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% | -- | #376 | ||||||||
|
1.0-SqNxt-23
|
57.5% | -- | 80.9% | -- | #374 | ||||||||
|
1.0-SqNxt-23v5
|
59.2% | -- | 82.2% | -- | #362 | ||||||||
|
128-MENet-8x1
(g=4) |
57.6% | -- | 80.4% | -- | #373 | ||||||||
|
1.5-SqNxt-23
|
65.1% | -- | 86.5% | -- | #345 | ||||||||
|
1.5-SqNxt-23v5
|
66.2% | -- | 87.0% | -- | #334 | ||||||||
|
160-MENet-8x1
(g=8) |
56.2% | -- | 79.2% | -- | #378 | ||||||||
|
2.0-SqNxt-23
|
69.4% | -- | 89.0% | -- | #312 | ||||||||
|
2.0-SqNxt-23v5
|
70.4% | -- | 89.3% | -- | #299 | ||||||||
|
228-MENet-12x1
(g=3) |
65.9% | -- | 86.8% | -- | #341 | ||||||||
|
256-MENet-12x1
(g=4) |
67.3% | -- | 87.5% | -- | #329 | ||||||||
|
348-MENet-12x1
(g=3) |
71.8% | -- | 90.4% | -- | #286 | ||||||||
|
352-MENet-12x1
(g=8) |
68.4% | -- | 88.0% | -- | #324 | ||||||||
|
456-MENet-24x1
(g=3) |
74.7% | -- | 92.0% | -- | #248 | ||||||||
|
AlexNet
|
59.0% | -- | 81.8% | -- | 417.5 | #366 | |||||||
|
AlexNet-b
|
58.4% | -- | 81.0% | -- | 418.1 | #369 | |||||||
|
BAM-ResNet-50
|
76.9% | -- | 93.4% | -- | #197 | ||||||||
|
BN-Inception
|
74.6% | -- | 92.3% | -- | 426.1 | #246 | |||||||
|
BN-VGG-11
|
71.0% | -- | 90.4% | -- | 420.9 | #284 | |||||||
|
BN-VGG-11b
|
70.4% | -- | 90.0% | -- | 415.6 | #298 | |||||||
|
BN-VGG-13
|
72.2% | -- | 90.9% | -- | 406.0 | #280 | |||||||
|
BN-VGG-13b
|
71.6% | -- | 90.4% | -- | 394.5 | #286 | |||||||
|
BN-VGG-16
|
74.3% | -- | 92.2% | -- | 400.7 | #238 | |||||||
|
BN-VGG-16b
|
72.8% | -- | 91.3% | -- | 394.4 | #275 | |||||||
|
BN-VGG-19
|
75.9% | -- | 92.9% | -- | 395.8 | #221 | |||||||
|
BN-VGG-19b
|
73.9% | -- | 91.6% | -- | 385.3 | #261 | |||||||
|
CBAM-ResNet-50
|
77.6% | -- | 93.9% | -- | #174 | ||||||||
|
CondenseNet-74
(C=G=4) |
73.8% | -- | 91.7% | -- | #263 | ||||||||
|
CondenseNet-74
(C=G=8) |
71.1% | -- | 89.9% | -- | #297 | ||||||||
|
DarkNet-53
|
78.3% | -- | 94.4% | -- | #125 | ||||||||
|
DARTS
|
73.3% | -- | 91.3% | -- | #273 | ||||||||
|
DenseNet-121
|
76.5% | 76.4% | 93.0% | 93.3% | #211 | ||||||||
|
DenseNet-161
|
77.1% | -- | 93.6% | -- | #187 | ||||||||
|
DenseNet-169
|
77.6% | 77.9% | 93.7% | 94.1% | #177 | ||||||||
|
DenseNet-201
|
76.9% | 78.5% | 93.4% | 94.5% | #201 | ||||||||
|
DiracNetV2-18
|
68.5% | -- | 88.3% | -- | #321 | ||||||||
|
DiracNetV2-34
|
71.2% | -- | 90.1% | -- | #293 | ||||||||
|
DLA-102
|
78.0% | -- | 94.0% | -- | #164 | ||||||||
|
DLA-169
|
78.7% | -- | 94.3% | -- | #128 | ||||||||
|
DLA-34
|
74.6% | -- | 92.1% | -- | #246 | ||||||||
|
DLA-46-C
|
65.7% | -- | 86.8% | -- | #336 | ||||||||
|
DLA-60
|
77.0% | -- | 93.3% | -- | #204 | ||||||||
|
DLA-X-102
|
78.5% | -- | 94.2% | -- | #136 | ||||||||
|
DLA-X2-102
|
79.5% | -- | 94.6% | -- | #97 | ||||||||
|
DLA-X-46-C
|
66.7% | -- | 87.3% | -- | #330 | ||||||||
|
DLA-X-60
|
78.2% | -- | 94.0% | -- | #157 | ||||||||
|
DLA-X-60-C
|
69.0% | -- | 89.1% | -- | #307 | ||||||||
|
DPN-131
|
79.5% | -- | 94.5% | -- | #105 | ||||||||
|
DPN-68
|
76.8% | -- | 93.2% | -- | #207 | ||||||||
|
DPN-98
|
79.2% | -- | 94.5% | -- | #109 | ||||||||
|
DRN-C-26
|
75.1% | -- | 92.5% | -- | #231 | ||||||||
|
DRN-C-42
|
77.1% | -- | 93.4% | -- | #195 | ||||||||
|
DRN-C-58
|
78.3% | -- | 94.0% | -- | #160 | ||||||||
|
DRN-D-105
|
79.4% | -- | 94.5% | -- | #104 | ||||||||
|
DRN-D-22
|
74.2% | -- | 91.8% | -- | #255 | ||||||||
|
DRN-D-38
|
76.2% | -- | 93.1% | -- | #212 | ||||||||
|
DRN-D-54
|
78.8% | -- | 94.1% | -- | #145 | ||||||||
|
EfficientNet-B0
|
75.2% | 76.3% | 92.5% | 93.2% | #230 | ||||||||
|
EfficientNet-B0b
|
76.1% | -- | 93.0% | -- | #217 | ||||||||
|
EfficientNet-B1
|
77.0% | 78.8% | 93.6% | 94.4% | #197 | ||||||||
|
EfficientNet-B1b
|
78.4% | -- | 94.1% | -- | #152 | ||||||||
|
EfficientNet-B2b
|
79.7% | -- | 94.7% | -- | 368.5 | #88 | |||||||
|
EfficientNet-B3b
|
81.4% | -- | 95.6% | -- | 328.4 | #53 | |||||||
|
EfficientNet-B4b
|
82.8% | -- | 96.2% | -- | 221.8 | #36 | |||||||
|
EfficientNet-B5b
|
83.6% | -- | 96.7% | -- | 115.4 | #28 | |||||||
|
EfficientNet-B6b
|
84.1% | -- | 96.9% | -- | 68.4 | #18 | |||||||
|
EfficientNet-B7b
|
84.3% | -- | 96.9% | -- | 39.5 | #17 | |||||||
|
ESPNetv2 x0.5
|
57.7% | -- | 79.8% | -- | #375 | ||||||||
|
ESPNetv2 x1.0
|
66.1% | -- | 86.5% | -- | #342 | ||||||||
|
ESPNetv2 x1.25
|
67.9% | -- | 87.8% | -- | #325 | ||||||||
|
ESPNetv2 x1.5
|
69.2% | -- | 88.7% | -- | #314 | ||||||||
|
ESPNetv2 x2.0
|
72.1% | -- | 90.4% | -- | #285 | ||||||||
|
FBNet-Cb
|
75.1% | -- | 92.4% | -- | 407.7 | #236 | |||||||
|
FD-MobileNet x0.25
|
44.1% | -- | 69.0% | -- | #384 | ||||||||
|
FD-MobileNet x0.5
|
56.9% | -- | 79.8% | -- | #374 | ||||||||
|
FD-MobileNet x0.75
|
61.6% | -- | 83.6% | -- | #355 | ||||||||
|
FD-MobileNet x1.0
|
65.8% | -- | 86.6% | -- | #339 | ||||||||
|
IBN(b)-ResNet-50
|
76.4% | -- | 93.1% | -- | #213 | ||||||||
|
IBN-DenseNet-121
|
75.5% | -- | 92.8% | -- | #223 | ||||||||
|
IBN-DenseNet-169
|
76.8% | -- | 93.5% | -- | #191 | ||||||||
|
IBN-ResNet-101
|
78.7% | -- | 94.4% | -- | #120 | ||||||||
|
IBN-ResNet-50
|
77.2% | -- | 93.6% | -- | #191 | ||||||||
|
IBN-ResNeXt-101
(32x4d) |
79.1% | -- | 94.6% | -- | #102 | ||||||||
|
IGCV3 x0.25
|
46.3% | -- | 71.3% | -- | #383 | ||||||||
|
IGCV3 x0.5
|
60.2% | -- | 82.7% | -- | #359 | ||||||||
|
IGCV3 x0.75
|
68.9% | -- | 88.6% | -- | #317 | ||||||||
|
IGCV3 x1.0
|
72.1% | -- | 90.8% | -- | #279 | ||||||||
|
InceptionResNetV
|
80.1% | -- | 95.1% | -- | #68 | ||||||||
|
InceptionV3
|
78.9% | -- | 94.4% | -- | #126 | ||||||||
|
InceptionV4
|
79.4% | -- | 94.7% | -- | #93 | ||||||||
|
i-RevNet-301
|
74.0% | -- | 91.6% | -- | #259 | ||||||||
|
MixNet-L
|
78.9% | 78.9% | 94.2% | 94.2% | 400.3 | #143 | |||||||
|
MixNet-M
|
77.1% | 77.0% | 93.4% | 93.3% | 404.0 | #200 | |||||||
|
MixNet-S
|
76.0% | 75.8% | 92.8% | 92.8% | 413.1 | #218 | |||||||
|
MnasNet
|
68.4% | -- | 88.3% | -- | #322 | ||||||||
|
MobileNetV2 x0.25
|
51.7% | -- | 75.5% | -- | #382 | ||||||||
|
MobileNetV2 x0.5
|
64.0% | -- | 85.1% | -- | #354 | ||||||||
|
MobileNetV2 x0.75
|
69.8% | -- | 89.2% | -- | #305 | ||||||||
|
MobileNetV2 x1.0
|
73.0% | -- | 91.1% | -- | #273 | ||||||||
|
MobileNetV3 L/224/1.0
|
75.1% | -- | 92.2% | -- | #233 | ||||||||
|
MobileNet x0.25
|
53.7% | -- | 77.5% | -- | #380 | ||||||||
|
MobileNet x0.5
|
65.8% | -- | 86.5% | -- | #346 | ||||||||
|
MobileNet x0.75
|
69.9% | -- | 89.2% | -- | #303 | ||||||||
|
MobileNet x1.0
|
73.4% | -- | 91.0% | -- | #276 | ||||||||
|
NASNet-A [email protected]
|
74.3% | -- | 91.8% | -- | #253 | ||||||||
|
NASNet-A [email protected]
|
81.9% | -- | 95.8% | -- | #46 | ||||||||
|
PeleeNet
|
68.2% | -- | 88.5% | -- | #323 | ||||||||
|
PNASNet-5-Large
|
82.1% | -- | 95.7% | -- | #49 | ||||||||
|
PolyNet
|
80.9% | 81.3% | 95.5% | 95.8% | #59 | ||||||||
|
PrepResNet-10
|
64.9% | -- | 85.8% | -- | #351 | ||||||||
|
PreResNet-101
|
78.3% | -- | 94.1% | -- | #155 | ||||||||
|
PreResNet-101b
|
79.0% | -- | 94.4% | -- | #113 | ||||||||
|
PreResNet-12
|
66.1% | -- | 86.5% | -- | #343 | ||||||||
|
PreResNet-14
|
67.4% | -- | 87.6% | -- | #326 | ||||||||
|
PreResNet-152
|
79.1% | -- | 94.5% | -- | #113 | ||||||||
|
PreResNet-152b
|
79.9% | -- | 94.8% | -- | #83 | ||||||||
|
PreResNet-16
|
69.5% | -- | 88.9% | -- | #311 | ||||||||
|
PreResNet-18
|
71.6% | -- | 90.3% | -- | #288 | ||||||||
|
PreResNet-200b
|
78.7% | -- | 94.1% | -- | #137 | ||||||||
|
PreResNet-26
|
73.7% | -- | 91.5% | -- | #265 | ||||||||
|
PreResNet-269b
|
79.1% | -- | 94.2% | -- | #110 | ||||||||
|
PreResNet-34
|
75.1% | -- | 92.3% | -- | #238 | ||||||||
|
PreResNet-50
|
77.6% | -- | 93.5% | -- | #190 | ||||||||
|
PreResNet-50b
|
77.5% | -- | 93.4% | -- | #183 | ||||||||
|
PreResNet-BC-26b
|
74.5% | -- | 92.0% | -- | #249 | ||||||||
|
PreResNet-BC-38b
|
77.1% | -- | 93.4% | -- | #194 | ||||||||
|
ProxylessNAS CPU
|
75.3% | -- | 92.4% | -- | #232 | ||||||||
|
ProxylessNAS GPU
|
75.2% | -- | 92.6% | -- | #231 | ||||||||
|
ProxylessNAS Mob-14
|
76.7% | -- | 93.4% | -- | #209 | ||||||||
|
ProxylessNAS Mobile
|
74.6% | -- | 92.2% | -- | #241 | ||||||||
|
PyramidNet-101
(a=360) |
78.0% | -- | 93.8% | -- | #171 | ||||||||
|
ResNet-10
|
65.3% | -- | 85.6% | -- | #352 | ||||||||
|
ResNet-101
|
78.1% | 78.2% | 93.8% | 94.0% | #164 | ||||||||
|
ResNet-101b
|
79.4% | -- | 94.7% | -- | #98 | ||||||||
|
ResNet-12
|
66.4% | -- | 86.7% | -- | #337 | ||||||||
|
ResNet-14
|
67.5% | -- | 87.5% | -- | #327 | ||||||||
|
ResNet-152
|
79.0% | 78.6% |
|
94.5% | 94.3% | #120 | |||||||
|
ResNet-152b
|
80.1% | -- | 95.0% | -- | #76 | ||||||||
|
ResNet-16
|
69.5% | -- | 88.8% | -- | #310 | ||||||||
|
ResNet-18
|
71.5% | 72.1% | 90.2% | -- | 412.9 | #293 | |||||||
|
ResNet-26
|
73.7% | -- | 91.5% | -- | #266 | ||||||||
|
ResNet-34
|
75.2% | -- | 92.2% | -- | #232 | ||||||||
|
ResNet-50
|
77.7% | 77.1% |
|
93.7% | 93.3% |
|
#177 | ||||||
|
ResNet-50b
|
77.6% | -- | 93.6% | -- | #175 | ||||||||
|
ResNet-BC-26b
|
74.9% | -- | 92.0% | -- | #247 | ||||||||
|
ResNet-BC-38b
|
76.3% | -- | 93.0% | -- | #214 | ||||||||
|
ResNeXt-101
(32x4d) |
78.2% | -- | 93.9% | -- | #167 | ||||||||
|
ResNeXt-101
(64x4d) |
79.0% | -- | 94.3% | -- | #132 | ||||||||
|
ResNeXt-14
(16x4d) |
68.1% | -- | 87.5% | -- | #328 | ||||||||
|
ResNeXt-14
(32x2d) |
67.4% | -- | 87.2% | -- | #328 | ||||||||
|
ResNeXt-14
(32x4d) |
69.7% | -- | 88.5% | -- | #307 | ||||||||
|
ResNeXt-26
(32x2d) |
73.4% | -- | 91.1% | -- | #275 | ||||||||
|
ResNeXt-26
(32x4d) |
75.9% | -- | 92.5% | -- | #223 | ||||||||
|
SENet-154
|
81.4% | 82.7% | 95.4% | 96.2% | #59 | ||||||||
|
SENet-16
|
74.4% | -- | 91.8% | -- | #254 | ||||||||
|
SENet-28
|
78.1% | -- | 94.0% | -- | #165 | ||||||||
|
SE-PreResNet-10
|
66.0% | -- | 86.6% | -- | #339 | ||||||||
|
SE-PreResNet-18
|
71.9% | -- | 90.4% | -- | #287 | ||||||||
|
SE-PreResNet-BC-26b
|
76.8% | -- | 93.4% | -- | #198 | ||||||||
|
SE-PreResNet-BC-38b
|
78.4% | -- | 94.2% | -- | #137 | ||||||||
|
SE-ResNet-10
|
66.1% | -- | 86.3% | -- | #348 | ||||||||
|
SE-ResNet-101
|
78.1% | -- | 94.1% | -- | #149 | ||||||||
|
SE-ResNet-152
|
78.5% | -- | 94.2% | -- | #141 | ||||||||
|
SE-ResNet-18
|
71.8% | -- | 90.4% | -- | #283 | ||||||||
|
SE-ResNet-26
|
74.3% | -- | 91.8% | -- | #256 | ||||||||
|
SE-ResNet-50
|
77.5% | -- | 93.6% | -- | #181 | ||||||||
|
SE-ResNet-50b
|
79.2% | -- | 94.6% | -- | #105 | ||||||||
|
SE-ResNet-BC-26b
|
76.4% | -- | 93.0% | -- | #212 | ||||||||
|
SE-ResNet-BC-38b
|
78.4% | -- | 94.1% | -- | #154 | ||||||||
|
SE-ResNeXt-101
(32x4d) |
80.0% | -- | 94.9% | -- | #78 | ||||||||
|
SE-ResNeXt-50
(32x4d) |
79.0% | -- | 94.5% | -- | #119 | ||||||||
|
ShuffleNetV2b x0.5
|
59.7% | -- | 81.8% | -- | #365 | ||||||||
|
ShuffleNetV2b x1.0
|
69.4% | -- | 88.8% | -- | #313 | ||||||||
|
ShuffleNetV2b x1.5
|
72.7% | -- | 90.9% | -- | #277 | ||||||||
|
ShuffleNetV2b x2.0
|
74.4% | -- | 91.7% | -- | #250 | ||||||||
|
ShuffleNetV2 x0.5
|
59.0% | -- | 81.4% | -- | #367 | ||||||||
|
ShuffleNetV2 x1.0
|
68.6% | -- | 88.4% | -- | #320 | ||||||||
|
ShuffleNetV2 x1.5
|
72.5% | -- | 90.6% | -- | #281 | ||||||||
|
ShuffleNetV2 x2.0
|
74.1% | -- | 91.6% | -- | #264 | ||||||||
|
ShuffleNet x0.25
(g=1) |
37.6% | -- | 62.7% | -- | #386 | ||||||||
|
ShuffleNet x0.25
(g=3) |
38.3% | -- | 63.5% | -- | #385 | ||||||||
|
ShuffleNet x0.5
(g=1) |
53.4% | -- | 77.4% | -- | #381 | ||||||||
|
ShuffleNet x0.5
(g=3) |
55.8% | -- | 79.2% | -- | #379 | ||||||||
|
ShuffleNet x0.75
(g=1) |
60.4% | -- | 82.9% | -- | #357 | ||||||||
|
ShuffleNet x0.75
(g=3) |
61.8% | -- | 83.5% | -- | #356 | ||||||||
|
ShuffleNet x1.0
(g=1) |
65.1% | -- | 86.1% | -- | #350 | ||||||||
|
ShuffleNet x1.0
(g=2) |
65.8% | -- | 86.4% | -- | #347 | ||||||||
|
ShuffleNet x1.0
(g=3) |
65.6% | -- | 86.5% | -- | #347 | ||||||||
|
ShuffleNet x1.0
(g=4) |
65.8% | -- | 86.6% | -- | #338 | ||||||||
|
ShuffleNet x1.0
(g=8) |
65.9% | -- | 86.6% | -- | #340 | ||||||||
|
SPNASNet
|
74.1% | -- | 91.8% | -- | 412.2 | #253 | |||||||
|
SqueezeNet v1.0
|
60.7% | -- | 82.3% | -- | #360 | ||||||||
|
SqueezeNet v1.1
|
60.7% | -- | 82.3% | -- | #361 | ||||||||
|
SqueezeResNet v1.0
|
60.2% | -- | 81.9% | -- | #361 | ||||||||
|
SqueezeResNet v1.1
|
59.9% | -- | 81.8% | -- | #364 | ||||||||
|
VGG-11
|
70.1% | 70.4% | 89.6% | 89.6% | 411.2 | #301 | |||||||
|
VGG-13
|
71.2% | 71.3% | 90.2% | 90.1% | 406.1 | #292 | |||||||
|
VGG-16
|
73.0% | 74.4% | 91.3% | 91.9% | 396.4 | #274 | |||||||
|
VGG-19
|
73.8% | 74.5% | 91.6% | 92.0% | 390.5 | #261 | |||||||
|
WRN-50-2
|
77.5% | -- | 93.6% | -- | #185 | ||||||||
|
Xception
|
79.0% | 79.0% | 94.5% | 94.5% | #106 | ||||||||
|
ZFNet
|
60.2% | 64.0% | 82.7% | 85.3% | 428.2 | #362 | |||||||
|
ZFNet-b
|
63.6% | -- | 85.1% | -- | 426.3 | #354 | |||||||
+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
#
17
Update PIP-packages (gluoncv2: 0.0.51, pytorchcv: 0.0.51, chaine…
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