details.md

Training Results

VGGNet

cifar100 + e100 + sgd + mslr + ssl(1e-5)

# vggnet16_bn default training
total -  top1 acc: 80.940  top5 acc: 95.550
# vggnet16_bn channel_wise pruning training
total -  top1 acc: 80.790  top5 acc: 95.310
# vggnet16_bn filter_and_channel_wise pruning training
total -  top1 acc: 80.770  top5 acc: 95.580
# vggnet16_bn filter_and_channel_wise pruning training
total -  top1 acc: 80.790  top5 acc: 95.310

ResNet

cifar100 + e100 + sgd + mslr + ssl(1e-5)

# resnet50 default training
total -  top1 acc: 84.070  top5 acc: 96.290
# resnet50 depth_wise pruning training
total -  top1 acc: 84.070  top5 acc: 96.290

Filter_wise

arch	prune type	prune way	pruning ratio	actual pruning ratio	flops/G	model size/MB	Flops after pruning	Model size after pruning	top1	top5
vggnet16_bn	filter_wise	group_lasso	20%	18.56%	15.51	134.68	7.67	130.88	80.810	95.090
vggnet16_bn	filter_wise	mean_abs	20%	19.13%	15.51	134.68	9.20	129.51	80.470	94.940
vggnet16_bn	filter_wise	mean	20%	19.13%	15.51	134.68	10.92	69.38	79.650	94.800
vggnet16_bn	filter_wise	sum_abs	20%	19.13%	15.51	134.68	6.75	132.22	79.440	94.880
vggnet16_bn	filter_wise	sum	20%	19.32%	15.51	134.68	13.85	55.88	79.580	95.100
vggnet16_bn	filter_wise	mean_abs	40%	39.01%	15.51	134.68	6.42	112.47	78.900	94.470
vggnet16_bn	filter_wise	mean_abs	40%	58.71%	15.51	134.68	4.60	74.06	75.880	93.090

Channel_wise

arch	prune type	prune way	pruning ratio	actual pruning ratio	flops/G	model size/MB	Flops after pruning	Model size after pruning	top1	top5
vggnet16_bn	channel_wise	group_lasso	20%	18.95%	15.51	134.68	8.21	131.26	80.800	95.070
vggnet16_bn	channel_wise	mean_abs	20%	19.38%	15.51	134.68	9.57	130.53	80.660	95.280
vggnet16_bn	channel_wise	mean_abs	40%	38.98%	15.51	134.68	6.15	126.91	79.900	94.910
vggnet16_bn	channel_wise	mean_abs	60%	59.00%	15.51	134.68	4.11	123.30	78.620	94.480

Filter_and_Channel_wise

arch	prune type	prune way	pruning ratio	actual pruning ratio	flops/G	model size/MB	Flops after pruning	Model size after pruning	top1	top5
vggnet16_bn	filter_and_channel_wise	group_lasso	20%	13.91%	15.51	134.68	9.56	131.94	80.53	95.320
vggnet16_bn	filter_and_channel_wise	mean_abs	20%	11.29%	15.51	134.68	11.36	131.84	80.720	95.130
vggnet16_bn	filter_and_channel_wise	mean_abs	40%	22.98%	15.51	134.68	8.98	128.81	80.040	95.180
vggnet16_bn	filter_and_channel_wise	mean_abs	60%	35.07%	15.51	134.68	7.45	125.67	79.570	94.920

Depth_wise

arch	prune type	prune way	N	flops/G	model size/MB	Flops after pruning	Model size after pruning	top1	top5
resnet50	depth_wise	group_lasso	1	4.11	23.72	3.89	23.64	83.610	95.970
resnet50	depth_wise	mean_abs	1	4.11	23.72	3.89	19.25	83.630	96.090
resnet50	depth_wise	mean_abs	2	4.11	23.72	3.67	14.79	82.990	95.710
resnet50	depth_wise	mean_abs	4	4.11	23.72	3.23	13.39	82.280	95.400