osmr / imgclsmob

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% -- #532
1.0-SqNxt-23
57.5% -- 80.9% -- #529
1.0-SqNxt-23v5
59.2% -- 82.2% -- #523
128-MENet-8x1
(g=4)
57.6% -- 80.4% -- #528
1.5-SqNxt-23
65.1% -- 86.5% -- #498
1.5-SqNxt-23v5
66.2% -- 87.0% -- #486
160-MENet-8x1
(g=8)
56.2% -- 79.2% -- #531
2.0-SqNxt-23
69.4% -- 89.0% -- #462
2.0-SqNxt-23v5
70.4% -- 89.3% -- #451
228-MENet-12x1
(g=3)
65.9% -- 86.8% -- #494
256-MENet-12x1
(g=4)
67.3% -- 87.5% -- #481
348-MENet-12x1
(g=3)
71.8% -- 90.4% -- #438
352-MENet-12x1
(g=8)
68.4% -- 88.0% -- #475
456-MENet-24x1
(g=3)
74.7% -- 92.0% -- #395
AlexNet
59.0% -- 81.8% -- 417.5 #524
AlexNet-b
58.4% -- 81.0% -- 418.1 #526
BAM-ResNet-50
76.9% -- 93.4% -- #324
BN-Inception
74.6% -- 92.3% -- 426.1 #381
BN-VGG-11
71.0% -- 90.4% -- 420.9 #438
BN-VGG-11b
70.4% -- 90.0% -- 415.6 #450
BN-VGG-13
72.2% -- 90.9% -- 406.0 #426
BN-VGG-13b
71.6% -- 90.4% -- 394.5 #440
BN-VGG-16
74.3% -- 92.2% -- 400.7 #382
BN-VGG-16b
72.8% -- 91.3% -- 394.4 #423
BN-VGG-19
75.9% -- 92.9% -- 395.8 #354
BN-VGG-19b
73.9% -- 91.6% -- 385.3 #410
CBAM-ResNet-50
77.6% -- 93.9% -- #299
CondenseNet-74
(C=G=4)
73.8% -- 91.7% -- #408
CondenseNet-74
(C=G=8)
71.1% -- 89.9% -- #446
DarkNet-53
78.3% -- 94.4% -- #265
DARTS
73.3% -- 91.3% -- #420
DenseNet-121
76.5% 76.4% 93.0% 93.3% #353
DenseNet-161
78.1% -- 93.9% -- 386.0 #278
DenseNet-169
77.6% 77.9% 93.7% 94.1% #303
DenseNet-201
78.2% 78.5% 93.9% 94.5% 392.4 #270
DiracNetV2-18
68.5% -- 88.3% -- #472
DiracNetV2-34
71.2% -- 90.1% -- #446
DLA-102
78.0% -- 94.0% -- #283
DLA-169
78.7% -- 94.3% -- #232
DLA-34
74.6% -- 92.1% -- #389
DLA-46-C
65.7% -- 86.8% -- #487
DLA-60
77.0% -- 93.3% -- #335
DLA-X-102
78.5% -- 94.2% -- #250
DLA-X2-102
79.5% -- 94.6% -- #185
DLA-X-46-C
66.7% -- 87.3% -- #483
DLA-X-60
78.2% -- 94.0% -- #267
DLA-X-60-C
69.0% -- 89.1% -- #456
DPN-131
79.5% -- 94.5% -- #199
DPN-68
76.8% -- 93.2% -- #340
DPN-98
79.2% -- 94.5% -- #208
DRN-C-26
75.1% -- 92.5% -- #375
DRN-C-42
77.1% -- 93.4% -- #322
DRN-C-58
78.3% -- 94.0% -- #272
DRN-D-105
79.4% -- 94.5% -- #200
DRN-D-22
74.2% -- 91.8% -- #403
DRN-D-38
76.2% -- 93.1% -- #352
DRN-D-54
78.8% -- 94.1% -- #232
EfficientNet-B0
75.2% 76.3% 92.5% 93.2% #371
EfficientNet-B0b
76.1% -- 93.0% -- #350
EfficientNet-B1
77.0% 78.8% 93.6% 94.4% #309
EfficientNet-B1b
78.4% -- 94.1% -- #257
EfficientNet-B2b
79.7% -- 94.7% -- 368.5 #174
EfficientNet-B3b
81.4% -- 95.6% -- 328.4 #98
EfficientNet-B4b
82.8% -- 96.2% -- 221.8 #67
EfficientNet-B5b
83.6% -- 96.7% -- 115.4 #49
EfficientNet-B6b
84.1% -- 96.9% -- 68.4 #46
EfficientNet-B7b
84.3% -- 96.9% -- 39.5 #35
ESPNetv2 x0.5
57.7% -- 79.8% -- #530
ESPNetv2 x1.0
66.1% -- 86.5% -- #489
ESPNetv2 x1.25
67.9% -- 87.8% -- #476
ESPNetv2 x1.5
69.2% -- 88.7% -- #465
ESPNetv2 x2.0
72.1% -- 90.4% -- #435
FBNet-Cb
75.1% -- 92.4% -- 407.7 #374
FD-MobileNet x0.25
44.1% -- 69.0% -- #539
FD-MobileNet x0.5
56.9% -- 79.8% -- #530
FD-MobileNet x0.75
61.6% -- 83.6% -- #511
FD-MobileNet x1.0
65.8% -- 86.6% -- #497
HarDNet-39DS
72.1% -- 90.4% -- 406.9 #434
HarDNet-68
76.5% -- 93.0% -- 403.0 #346
HarDNet-68DS
74.3% -- 91.9% -- 408.0 #400
HarDNet-85
78.0% -- 93.9% -- 392.2 #282
HRNetV2-W18
76.8% -- 93.4% -- 382.9 #318
HRNetV2-W30
78.2% -- 94.2% -- 377.8 #248
HRNetV2-W32
78.4% -- 94.2% -- 374.2 #253
HRNetV2-W40
78.9% -- 94.5% -- 354.0 #223
HRNetV2-W44
78.9% -- 94.4% -- 333.8 #226
HRNetV2-W48
79.3% -- 94.5% -- 315.6 #201
HRNetV2-W64
79.5% -- 94.7% -- 265.0 #182
HRNet-W18 Small V1
72.3% -- 90.7% -- 399.5 #429
HRNet-W18 Small V2
75.1% -- 92.4% -- 393.6 #376
IBN(b)-ResNet-50
76.4% -- 93.1% -- #344
IBN-DenseNet-121
75.5% -- 92.8% -- #363
IBN-DenseNet-169
76.8% -- 93.5% -- #339
IBN-ResNet-101
78.7% -- 94.4% -- #236
IBN-ResNet-50
77.2% -- 93.6% -- #315
IBN-ResNeXt-101
(32x4d)
79.1% -- 94.6% -- #196
IGCV3 x0.25
46.3% -- 71.3% -- #538
IGCV3 x0.5
60.2% -- 82.7% -- #515
IGCV3 x0.75
68.9% -- 88.6% -- #467
IGCV3 x1.0
72.1% -- 90.8% -- #428
InceptionResNetV2
80.1% -- 95.1% -- #153
InceptionV3
78.9% -- 94.4% -- #231
InceptionV4
79.4% -- 94.7% -- #180
i-RevNet-301
74.0% -- 91.6% -- #409
MixNet-L
78.9% 78.9% 94.2% 94.2% 400.3 #229
MixNet-M
77.1% 77.0% 93.4% 93.3% 404.0 #328
MixNet-S
76.0% 75.8% 92.8% 92.8% 413.1 #355
MnasNet-A1
75.3% 75.2% 92.6% 92.5% 413.1 #367
MnasNet-B1
74.6% -- 92.2% -- 415.4 #387
MobileNetV2b x0.25
51.4% -- 74.7% -- 402.4 #537
MobileNetV2b x0.5
64.0% -- 85.0% -- 406.1 #509
MobileNetV2b x0.75
68.9% -- 88.2% -- 390.5 #473
MobileNetV2b x1.0
71.5% -- 90.2% -- 403.0 #442
MobileNetV2 x0.25
51.7% -- 75.5% -- #536
MobileNetV2 x0.5
64.0% -- 85.1% -- #508
MobileNetV2 x0.75
69.8% -- 89.2% -- #455
MobileNetV2 x1.0
73.0% -- 91.1% -- #420
MobileNetV3 L/224/1.0
75.1% -- 92.2% -- #374
MobileNet x0.25
53.7% -- 77.5% -- #534
MobileNet x0.5
65.8% -- 86.5% -- #495
MobileNet x0.75
69.9% -- 89.2% -- #454
MobileNet x1.0
73.4% -- 91.0% -- #416
74.3% -- 91.8% -- #399
81.9% -- 95.8% -- #84
PeleeNet
68.2% -- 88.5% -- #469
PNASNet-5-Large
82.1% -- 95.7% -- #88
PolyNet
80.9% 81.3% 95.5% 95.8% #105
PrepResNet-10
64.9% -- 85.8% -- #505
PreResNet-101
78.3% -- 94.1% -- #264
PreResNet-101b
79.0% -- 94.4% -- #222
PreResNet-12
66.1% -- 86.5% -- #487
PreResNet-14
67.4% -- 87.6% -- #481
PreResNet-152
79.1% -- 94.5% -- #215
PreResNet-152b
79.9% -- 94.8% -- #160
PreResNet-16
69.5% -- 88.9% -- #461
PreResNet-18
71.6% -- 90.3% -- #442
PreResNet-18 x0.25
59.9% -- 81.9% -- 426.0 #520
PreResNet-18 x0.5
66.0% -- 86.6% -- 433.1 #493
PreResNet-18 x0.75
69.8% -- 88.9% -- 429.0 #459
PreResNet-200b
78.7% -- 94.1% -- #241
PreResNet-26
73.7% -- 91.5% -- #414
PreResNet-269b
79.1% -- 94.2% -- #252
PreResNet-34
75.1% -- 92.3% -- #379
PreResNet-50
77.6% -- 93.5% -- #301
PreResNet-50b
77.5% -- 93.4% -- #307
PreResNet=BC-14b
68.7% -- 88.2% -- 427.5 #470
PreResNet-BC-26b
74.5% -- 92.0% -- #397
PreResNet-BC-38b
77.1% -- 93.4% -- #320
ProxylessNAS CPU
75.3% -- 92.4% -- #373
ProxylessNAS GPU
75.2% -- 92.6% -- #364
ProxylessNAS Mob-14
76.7% -- 93.4% -- #329
ProxylessNAS Mobile
74.6% -- 92.2% -- #393
PyramidNet-101
(a=360)
78.0% -- 93.8% -- #289
ResNet-10
65.3% -- 85.6% -- #506
ResNet-101
78.1% 78.2% 93.8% 94.0% #277
ResNet-101b
79.4% -- 94.7% -- #181
ResNet-12
66.4% -- 86.7% -- #489
ResNet-14
67.5% -- 87.5% -- #479
ResNet-152
79.0% 78.6%
94.5% 94.3% #220
ResNet-152b
80.1% -- 95.0% -- #151
ResNet-16
69.5% -- 88.8% -- #460
ResNet-18
71.5% 72.1% 90.2% -- 412.9 #443
ResNet-18 x0.25
60.4% -- 82.2% -- 438.7 #516
ResNet-18 x0.5
66.2% -- 86.7% -- 438.2 #485
ResNet-18 x0.75
69.6% -- 88.9% -- 435.0 #459
ResNet-26
73.7% -- 91.5% -- #413
ResNet-34
75.2% -- 92.2% -- #383
ResNet-50
77.7% 77.1%
93.7% 93.3%
#296
ResNet-50b
77.6% -- 93.6% -- #300
ResNet-BC-14b
69.3% -- 88.5% -- 428.6 #464
ResNet-BC-26b
74.9% -- 92.0% -- #383
ResNet-BC-38b
76.3% -- 93.0% -- #349
ResNeXt-101
(32x4d)
80.0% -- 94.8% -- #170
ResNeXt-101
(64x4d)
80.4% -- 94.9% -- #150
ResNeXt-14
(16x4d)
68.1% -- 87.5% -- #475
ResNeXt-14
(32x2d)
67.4% -- 87.2% -- #483
ResNeXt-14
(32x4d)
69.7% -- 88.5% -- #467
ResNeXt-26
(32x2d)
73.4% -- 91.1% -- #423
ResNeXt-26
(32x4d)
75.9% -- 92.5% -- #366
ResNeXt-50
(32x4d)
79.2% -- 94.4% -- #205
SelecSLS-42b
77.1% -- 93.4% -- 405.9 #325
SelecSLS-60
77.9% -- 93.9% -- 414.8 #291
SelecSLS-60b
78.4% -- 94.2% -- 409.4 #262
SENet-154
81.4% 82.7% 95.4% 96.2% #92
SENet-16
74.4% -- 91.8% -- #397
SENet-28
78.1% -- 94.0% -- #270
SE-PreResNet-10
66.0% -- 86.6% -- #492
SE-PreResNet-18
71.9% -- 90.4% -- #436
SE-PreResNet-BC-26b
76.8% -- 93.4% -- #326
SE-PreResNet-BC-38b
78.4% -- 94.2% -- #260
SE-ResNet-10
66.1% -- 86.3% -- #488
SE-ResNet-101
78.1% -- 94.1% -- #263
SE-ResNet-101b
80.3% -- 95.1% -- 394.9 #132
SE-ResNet-152
78.5% -- 94.2% -- #248
SE-ResNet-18
71.8% -- 90.4% -- #437
SE-ResNet-26
74.3% -- 91.8% -- #407
SE-ResNet-50
78.8% -- 94.2% -- 369.0 #240
SE-ResNet-50b
79.2% -- 94.6% -- #194
SE-ResNet-BC-26b
76.4% -- 93.0% -- #347
SE-ResNet-BC-38b
78.4% -- 94.1% -- #259
SE-ResNeXt-101
(32x4d)
80.8% -- 95.2% -- 369.8 #114
SE-ResNeXt-101
(64x4d)
80.7% -- 95.2% -- 263.6 #126
SE-ResNeXt-50
(32x4d)
79.7% -- 94.8% -- 389.8 #172
ShuffleNetV2b x0.5
59.7% -- 81.8% -- #522
ShuffleNetV2b x1.0
69.4% -- 88.8% -- #463
ShuffleNetV2b x1.5
72.7% -- 90.9% -- #424
ShuffleNetV2b x2.0
74.4% -- 91.7% -- #396
ShuffleNetV2 x0.5
59.0% -- 81.4% -- #525
ShuffleNetV2 x1.0
68.6% -- 88.4% -- #471
ShuffleNetV2 x1.5
72.5% -- 90.6% -- #433
ShuffleNetV2 x2.0
74.1% -- 91.6% -- #413
ShuffleNet x0.25
(g=1)
37.6% -- 62.7% -- #541
ShuffleNet x0.25
(g=3)
38.3% -- 63.5% -- #540
ShuffleNet x0.5
(g=1)
53.4% -- 77.4% -- #535
ShuffleNet x0.5
(g=3)
55.8% -- 79.2% -- #533
ShuffleNet x0.75
(g=1)
60.4% -- 82.9% -- #513
ShuffleNet x0.75
(g=3)
61.8% -- 83.5% -- #512
ShuffleNet x1.0
(g=1)
65.1% -- 86.1% -- #504
ShuffleNet x1.0
(g=2)
65.8% -- 86.4% -- #500
ShuffleNet x1.0
(g=3)
65.6% -- 86.5% -- #497
ShuffleNet x1.0
(g=4)
65.8% -- 86.6% -- #490
ShuffleNet x1.0
(g=8)
65.9% -- 86.6% -- #494
SPNASNet
74.1% -- 91.8% -- 412.2 #406
SqueezeNet v1.0
60.7% -- 82.3% -- #513
SqueezeNet v1.1
60.7% -- 82.3% -- #514
SqueezeResNet v1.0
60.2% -- 81.9% -- #520
SqueezeResNet v1.1
59.9% -- 81.8% -- #521
VGG-11
70.1% 70.4% 89.6% 89.6% 411.2 #451
VGG-13
71.2% 71.3% 90.2% 90.1% 406.1 #445
VGG-16
73.0% 74.4% 91.3% 91.9% 396.4 #417
VGG-19
74.3% 74.5% 92.1% 92.0% 338.8 #391
VoVNet-39
76.8% -- 93.4% -- 410.3 #319
VoVNet-57
77.7% -- 93.7% -- 402.2 #295
WRN-50-2
77.5% -- 93.6% -- #307
Xception
79.0% 79.0% 94.5% 94.5% #219
ZFNet
60.2% 64.0% 82.7% 85.3% 428.2 #514
ZFNet-b
63.6% -- 85.1% -- 426.3 #509
See Full Build Details +get badge code
[![SotaBench](https://img.shields.io/endpoint.svg?url=https://sotabench.com/api/v0/badge/gh/osmr/imgclsmob)](https://sotabench.com/user/osemery/repos/osmr/imgclsmob)

How the Repository is Evaluated

The full sotabench.py file - source
from torchbench.image_classification import ImageNet
from pytorch.pytorchcv.models.model_store import _model_sha1
from pytorch.pytorchcv.model_provider import get_model as ptcv_get_model
import torchvision.transforms as transforms
import torch
import math
from sys import version_info
# import os


for model_name, model_metainfo in (_model_sha1.items() if version_info[0] >= 3 else _model_sha1.iteritems()):
    net = ptcv_get_model(model_name, pretrained=True)
    error, checksum, repo_release_tag, caption, paper, ds, img_size, scale, batch, rem = model_metainfo
    if (ds != "in1k") or (img_size == 0) or ((len(rem) > 0) and (rem[-1] == "*")):
        continue
    paper_model_name = caption
    paper_arxiv_id = paper
    input_image_size = img_size
    resize_inv_factor = scale
    batch_size = batch
    model_description = "pytorch" + (rem if rem == "" else ", " + rem)
    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=paper_model_name,
        paper_arxiv_id=paper_arxiv_id,
        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
Some experiments with TFL models
osmr   5070326  ·  yesterday
unknown
Prepare links to some MobileNet(B) pretrained models
osmr   f596708  ·  yesterday
unknown
Prepare links to MobileNet(B) x1.0 pretrained models
osmr   2d9327d  ·  2 days ago
unknown
After upd ResNeSt release
osmr   1c44102  ·  3 days ago
0h:10m:33s
First test of ResNeSt release
osmr   9644e02  ·  3 days ago
0h:11m:42s
Add statistics for ResNeSt
osmr   3795882  ·  3 days ago
0h:12m:28s
Upd presets for ResNeSt
osmr   8d6ebff  ·  4 days ago
0h:11m:14s
Test ResNeSt, 2
osmr   2bd0e14  ·  4 days ago
0h:10m:50s
Test ResNeSt models (problems with dropout)
osmr   1430330  ·  4 days ago
0h:10m:52s
Add scale for large ResNeSt
osmr   c2e678d  ·  4 days ago
0h:09m:12s
Work on ResNeSt, 12 (fix some bugs)
osmr   bbb709d  ·  4 days ago
0h:11m:02s
Work on ResNeSt, 11 (fix some bugs)
osmr   d417fda  ·  4 days ago
0h:07m:32s
Work on ResNeSt, 9 (debuging)
osmr   879ae4c  ·  4 days ago
0h:10m:38s
Work on ResNeSt, 8 (add links to ResNeStA models)
osmr   be10225  ·  4 days ago
0h:04m:21s
Work on ResNeSt, 7 (debuging ResNeStA models)
osmr   4151232  ·  4 days ago
0h:10m:51s
Work on ResNeSt, 6 (add ResNeStA models in GL)
osmr   ae19d89  ·  5 days ago
0h:09m:29s
Work on ResNeSt, 5 (add saconv3x3_block in GL)
osmr   03b5d31  ·  5 days ago
0h:07m:41s
Work on ResNeSt, 4 (add SABlock in GL)
osmr   8d43815  ·  5 days ago
0h:09m:21s
Work on ResNeSt, 3
osmr   9820c89  ·  5 days ago
0h:09m:07s
Work on ResNeSt, 2
osmr   10dc85b  ·  5 days ago
0h:13m:03s
Work on ResNeSt, 1
osmr   68090c1  ·  5 days ago
0h:11m:42s
Start to analyze ResNeSt
osmr   1d95173  ·  6 days ago
0h:10m:07s
Some cosmetic changes in TF2-Resnet
osmr   d1fc6de  ·  6 days ago
0h:06m:38s
Some cosmetic changes
osmr   36aa6b3  ·  6 days ago
0h:09m:42s
Add links to MobileNet(B) x0.5 pretrained models
osmr   de0aa2a  ·  Oct 16 2020
0h:06m:18s
Upd TF2-MobileNet for SimpleSequential
osmr   d293cb5  ·  Oct 16 2020
0h:09m:25s
Add extra MobileNet(B) models
osmr   a7697b8  ·  Oct 16 2020
0h:08m:59s
Upd links for ResNet(A)-18 models
osmr 58e1dcf    b6a103e  ·  Oct 15 2020
0h:11m:14s
Add x0.5 MobileNet(B)-224 model
osmr 0a14c96    46cdf20  ·  Oct 08 2020
0h:11m:56s
Add preset for ResNet(A)-18
osmr   92f642d  ·  Oct 06 2020
0h:09m:19s
Upd a comment for VisemeNet (GL), (fix a link)
osmr   aa6c618  ·  Jul 23 2020
0h:04m:07s
Upd a comment for VisemeNet (GL)
osmr   329944f  ·  Jul 23 2020
0h:09m:52s
Some experiments on VisemeNet (GL)
osmr   4755408  ·  Jul 23 2020
0h:09m:53s
Fix some broken links
osmr   5bb38b3  ·  Jul 22 2020
0h:03m:46s
Add NvpAttExp model
osmr   f1c2d98  ·  Jul 22 2020
0h:10m:59s
Update PIP-packages (gluoncv2: 0.0.58)
osmr   a5c5bf8  ·  Jul 03 2020
0h:04m:38s
Fix a bug in VGG (fix #70)
osmr   ce09a65  ·  Jul 03 2020
0h:09m:32s
Some changes in Res2Net (GL)
osmr   2c1ece7  ·  Jun 16 2020
0h:08m:16s
Merge pull request #68 from RuRo/fix_res2net_preactiv Fix res2n…
osmr aafbed6    21bc972  ·  Jun 16 2020
0h:10m:46s
Update model-common files
osmr   306fa33  ·  Jun 09 2020
0h:09m:41s
Update SINet for TF2
osmr   e8d8e31  ·  Jun 09 2020
0h:10m:07s
Update info about BiSeNet model
osmr   a550301  ·  May 26 2020
0h:04m:05s
Update info about SE-PreResNet-50b model
osmr   1ceb800  ·  May 26 2020
0h:10m:25s
Fix a bug in eval-scripts
osmr   8b95d08  ·  May 26 2020
0h:09m:35s
Modify TF2 BiSeNet model for better TFL conversion
osmr   81f68d2  ·  May 25 2020
0h:09m:57s
Add BiSeNet model
osmr   bdeb707  ·  May 25 2020
0h:10m:02s
Update scripts for seg net training, 2
osmr   cf01fc2  ·  Apr 14 2020
0h:59m:02s
Work on training seg models (gl)
osmr   d0d1c49  ·  Apr 14 2020
1h:01m:20s
Refactoring MobNet (separate FD-MobNet)
osmr   956b4eb  ·  Apr 09 2020
1h:01m:37s
Update PIP-packages (gluoncv2: 0.0.57, pytorchcv: 0.0.58, chaine…
osmr   63859ce  ·  Apr 09 2020
0h:50m:39s
Comment all temporal model links
osmr   52ed229  ·  Apr 09 2020
1h:01m:04s
Add VOCA model
osmr   474c1bc  ·  Apr 08 2020
1h:02m:27s
Work on LFFD, 3
osmr   4c25277  ·  Apr 02 2020
1h:01m:31s
Work on LFFD, 2
osmr   bf0c7bd  ·  Mar 30 2020
1h:01m:53s
Study LFFD
osmr   bdb72a0  ·  Mar 27 2020
1h:00m:33s
Update info about SE-ResNet-101b model
osmr   5f49747  ·  Mar 20 2020
1h:00m:55s
Study CenterNet, 4
osmr   3a7fa91  ·  Mar 19 2020
0h:58m:23s
Study CenterNet, 3
osmr   33d7932  ·  Mar 18 2020
0h:58m:48s
Study CenterNet, 2
osmr   aaf78fd  ·  Mar 18 2020
0h:56m:56s
Study CenterNet
osmr   1a66d72  ·  Mar 17 2020
0h:58m:42s
Testing G-RMI Pose net, 2
osmr   3366eb8  ·  Mar 16 2020
0h:59m:33s
Testing G-RMI Pose net
osmr   e2082de  ·  Mar 12 2020
0h:56m:52s
Update info about IBPPose model
osmr   97d7787  ·  Mar 11 2020
0h:59m:25s
Work on IBPPose, 5
osmr   e0ce2cf  ·  Mar 10 2020
0h:09m:46s
Update info about LwOpenPose models
osmr   fc2ead1  ·  Mar 03 2020
0h:56m:45s
Fix a PEP warning for Python2
osmr   ad48035  ·  Mar 03 2020
unknown
Work on IBPPose
osmr   046811a  ·  Mar 05 2020
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Work on IBPPose, 2
osmr   c76f274  ·  Mar 06 2020
unknown
Work on IBPPose, 3
osmr   754882a  ·  Mar 06 2020
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Work on IBPPose, 4
osmr   41c20a1  ·  Mar 07 2020
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Work on LwOpenPose, 4
osmr   a92c93d  ·  Mar 02 2020
0h:51m:20s
Work on LwOpenPose, 3
osmr   2650a9a  ·  Mar 02 2020
0h:58m:07s
Work on LwOpenPose, 2
osmr   3874c0a  ·  Mar 01 2020
0h:57m:42s
Work on LwOpenPose
osmr   f5b056d  ·  Feb 29 2020
0h:58m:24s
Start working on LwOpenPose
osmr   a7e71f6  ·  Feb 28 2020
0h:58m:51s
Fix a PEP warning for Python2, 2
osmr   a1f1f52  ·  Feb 22 2020
0h:48m:45s
Fix PEP warning for Python2
osmr   b901956  ·  Feb 22 2020
unknown
1) Fix a bug in the previous commit, 2) Working on PRNet
osmr   6587578  ·  Feb 22 2020
0h:55m:44s
Update info about ICNet models
osmr   100370e  ·  Feb 21 2020
1h:01m:45s
Working on ICNet, 2
osmr   460dc07  ·  Feb 21 2020
0h:56m:39s
Working on ICNet
osmr   3544837  ·  Feb 20 2020
0h:58m:27s
Update info about SimplePose(Mobile) models
osmr   4c1ed04  ·  Feb 19 2020
0h:49m:33s
Update info about SimplePose models
osmr   b130280  ·  Feb 19 2020
unknown
Update info about AlphaPose models
osmr   9c98c51  ·  Feb 19 2020
unknown
Update info about MobileNetV2b models
osmr   ed026eb  ·  Feb 19 2020
1h:07m:27s
Working on COCO dataset, 7
osmr   553f777  ·  Feb 18 2020
0h:54m:16s
Working on COCO dataset, 6
osmr   d137cd2  ·  Feb 16 2020
0h:59m:00s
Working on COCO dataset, 5
osmr   2593c5c  ·  Feb 15 2020
0h:57m:28s
Working on COCO dataset, 4
osmr   bb5532e  ·  Feb 14 2020
0h:55m:58s
Working on COCO dataset, 3
osmr   263934a  ·  Feb 14 2020
0h:54m:59s
Working on COCO dataset, 2
osmr   1fffda8  ·  Feb 13 2020
0h:56m:32s
Working on COCO dataset
osmr   b7be479  ·  Feb 11 2020
0h:56m:28s
Update info about ResNet(A) models
osmr   7509b95  ·  Feb 09 2020
0h:55m:23s
Work on ResNet-A
osmr   4867ad5  ·  Feb 08 2020
0h:53m:04s
Update PIP-package (pytorchcv: 0.0.57), fix #52
osmr   a092c00  ·  Feb 07 2020
0h:57m:51s
Fix TF2 seg model loading bug
osmr   a7bab29  ·  Feb 07 2020
0h:47m:47s
Fix PEP warning
osmr   40dc20f  ·  Feb 07 2020
0h:47m:52s
Update info about TF2 seg models for COCO
osmr   74f8ae0  ·  Feb 07 2020
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Update info about TF2 models for ADE20K
osmr   25a4d50  ·  Feb 07 2020
0h:54m:59s
Update info about PSPNet/TF2 model for Cityscapes
osmr   2089cd9  ·  Feb 07 2020
0h:48m:28s
Update info about TF2 models for VOC2012
osmr   698ad3f  ·  Feb 07 2020
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Update info about ResNet(D) models for TF2
osmr   c5c08cc  ·  Feb 07 2020
0h:56m:08s
Work on seg models for TF2
osmr   8fd3496  ·  Feb 06 2020
0h:54m:22s
Update info about CUB models for TF2
osmr   7707460  ·  Feb 06 2020
0h:54m:51s
Update info about DenseNet models for TF2/CF/SVHN
osmr   2bd40d6  ·  Feb 04 2020
0h:49m:55s
Update info about PyramidNet models for TF2/CF/SVHN
osmr   4466473  ·  Feb 04 2020
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Update info about SE-PreResNet models for TF2/CF/SVHN
osmr   6cf816a  ·  Feb 04 2020
unknown
Update info about SE-ResNet models for TF2/CF/SVHN
osmr   933b94c  ·  Feb 04 2020
0h:58m:02s
Update info about SE-ResNet-50 model
osmr   f280183  ·  Feb 03 2020
0h:52m:28s
Update info about ResNeXt models for TF2/CF/SVHN
osmr   04287f4  ·  Feb 03 2020
0h:18m:49s
Update info about PreResNet models for TF2/CF/SVHN
osmr   9cd2847  ·  Feb 03 2020
0h:28m:03s
Fix a mistake in readme
osmr   ccf5094  ·  Feb 02 2020
0h:55m:35s
Fix links after testing
osmr   ec62412  ·  Feb 01 2020
0h:54m:02s
Update info about ResNet models for TF2/CF/SVHN
osmr   9f6ec71  ·  Feb 01 2020
0h:49m:44s
Update info about SINet model, 2
osmr   5613523  ·  Feb 01 2020
unknown
Refactoring SINet
osmr   f11f809  ·  Feb 01 2020
0h:57m:47s
Update Readme
osmr   9e21176  ·  Jan 30 2020
0h:57m:21s
Working on SINet, 3
osmr   491a274  ·  Jan 30 2020
unknown
Update info about SINet model
osmr   fd4fdec  ·  Jan 30 2020
unknown
Working on SINet, 2
osmr   651da9e  ·  Jan 30 2020
0h:59m:01s
Working on SINet
osmr   f913454  ·  Jan 29 2020
0h:54m:38s
Working on TF2 for SVHN
osmr   6a9718b  ·  Jan 28 2020
0h:56m:57s
Working on TF2 for CF10
osmr   1812673  ·  Jan 27 2020
0h:57m:02s
Update PIP-packages (gluoncv2: 0.0.55, pytorchcv: 0.0.55, chaine…
osmr   f928f6c  ·  Jan 17 2020
0h:48m:10s
Remove unused script
osmr   99370ed  ·  Jan 17 2020
unknown
Update info about HarDNet models
osmr   ea3df12  ·  Jan 17 2020
1h:05m:28s
Update PIP-packages (gluoncv2: 0.0.54, pytorchcv: 0.0.54, chaine…
osmr   ba82c6a  ·  Jan 16 2020
0h:48m:44s
Fix some mistakes in models
osmr   ea65d92  ·  Jan 16 2020
1h:07m:39s
Fix some mistakes in README
osmr   3a44d79  ·  Jan 15 2020
0h:04m:25s
Update info about EffiNet-Edge models
osmr   8b58548  ·  Jan 15 2020
0h:08m:55s
Update info about EffiNet-AP models
osmr   26debd8  ·  Jan 15 2020
0h:05m:20s
Update info about DenseNet-161 models
osmr   39b70a0  ·  Jan 15 2020
0h:08m:55s
Add EffiNet-Edge model
osmr   c90a031  ·  Jan 14 2020
0h:10m:23s
Update info about VoVNet models
osmr   1192791  ·  Jan 13 2020
0h:04m:19s
Add VoVNet model
osmr   3197ca9  ·  Jan 13 2020
0h:08m:42s
Update PIP-packages (tf2cv: 0.0.5)
osmr   2de308d  ·  Jan 12 2020
0h:08m:38s
Fix some PEP8 warnings, 4
osmr   59770a7  ·  Jan 12 2020
0h:03m:07s
Fix some PEP8 warnings, 3
osmr   0a75716  ·  Jan 12 2020
unknown
Fix some PEP8 warnings, 2
osmr   572fe63  ·  Jan 12 2020
0h:04m:49s
Fix some PEP8 warnings
osmr   6a474c6  ·  Jan 12 2020
0h:07m:46s
Update info about SelecSLS models
osmr   83d92dc  ·  Jan 12 2020
0h:08m:15s
Prepare some TF models for publishing, 3
osmr   c03fa67  ·  Jan 11 2020
0h:08m:35s
1) Add testing all models for PT & TF2; 2) Prepare some TF model…
osmr   3c35a25  ·  Jan 10 2020
0h:09m:16s
Experiments with sotabench, 3
osmr   f390668  ·  Jan 01 2020
0h:44m:50s
Experiments with sotabench, 2
osmr   2f36bc6  ·  Jan 01 2020
1h:00m:50s
Experiments with sotabench
osmr   06382bb  ·  Jan 01 2020
0h:07m:23s
Update README
osmr   e9c4ebd  ·  Dec 31 2019
0h:37m:10s
Ready to publish the fourth pack of the TF2-models
osmr   9926b32  ·  Dec 31 2019
unknown
1) Work on TF2-models, 2) Prepare some TF models for publishing,…
osmr   c427bf9  ·  Dec 31 2019
0h:43m:40s
Work on TF2-models
osmr   f32142b  ·  Dec 30 2019
0h:42m:22s
Work on DLA & DPN for TF2
osmr   d0b0560  ·  Dec 29 2019
0h:40m:53s
Work on FBNet for TF2
osmr   9eccd0b  ·  Dec 29 2019
0h:36m:11s
Update PIP-packages (tf2cv: 0.0.4)
osmr   103ba89  ·  Dec 29 2019
unknown
Work on IBN-nets for TF2
osmr   9ec8f93  ·  Dec 29 2019
0h:42m:02s
Update info about DRN models for TF2
osmr   0cc0c7f  ·  Dec 29 2019
0h:43m:16s
Add some nets to TF2, 5
osmr   9d8ff60  ·  Dec 28 2019
0h:40m:19s
Merge
osmr f28bf12    9761d05  ·  Dec 28 2019
0h:36m:53s
Add some nets to TF2, 4
osmr   c114b40  ·  Dec 28 2019
0h:42m:08s
Add some nets to TF2, 3
osmr   75c4fbc  ·  Dec 28 2019
0h:42m:07s
Update info about some TF2 models, 2
osmr   b9ee37c  ·  Dec 27 2019
0h:42m:07s
Add some nets to TF2, 2
osmr   08ad9cc  ·  Dec 26 2019
0h:42m:07s
Add some nets to TF2
osmr   2b976af  ·  Dec 25 2019
0h:42m:38s
Remove unused scripts
osmr   ec626d3  ·  Dec 23 2019
0h:36m:58s
Work on GhostNet
osmr   1f4ac77  ·  Dec 23 2019
0h:45m:01s
Some tests of GhostNet
osmr   e0b8404  ·  Dec 23 2019
0h:44m:40s
Fix some bugs in readmes
osmr   0688051  ·  Dec 14 2019
0h:44m:47s
Update PIP-packages (tf2cv: 0.0.3)
osmr   8959784  ·  Dec 13 2019
0h:37m:17s
Fix some bugs in TF2 models
osmr   5859d9c  ·  Dec 13 2019
0h:46m:11s
Update TF2 readme
osmr   667d01d  ·  Dec 13 2019
0h:37m:24s
Update PIP-packages (tf2cv: 0.0.2)
osmr   144e877  ·  Dec 13 2019
unknown
Fix a bug with TF2 models loading
osmr   564b131  ·  Dec 13 2019
0h:44m:03s
Update info for TF2 models
osmr   47e2956  ·  Dec 12 2019
0h:37m:22s
Update README for TF 2.0, 4
osmr   8be4bfc  ·  Dec 12 2019
0h:45m:29s
Update README for TF 2.0, 3
osmr   397c10a  ·  Dec 11 2019
0h:46m:50s
Update README for TF 2.0, 2
osmr   7ad99af  ·  Dec 10 2019
0h:39m:50s
Update README for TF 2.0
osmr   e8be354  ·  Dec 10 2019
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Update PIP-packages (tf2cv: 0.0.1)
osmr   7c47c17  ·  Dec 10 2019
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Work on TF2, 3
osmr   05b4c50  ·  Dec 10 2019
0h:43m:55s
Work on TF2, 2
osmr   2980953  ·  Dec 09 2019
0h:42m:43s
Work on TF2
osmr   0c30e37  ·  Dec 08 2019
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Update PIP-packages (gluoncv2: 0.0.53, pytorchcv: 0.0.53, chaine…
osmr   72e817a  ·  Dec 05 2019
unknown
Remove unused script
osmr   a26629a  ·  Dec 04 2019
unknown
Update info about HRNet models
osmr   e93203e  ·  Dec 04 2019
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1) Working on HRNet, 2) Some experiments with TF2
osmr   93610a1  ·  Dec 03 2019
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Experiments with HRNet, 2
osmr   ff7423d  ·  Dec 03 2019
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Experiments with HRNet
osmr   71920cd  ·  Dec 01 2019
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Update info about VGG-19 model
osmr   8d635f0  ·  Nov 29 2019
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Working on TF2, 3
osmr   a2b859e  ·  Nov 26 2019
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Remove the previous version of MnasNet architecture
osmr   719fe9c  ·  Nov 24 2019
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Update PIP-packages (gluoncv2: 0.0.52, pytorchcv: 0.0.52, chaine…
osmr   df5b285  ·  Nov 24 2019
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Update info about MnasNet models
osmr   1005d0d  ·  Nov 24 2019
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Working on MnasNet for TF1
osmr   b856755  (+4 commits )  ·  Nov 23 2019
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Update info about SE-ResNeXt-50/101 models
osmr   b6a154f  ·  Nov 20 2019
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Working on ResNeXt models
osmr   dbb02e9  ·  Nov 20 2019
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Working on TF2, 2
osmr   e73091e  ·  Nov 20 2019
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Working on TF2
osmr   b665d52  ·  Nov 18 2019
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Update PIP-packages (kerascv: 0.0.38)
osmr   f8e7fbb  ·  Nov 16 2019
unknown
Fix a bug in EffNet for KE (fix #40)
osmr   92a0c62  ·  Nov 16 2019
unknown
Prep for workin on MnasNet
osmr   fb220bf  ·  Nov 10 2019
unknown
Update PIP-packages (gluoncv2: 0.0.51, pytorchcv: 0.0.51, chaine…
osmr   458c23b  ·  Nov 10 2019
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Cosmetic refactoring
osmr   abdece4  ·  Nov 10 2019
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Update info about SPNASNet model
osmr   2a0143a  ·  Nov 06 2019
unknown