DLA-X-60-C
|
69.0% | -- | 89.1% | -- | #353 | ||||||||
|
DPN-131
|
79.5% | -- | 94.5% | -- | #107 | ||||||||
|
DPN-68
|
76.8% | -- | 93.2% | -- | #235 | ||||||||
|
DPN-98
|
79.2% | -- | 94.5% | -- | #124 | ||||||||
|
DRN-C-26
|
75.1% | -- | 92.5% | -- | #265 | ||||||||
|
DRN-C-42
|
77.1% | -- | 93.4% | -- | #222 | ||||||||
|
DRN-C-58
|
78.3% | -- | 94.0% | -- | #185 | ||||||||
|
DRN-D-105
|
79.4% | -- | 94.5% | -- | #112 | ||||||||
|
DRN-D-22
|
74.2% | -- | 91.8% | -- | #290 | ||||||||
|
DRN-D-38
|
76.2% | -- | 93.1% | -- | #241 | ||||||||
|
DRN-D-54
|
78.8% | -- | 94.1% | -- | #149 | ||||||||
|
EfficientNet-B0
|
75.2% | 76.3% | 92.5% | 93.2% | #261 | ||||||||
|
EfficientNet-B0b
|
76.1% | -- | 93.0% | -- | #244 | ||||||||
|
EfficientNet-B1
|
77.0% | 78.8% | 93.6% | 94.4% | #211 | ||||||||
|
EfficientNet-B1b
|
78.4% | -- | 94.1% | -- | #177 | ||||||||
|
EfficientNet-B2b
|
79.7% | -- | 94.7% | -- | 368.5 | #101 | |||||||
|
EfficientNet-B3b
|
81.4% | -- | 95.6% | -- | 328.4 | #55 | |||||||
|
EfficientNet-B4b
|
82.8% | -- | 96.2% | -- | 221.8 | #41 | |||||||
|
EfficientNet-B5b
|
83.6% | -- | 96.7% | -- | 115.4 | #27 | |||||||
|
EfficientNet-B6b
|
84.1% | -- | 96.9% | -- | 68.4 | #22 | |||||||
|
EfficientNet-B7b
|
84.3% | -- | 96.9% | -- | 39.5 | #14 | |||||||
|
ESPNetv2 x0.5
|
57.7% | -- | 79.8% | -- | #415 | ||||||||
|
ESPNetv2 x1.0
|
66.1% | -- | 86.5% | -- | #380 | ||||||||
|
ESPNetv2 x1.25
|
67.9% | -- | 87.8% | -- | #362 | ||||||||
|
ESPNetv2 x1.5
|
69.2% | -- | 88.7% | -- | #352 | ||||||||
|
ESPNetv2 x2.0
|
72.1% | -- | 90.4% | -- | #322 | ||||||||
|
FBNet-Cb
|
75.1% | -- | 92.4% | -- | 407.7 | #267 | |||||||
|
FD-MobileNet x0.25
|
44.1% | -- | 69.0% | -- | #424 | ||||||||
|
FD-MobileNet x0.5
|
56.9% | -- | 79.8% | -- | #415 | ||||||||
|
FD-MobileNet x0.75
|
61.6% | -- | 83.6% | -- | #396 | ||||||||
|
FD-MobileNet x1.0
|
65.8% | -- | 86.6% | -- | #382 | ||||||||
|
HRNetV2-W18
|
76.8% | -- | 93.4% | -- | 382.9 | #236 | |||||||
|
HRNetV2-W30
|
78.2% | -- | 94.2% | -- | 377.8 | #160 | |||||||
|
HRNetV2-W32
|
78.4% | -- | 94.2% | -- | 374.2 | #165 | |||||||
|
HRNetV2-W40
|
78.9% | -- | 94.5% | -- | 354.0 | #126 | |||||||
|
HRNetV2-W44
|
78.9% | -- | 94.4% | -- | 333.8 | #143 | |||||||
|
HRNetV2-W48
|
79.3% | -- | 94.5% | -- | 315.6 | #116 | |||||||
|
HRNetV2-W64
|
79.5% | -- | 94.7% | -- | 265.0 | #106 | |||||||
|
HRNet-W18 Small V1
|
72.3% | -- | 90.7% | -- | 399.5 | #315 | |||||||
|
HRNet-W18 Small V2
|
75.1% | -- | 92.4% | -- | 393.6 | #267 | |||||||
|
IBN(b)-ResNet-50
|
76.4% | -- | 93.1% | -- | #242 | ||||||||
|
IBN-DenseNet-121
|
75.5% | -- | 92.8% | -- | #255 | ||||||||
|
IBN-DenseNet-169
|
76.8% | -- | 93.5% | -- | #216 | ||||||||
|
IBN-ResNet-101
|
78.7% | -- | 94.4% | -- | #138 | ||||||||
|
IBN-ResNet-50
|
77.2% | -- | 93.6% | -- | #217 | ||||||||
|
IBN-ResNeXt-101
(32x4d) |
79.1% | -- | 94.6% | -- | #115 | ||||||||
|
IGCV3 x0.25
|
46.3% | -- | 71.3% | -- | #423 | ||||||||
|
IGCV3 x0.5
|
60.2% | -- | 82.7% | -- | #399 | ||||||||
|
IGCV3 x0.75
|
68.9% | -- | 88.6% | -- | #353 | ||||||||
|
IGCV3 x1.0
|
72.1% | -- | 90.8% | -- | #314 | ||||||||
|
InceptionResNetV
|
80.1% | -- | 95.1% | -- | #88 | ||||||||
|
InceptionV3
|
78.9% | -- | 94.4% | -- | #147 | ||||||||
|
InceptionV4
|
79.4% | -- | 94.7% | -- | #113 | ||||||||
|
i-RevNet-301
|
74.0% | -- | 91.6% | -- | #294 | ||||||||
|
MixNet-L
|
78.9% | 78.9% | 94.2% | 94.2% | 400.3 | #168 | |||||||
|
MixNet-M
|
77.1% | 77.0% | 93.4% | 93.3% | 404.0 | #223 | |||||||
|
MixNet-S
|
76.0% | 75.8% | 92.8% | 92.8% | 413.1 | #250 | |||||||
|
MnasNet-A1
|
75.3% | 75.2% | 92.6% | 92.5% | 413.1 | #258 | |||||||
|
MnasNet-B1
|
74.6% | -- | 92.2% | -- | 415.4 | #280 | |||||||
|
MobileNetV2 x0.25
|
51.7% | -- | 75.5% | -- | #422 | ||||||||
|
MobileNetV2 x0.5
|
64.0% | -- | 85.1% | -- | #393 | ||||||||
|
MobileNetV2 x0.75
|
69.8% | -- | 89.2% | -- | #343 | ||||||||
|
MobileNetV2 x1.0
|
73.0% | -- | 91.1% | -- | #308 | ||||||||
|
MobileNetV3 L/224/1.0
|
75.1% | -- | 92.2% | -- | #264 | ||||||||
|
MobileNet x0.25
|
53.7% | -- | 77.5% | -- | #420 | ||||||||
|
MobileNet x0.5
|
65.8% | -- | 86.5% | -- | #384 | ||||||||
|
MobileNet x0.75
|
69.9% | -- | 89.2% | -- | #341 | ||||||||
|
MobileNet x1.0
|
73.4% | -- | 91.0% | -- | #311 | ||||||||
|
NASNet-A [email protected]
|
74.3% | -- | 91.8% | -- | #287 | ||||||||
|
NASNet-A [email protected]
|
81.9% | -- | 95.8% | -- | #51 | ||||||||
|
PeleeNet
|
68.2% | -- | 88.5% | -- | #360 | ||||||||
|
PNASNet-5-Large
|
82.1% | -- | 95.7% | -- | #44 | ||||||||
|
PolyNet
|
80.9% | 81.3% | 95.5% | 95.8% | #62 | ||||||||
|
PrepResNet-10
|
64.9% | -- | 85.8% | -- | #390 | ||||||||
|
PreResNet-101
|
78.3% | -- | 94.1% | -- | #178 | ||||||||
|
PreResNet-101b
|
79.0% | -- | 94.4% | -- | #129 | ||||||||
|
PreResNet-12
|
66.1% | -- | 86.5% | -- | #373 | ||||||||
|
PreResNet-14
|
67.4% | -- | 87.6% | -- | #367 | ||||||||
|
PreResNet-152
|
79.1% | -- | 94.5% | -- | #131 | ||||||||
|
PreResNet-152b
|
79.9% | -- | 94.8% | -- | #94 | ||||||||
|
PreResNet-16
|
69.5% | -- | 88.9% | -- | #349 | ||||||||
|
PreResNet-18
|
71.6% | -- | 90.3% | -- | #326 | ||||||||
|
PreResNet-200b
|
78.7% | -- | 94.1% | -- | #158 | ||||||||
|
PreResNet-26
|
73.7% | -- | 91.5% | -- | #299 | ||||||||
|
PreResNet-269b
|
79.1% | -- | 94.2% | -- | #163 | ||||||||
|
PreResNet-34
|
75.1% | -- | 92.3% | -- | #271 | ||||||||
|
PreResNet-50
|
77.6% | -- | 93.5% | -- | #201 | ||||||||
|
PreResNet-50b
|
77.5% | -- | 93.4% | -- | #208 | ||||||||
|
PreResNet-BC-26b
|
74.5% | -- | 92.0% | -- | #285 | ||||||||
|
PreResNet-BC-38b
|
77.1% | -- | 93.4% | -- | #221 | ||||||||
|
ProxylessNAS CPU
|
75.3% | -- | 92.4% | -- | #265 | ||||||||
|
ProxylessNAS GPU
|
75.2% | -- | 92.6% | -- | #259 | ||||||||
|
ProxylessNAS Mob-14
|
76.7% | -- | 93.4% | -- | #227 | ||||||||
|
ProxylessNAS Mobile
|
74.6% | -- | 92.2% | -- | #274 | ||||||||
|
PyramidNet-101
(a=360) |
78.0% | -- | 93.8% | -- | #192 | ||||||||
|
ResNet-10
|
65.3% | -- | 85.6% | -- | #387 | ||||||||
|
ResNet-101
|
78.1% | 78.2% | 93.8% | 94.0% | #196 | ||||||||
|
ResNet-101b
|
79.4% | -- | 94.7% | -- | #106 | ||||||||
|
ResNet-12
|
66.4% | -- | 86.7% | -- | #371 | ||||||||
|
ResNet-14
|
67.5% | -- | 87.5% | -- | #365 | ||||||||
|
ResNet-152
|
79.0% | 78.6% |
|
94.5% | 94.3% | #138 | |||||||
|
ResNet-152b
|
80.1% | -- | 95.0% | -- | #87 | ||||||||
|
ResNet-16
|
69.5% | -- | 88.8% | -- | #348 | ||||||||
|
ResNet-18
|
71.5% | 72.1% | 90.2% | -- | 412.9 | #331 | |||||||
|
ResNet-26
|
73.7% | -- | 91.5% | -- | #300 | ||||||||
|
ResNet-34
|
75.2% | -- | 92.2% | -- | #272 | ||||||||
|
ResNet-50
|
77.7% | 77.1% |
|
93.7% | 93.3% |
|
#197 | ||||||
|
ResNet-50b
|
77.6% | -- | 93.6% | -- | #205 | ||||||||
|
ResNet-BC-26b
|
74.9% | -- | 92.0% | -- | #272 | ||||||||
|
ResNet-BC-38b
|
76.3% | -- | 93.0% | -- | #243 | ||||||||
|
ResNeXt-101
(32x4d) |
80.0% | -- | 94.8% | -- | #99 | ||||||||
|
ResNeXt-101
(64x4d) |
80.4% | -- | 94.9% | -- | #88 | ||||||||
|
ResNeXt-14
(16x4d) |
68.1% | -- | 87.5% | -- | #366 | ||||||||
|
ResNeXt-14
(32x2d) |
67.4% | -- | 87.2% | -- | #366 | ||||||||
|
ResNeXt-14
(32x4d) |
69.7% | -- | 88.5% | -- | #355 | ||||||||
|
ResNeXt-26
(32x2d) |
73.4% | -- | 91.1% | -- | #310 | ||||||||
|
ResNeXt-26
(32x4d) |
75.9% | -- | 92.5% | -- | #252 | ||||||||
|
ResNeXt-50
(32x4d) |
79.2% | -- | 94.4% | -- | #136 | ||||||||
|
SENet-154
|
81.4% | 82.7% | 95.4% | 96.2% | #65 | ||||||||
|
SENet-16
|
74.4% | -- | 91.8% | -- | #285 | ||||||||
|
SENet-28
|
78.1% | -- | 94.0% | -- | #189 | ||||||||
|
SE-PreResNet-10
|
66.0% | -- | 86.6% | -- | #377 | ||||||||
|
SE-PreResNet-18
|
71.9% | -- | 90.4% | -- | #321 | ||||||||
|
SE-PreResNet-BC-26b
|
76.8% | -- | 93.4% | -- | #225 | ||||||||
|
SE-PreResNet-BC-38b
|
78.4% | -- | 94.2% | -- | #158 | ||||||||
|
SE-ResNet-10
|
66.1% | -- | 86.3% | -- | #374 | ||||||||
|
SE-ResNet-101
|
78.1% | -- | 94.1% | -- | #187 | ||||||||
|
SE-ResNet-152
|
78.5% | -- | 94.2% | -- | #155 | ||||||||
|
SE-ResNet-18
|
71.8% | -- | 90.4% | -- | #322 | ||||||||
|
SE-ResNet-26
|
74.3% | -- | 91.8% | -- | #291 | ||||||||
|
SE-ResNet-50
|
77.5% | -- | 93.6% | -- | #206 | ||||||||
|
SE-ResNet-50b
|
79.2% | -- | 94.6% | -- | #122 | ||||||||
|
SE-ResNet-BC-26b
|
76.4% | -- | 93.0% | -- | #245 | ||||||||
|
SE-ResNet-BC-38b
|
78.4% | -- | 94.1% | -- | #179 | ||||||||
|
SE-ResNeXt-101
(32x4d) |
80.8% | -- | 95.2% | -- | 369.8 | #73 | |||||||
|
SE-ResNeXt-101
(64x4d) |
80.7% | -- | 95.2% | -- | 263.6 | #71 | |||||||
|
SE-ResNeXt-50
(32x4d) |
79.7% | -- | 94.8% | -- | 389.8 | #98 | |||||||
|
ShuffleNetV2b x0.5
|
59.7% | -- | 81.8% | -- | #405 | ||||||||
|
ShuffleNetV2b x1.0
|
69.4% | -- | 88.8% | -- | #351 | ||||||||
|
ShuffleNetV2b x1.5
|
72.7% | -- | 90.9% | -- | #312 | ||||||||
|
ShuffleNetV2b x2.0
|
74.4% | -- | 91.7% | -- | #295 | ||||||||
|
ShuffleNetV2 x0.5
|
59.0% | -- | 81.4% | -- | #407 | ||||||||
|
ShuffleNetV2 x1.0
|
68.6% | -- | 88.4% | -- | #357 | ||||||||
|
ShuffleNetV2 x1.5
|
72.5% | -- | 90.6% | -- | #313 | ||||||||
|
ShuffleNetV2 x2.0
|
74.1% | -- | 91.6% | -- | #298 | ||||||||
|
ShuffleNet x0.25
(g=1) |
37.6% | -- | 62.7% | -- | #426 | ||||||||
|
ShuffleNet x0.25
(g=3) |
38.3% | -- | 63.5% | -- | #425 | ||||||||
|
ShuffleNet x0.5
(g=1) |
53.4% | -- | 77.4% | -- | #421 | ||||||||
|
ShuffleNet x0.5
(g=3) |
55.8% | -- | 79.2% | -- | #419 | ||||||||
|
ShuffleNet x0.75
(g=1) |
60.4% | -- | 82.9% | -- | #399 | ||||||||
|
ShuffleNet x0.75
(g=3) |
61.8% | -- | 83.5% | -- | #395 | ||||||||
|
ShuffleNet x1.0
(g=1) |
65.1% | -- | 86.1% | -- | #389 | ||||||||
|
ShuffleNet x1.0
(g=2) |
65.8% | -- | 86.4% | -- | #385 | ||||||||
|
ShuffleNet x1.0
(g=3) |
65.6% | -- | 86.5% | -- | #386 | ||||||||
|
ShuffleNet x1.0
(g=4) |
65.8% | -- | 86.6% | -- | #381 | ||||||||
|
ShuffleNet x1.0
(g=8) |
65.9% | -- | 86.6% | -- | #379 | ||||||||
|
SPNASNet
|
74.1% | -- | 91.8% | -- | 412.2 | #292 | |||||||
|
SqueezeNet v1.0
|
60.7% | -- | 82.3% | -- | #400 | ||||||||
|
SqueezeNet v1.1
|
60.7% | -- | 82.3% | -- | #398 | ||||||||
|
SqueezeResNet v1.0
|
60.2% | -- | 81.9% | -- | #403 | ||||||||
|
SqueezeResNet v1.1
|
59.9% | -- | 81.8% | -- | #404 | ||||||||
|
VGG-11
|
70.1% | 70.4% | 89.6% | 89.6% | 411.2 | #339 | |||||||
|
VGG-13
|
71.2% | 71.3% | 90.2% | 90.1% | 406.1 | #332 | |||||||
|
VGG-16
|
73.0% | 74.4% | 91.3% | 91.9% | 396.4 | #309 | |||||||
|
VGG-19
|
74.3% | 74.5% | 92.1% | 92.0% | 338.8 | #289 | |||||||
|
WRN-50-2
|
77.5% | -- | 93.6% | -- | #210 | ||||||||
|
Xception
|
79.0% | 79.0% | 94.5% | 94.5% | #137 | ||||||||
|
ZFNet
|
60.2% | 64.0% | 82.7% | 85.3% | 428.2 | #402 | |||||||
|
ZFNet-b
|
63.6% | -- | 85.1% | -- | 426.3 | #392 | |||||||
+get badge code
[](https://sotabench.com/user/osemery/repos/osmr/imgclsmob)
How the Repository is Evaluated
The fullsotabench.py file - source
from torchbench.image_classification import ImageNet
from pytorch.pytorchcv.model_provider import trained_model_metainfo_list
from pytorch.pytorchcv.model_provider import get_model as ptcv_get_model
import torchvision.transforms as transforms
import torch
import math
# import os
for model_metainfo in trained_model_metainfo_list:
net = ptcv_get_model(model_metainfo[0], pretrained=True)
input_image_size = model_metainfo[3]
resize_inv_factor = model_metainfo[4]
batch_size = model_metainfo[5]
model_description = model_metainfo[6]
assert (not hasattr(net, "in_size")) or (input_image_size == net.in_size[0])
ImageNet.benchmark(
model=net,
model_description=model_description,
paper_model_name=model_metainfo[1],
paper_arxiv_id=model_metainfo[2],
input_transform=transforms.Compose([
transforms.Resize(int(math.ceil(float(input_image_size) / resize_inv_factor))),
transforms.CenterCrop(input_image_size),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
]),
batch_size=batch_size,
num_gpu=1,
# data_root=os.path.join("..", "imgclsmob_data", "imagenet")
)
torch.cuda.empty_cache()
STATUS
BUILD
COMMIT MESSAGE
RUN TIME
#
36
Update PIP-packages (gluoncv2: 0.0.53, pytorchcv: 0.0.53, chaine…
osmr
72e817a
· 2 days ago
#
27
Update PIP-packages (gluoncv2: 0.0.52, pytorchcv: 0.0.52, chaine…
osmr
df5b285
· Nov 24 2019
#
17
Update PIP-packages (gluoncv2: 0.0.51, pytorchcv: 0.0.51, chaine…
osmr
458c23b
· Nov 10 2019