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