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pytorch-classification

trian (more reference pytorch-examples-imagenet)

  • python main.py -a alexnet --lr 0.01

test

  • python main.py -a alexnet -e --pretrained

visual

  • python visualization.py alexnet

generate_json.py(Generate Json File for tensorrtCV)

  • python generate_json.py -a alennet --pretrained

model_zoo(imagenet dataset)

  • batch_time is a reference value, not necessarily accurate, please use it with caution.
  • batch_time(s/each 256 image) [One 1080Ti GPU]
model top1-acc top5-acc 1080Ti
alexnet 56.630 79.054 0.488
vgg11 68.872 88.658 0.521
vgg11_bn 70.408 89.724 0.513
vgg13 69.984 89.306 0.555
vgg13_bn 71.618 90.360 0.625
vgg16 71.628 90.368 0.642
vgg16_bn 73.476 91.536 0.716
vgg19 72.360 90.850 0.736
vgg19_bn 74.216 91.848 0.815
googlenet 69.744 89.544 0.504
inception_v3(299 x 299) 77.248 93.520 0.670
resnet18 69.644 88.982 0.492
resnet34 73.266 91.430 0.503
resnet50 76.012 92.934 0.523
resnet101 77.314 93.556 0.638
resnet152 78.250 93.982 0.895
resnext50_32x4d 77.628 93.680 0.554
resnext101_32x8d 79.210 94.556 1.414
wide_resnet50_2 78.464 94.064 0.678
wide_resnet101_2 78.910 94.344 1.116
densenet121 74.472 91.974 0.520
densenet161 77.146 93.602 0.984
densenet169 75.628 92.810 0.537
densenet201 76.932 93.390 0.687
squeezenet1_0 58.000 80.488 0.496
squeezenet1_1 58.184 80.514 0.493
shufflenet_v2_x0_5 60.646 81.696 0.488
shufflenet_v2_x1_0 69.402 88.374 0.490
mobilenet_v2 71.850 90.334 0.502
mobilenetv3_small 67.430 87.278 0.493
mnasnet0_5 67.830 87.456 0.490
mnasnet1_0 73.402 91.454 0.500
efficientnet_b0 76.090 93.006 0.499
efficientnet_b1(240 x 240) 78.166 93.994 0.563
efficientnet_b2(260 x 260) 79.298 94.510 0.727
efficientnet_b3(300 x 300) 81.126 95.518 -
hrnet_w18 76.832 93.404 0.617
hrnet_w18_small_v1 72.276 90.586 0.499
hrnet_w18_small_v2 75.164 92.430 0.506
hrnet_w30 78.134 94.192 0.798
ghostnet_1x 73.938 91.470 0.495
res2net_dla60 78.522 94.252 0.556
res2next_dla60 78.322 94.190 0.582
res2net50_v1b_26w_4s 80.208 95.044 0.569
res2net101_v1b_26w_4s 81.196 95.392 0.890
res2net50_26w_4s 77.966 93.830 0.542
res2net101_26w_4s 79.120 94.432 0.851
res2net50_26w_6s 78.604 94.164 0.746
res2net50_26w_8s 79.130 94.404 0.934
res2net50_48w_2s 77.518 93.582 0.539
res2net50_14w_8s 78.118 93.836 0.602
res2next50 78.080 93.952 0.590
regnet_200M 67.590 88.028 0.497
regnet_400M 71.946 90.632 0.493
regnet_600M 73.554 91.570 0.501
regnet_800M 74.880 92.294 0.503
regnet_1600M 76.996 93.452 0.510
regnet_3200M 78.358 94.162 0.523
regnet_6400M 79.202 94.764 0.659
resnest50 80.970 95.350 1.074
resnest50_fast_1s1x64d 80.150 95.112 0.513
resnest50_fast_2s1x64d 80.472 95.262 0.554
resnest50_fast_1s2x40d 80.400 95.310 0.558
resnest50_fast_2s2x40d 80.626 95.412 0.561
resnest50_fast_1s4x24d 80.870 95.364 0.550

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