Each model is train for 200 epoch.
| IN-linear | IN-finetune | coco-bbox | coco-segm | cityscapes | link | |
|---|---|---|---|---|---|---|
| mocov2 r50 | 67.36 | 77.07 | 38.68 | 33.88 | 77.88 | link |
| +fast-moco | 70.83 | 77.16 | 39.30 | 34.38 | 77.94 | link |
| +cutmix | 71.32 | 77.15 | 39.41 | 34.47 | 78.63 | link |
| +mixup | 70.42 | 77.28 | 39.46 | 34.56 | 78.54 | link |
| +dense | 68.79 | 77.28 | 40.00 | 34.81 | 78.69 | link |
Notes:
IN-linear: linear evaluation on imagenet.
IN-finetune: finetune on imagenet.
coco-bbox: object detection on coco.
coco-segm: instance segmentation on coco.
cityscapes: semantic segmentation on cityscapes.
- ubuntu 18.04
- 32 nvidia Tesla T4 gpu, driver 450.80.02
- cuda 11.3
- cudnn 8
- miniconda python 3.8.8
- pytorch 1.12.0
Each experiment is done 4 times, and above result in the table is the mean of the 4 results.
mocov2:
linear:
Acc@1 67.416 Acc@5 87.872
Acc@1 67.312 Acc@5 87.886
Acc@1 67.320 Acc@5 87.812
Acc@1 67.404 Acc@5 87.866
finetune:
Acc@1 77.252 Acc@5 93.598
Acc@1 76.902 Acc@5 93.478
Acc@1 77.028 Acc@5 93.550
Acc@1 77.114 Acc@5 93.582
coco:
bbox: 38.9088,58.6155,42.1195,22.5249,43.4853,53.2623
segm: 34.1413,55.4126,36.3194,15.2867,37.2440,51.9860
bbox: 38.1508,57.6392,41.1611,20.7222,42.8992,51.8489
segm: 33.4272,54.5981,35.3582,14.3102,36.7986,50.7192
bbox: 38.7340,58.1209,42.1626,22.4818,43.5662,52.4255
segm: 33.9001,54.8911,36.1609,15.3182,37.3886,50.5798
bbox: 38.9785,58.5592,42.1268,22.3429,43.7718,52.9737
segm: 34.0710,55.2887,36.2458,15.4479,37.4157,50.8065
deeplab:
78.2688,58.6464,90.2149,77.6179
77.7682,57.8095,90.2813,77.4702
78.1918,58.4643,90.2833,77.6382
77.3166,58.1030,90.2396,77.6255
+fast-moco:
linear:
Acc@1 70.778 Acc@5 89.818
Acc@1 70.858 Acc@5 89.918
Acc@1 70.868 Acc@5 89.872
Acc@1 70.854 Acc@5 89.946
finetune:
Acc@1 77.244 Acc@5 93.468
Acc@1 77.214 Acc@5 93.490
Acc@1 77.122 Acc@5 93.524
Acc@1 77.096 Acc@5 93.560
coco:
bbox: 38.8650,58.7779,41.7332,22.4263,43.9251,51.6782
segm: 33.9814,55.3545,35.9995,15.5256,37.7407,50.4110
bbox: 39.6814,59.4448,42.9916,22.3039,44.6076,53.6434
segm: 34.6912,56.1206,36.8248,15.7709,38.3276,51.9963
bbox: 39.4916,59.3736,42.8254,23.1930,44.4368,52.5870
segm: 34.5428,56.0413,36.6260,15.7507,38.4682,51.1193
bbox: 39.1963,59.0715,42.3853,22.1433,44.3828,52.2242
segm: 34.3486,55.7250,36.5795,15.4824,38.0844,50.9105
deeplab:
78.1100,59.0041,90.3268,78.1409
78.2087,59.1489,90.3703,78.0988
77.5934,58.0122,90.3006,77.9366
77.8860,58.6174,90.3806,78.2742
+cutmix:
linear:
Acc@1 71.328 Acc@5 90.156
Acc@1 71.304 Acc@5 90.140
Acc@1 71.420 Acc@5 90.122
Acc@1 71.244 Acc@5 90.138
finetune:
Acc@1 77.144 Acc@5 93.610
Acc@1 77.012 Acc@5 93.440
Acc@1 77.284 Acc@5 93.570
Acc@1 77.208 Acc@5 93.564
coco:
bbox: 39.1084,59.1479,42.2791,22.4279,44.4237,53.2122
segm: 34.2483,55.8341,36.5099,15.1468,38.1949,51.5365
bbox: 39.5533,59.3614,42.9080,22.7960,44.7712,53.9648
segm: 34.5953,55.9939,36.8683,15.2826,38.3724,52.1124
bbox: 39.4069,59.3092,42.6750,23.1086,44.6547,53.2479
segm: 34.5314,55.9698,36.7320,16.6169,38.6138,51.2430
bbox: 39.5974,59.5217,42.5368,23.4857,45.2232,53.7143
segm: 34.5555,56.2276,36.6526,16.6227,38.6538,52.0167
deeplab:
78.6545,58.8794,90.4519,78.2854
78.5601,59.4348,90.4387,78.0768
78.3252,59.1720,90.4460,78.2306
78.9993,59.0939,90.5852,78.4380
+mixup:
linear:
Acc@1 70.426 Acc@5 89.920
Acc@1 70.502 Acc@5 89.952
Acc@1 70.458 Acc@5 89.982
Acc@1 70.292 Acc@5 89.952
finetune:
Acc@1 77.232 Acc@5 93.526
Acc@1 77.362 Acc@5 93.634
Acc@1 77.262 Acc@5 93.532
Acc@1 77.280 Acc@5 93.696
coco:
bbox: 39.4056,59.0497,42.3511,22.5197,44.3473,53.2681
segm: 34.5090,55.8374,36.8892,15.4050,38.3349,51.3551
bbox: 39.4914,59.2288,42.7277,21.8810,44.7128,53.6556
segm: 34.6709,56.1226,37.1046,15.4873,38.2889,52.5108
bbox: 39.4731,59.2949,42.4782,23.3873,44.6141,53.2002
segm: 34.6118,56.0852,36.8488,16.4710,38.2405,51.8747
bbox: 39.3198,58.9063,42.3989,22.9052,44.1219,53.4169
segm: 34.4959,55.8026,36.7658,16.2410,38.2230,52.2665
deeplab:
78.7261,58.7874,90.4714,78.3816
78.6566,58.2874,90.5395,78.3319
78.3647,58.4627,90.4798,78.5871
78.4664,58.7298,90.4983,78.3792
+dense:
linear:
Acc@1 68.878 Acc@5 88.988
Acc@1 68.794 Acc@5 88.962
Acc@1 68.722 Acc@5 88.982
Acc@1 68.784 Acc@5 88.952
finetune:
Acc@1 77.108 Acc@5 93.560
Acc@1 77.374 Acc@5 93.696
Acc@1 77.274 Acc@5 93.560
Acc@1 77.404 Acc@5 93.630
coco:
bbox: 39.9832,59.9284,43.4874,22.5677,45.1365,54.0637
segm: 34.9129,56.6404,37.0795,15.4479,38.6572,52.1838
bbox: 39.7174,59.5656,42.8223,22.8941,45.0750,53.2531
segm: 34.5298,56.3506,36.8583,15.6896,38.3153,51.9833
bbox: 40.1698,59.9970,43.3704,23.9472,45.6143,53.9589
segm: 34.9143,56.3698,37.4172,17.2151,38.8261,52.0241
bbox: 40.1558,59.7698,43.3668,22.2897,45.6031,54.1690
segm: 34.9127,56.6232,37.0422,15.5800,38.7052,52.5246
deeplab:
78.4191,58.8781,90.5701,78.7800
78.7982,59.7372,90.4969,78.4392
78.9305,59.1239,90.5806,78.7365
78.6499,58.9398,90.4527,78.1707