2.3. ResNet and ResNet_vd series

2.3.1. Overview

The ResNet series model was proposed in 2015 and won the championship in the ILSVRC2015 competition with a top5 error rate of 3.57%. The network innovatively proposed the residual structure, and built the ResNet network by stacking multiple residual structures. Experiments show that using residual blocks can improve the convergence speed and accuracy effectively.

Joyce Xu of Stanford university calls ResNet one of three architectures that “really redefine the way we think about neural networks.” Due to the outstanding performance of ResNet, more and more scholars and engineers from academia and industry have improved its structure. The well-known ones include wide-resnet, resnet-vc, resnet-vd, Res2Net, etc. The number of parameters and FLOPs of resnet-vc and resnet-vd are almost the same as those of ResNet, so we hereby unified them into the ResNet series.

The models of the ResNet series released this time include 14 pre-trained models including ResNet50, ResNet50_vd, ResNet50_vd_ssld, and ResNet200_vd. At the training level, ResNet adopted the standard training process for training ImageNet, while the rest of the improved model adopted more training strategies, such as cosine decay for the decline of learning rate and the regular label smoothing method,mixup was added to the data preprocessing, and the total number of iterations increased from 120 epoches to 200 epoches.

Among them, ResNet50_vd_v2 and ResNet50_vd_ssld adopted knowledge distillation, which further improved the accuracy of the model while keeping the structure unchanged. Specifically, the teacher model of ResNet50_vd_v2 is ResNet152_vd (top1 accuracy 80.59%), the training set is imagenet-1k, the teacher model of ResNet50_vd_ssld is ResNeXt101_32x16d_wsl (top1 accuracy 84.2%), and the training set is the combination of 4 million data mined by imagenet-22k and ImageNet-1k . The specific methods of knowledge distillation are being continuously updated.

The FLOPS, parameters, and inference time on the T4 GPU of this series of models are shown in the figure below.

As can be seen from the above curves, the higher the number of layers, the higher the accuracy, but the corresponding number of parameters, calculation and latency will increase. ResNet50_vd_ssld further improves the accuracy of top-1 of the ImageNet-1k validation set by using stronger teachers and more data, reaching 82.39%, refreshing the accuracy of ResNet50 series models.

2.3.2. Accuracy, FLOPS and Parameters

Models	Top1	Top5	Reference top1	Reference top5	FLOPS (G)	Parameters (M)
ResNet18	0.710	0.899	0.696	0.891	3.660	11.690
ResNet18_vd	0.723	0.908			4.140	11.710
ResNet34	0.746	0.921	0.732	0.913	7.360	21.800
ResNet34_vd	0.760	0.930			7.390	21.820
ResNet50	0.765	0.930	0.760	0.930	8.190	25.560
ResNet50_vc	0.784	0.940			8.670	25.580
ResNet50_vd	0.791	0.944	0.792	0.946	8.670	25.580
ResNet50_vd_v2	0.798	0.949			8.670	25.580
ResNet101	0.776	0.936	0.776	0.938	15.520	44.550
ResNet101_vd	0.802	0.950			16.100	44.570
ResNet152	0.783	0.940	0.778	0.938	23.050	60.190
ResNet152_vd	0.806	0.953			23.530	60.210
ResNet200_vd	0.809	0.953			30.530	74.740
ResNet50_vd_ssld	0.824	0.961			8.670	25.580
ResNet50_vd_ssld_v2	0.830	0.964			8.670	25.580
Fix_ResNet50_vd_ssld_v2	0.840	0.970			17.696	25.580
ResNet101_vd_ssld	0.837	0.967			16.100	44.570

• Note: ResNet50_vd_ssld_v2 is obtained by adding AutoAugment in training process on the basis of ResNet50_vd_ssld training strategy.Fix_ResNet50_vd_ssld_v2 stopped all parameter updates of ResNet50_vd_ssld_v2 except the FC layer,and fine-tuned on ImageNet1k dataset, the resolution is 320x320.

2.3.3. Inference speed based on V100 GPU

Models	Crop Size	Resize Short Size	FP32 Batch Size=1 (ms)
ResNet18	224	256	1.499
ResNet18_vd	224	256	1.603
ResNet34	224	256	2.272
ResNet34_vd	224	256	2.343
ResNet50	224	256	2.939
ResNet50_vc	224	256	3.041
ResNet50_vd	224	256	3.165
ResNet50_vd_v2	224	256	3.165
ResNet101	224	256	5.314
ResNet101_vd	224	256	5.252
ResNet152	224	256	7.205
ResNet152_vd	224	256	7.200
ResNet200_vd	224	256	8.885
ResNet50_vd_ssld	224	256	3.165
ResNet101_vd_ssld	224	256	5.252

2.3.4. Inference speed based on T4 GPU

Models	Crop Size	Resize Short Size	FP16 Batch Size=1 (ms)	FP16 Batch Size=4 (ms)	FP16 Batch Size=8 (ms)	FP32 Batch Size=1 (ms)	FP32 Batch Size=4 (ms)	FP32 Batch Size=8 (ms)
ResNet18	224	256	1.3568	2.5225	3.61904	1.45606	3.56305	6.28798
ResNet18_vd	224	256	1.39593	2.69063	3.88267	1.54557	3.85363	6.88121
ResNet34	224	256	2.23092	4.10205	5.54904	2.34957	5.89821	10.73451
ResNet34_vd	224	256	2.23992	4.22246	5.79534	2.43427	6.22257	11.44906
ResNet50	224	256	2.63824	4.63802	7.02444	3.47712	7.84421	13.90633
ResNet50_vc	224	256	2.67064	4.72372	7.17204	3.52346	8.10725	14.45577
ResNet50_vd	224	256	2.65164	4.84109	7.46225	3.53131	8.09057	14.45965
ResNet50_vd_v2	224	256	2.65164	4.84109	7.46225	3.53131	8.09057	14.45965
ResNet101	224	256	5.04037	7.73673	10.8936	6.07125	13.40573	24.3597
ResNet101_vd	224	256	5.05972	7.83685	11.34235	6.11704	13.76222	25.11071
ResNet152	224	256	7.28665	10.62001	14.90317	8.50198	19.17073	35.78384
ResNet152_vd	224	256	7.29127	10.86137	15.32444	8.54376	19.52157	36.64445
ResNet200_vd	224	256	9.36026	13.5474	19.0725	10.80619	25.01731	48.81399
ResNet50_vd_ssld	224	256	2.65164	4.84109	7.46225	3.53131	8.09057	14.45965
ResNet50_vd_ssld_v2	224	256	2.65164	4.84109	7.46225	3.53131	8.09057	14.45965
Fix_ResNet50_vd_ssld_v2	320	320	3.42818	7.51534	13.19370	5.07696	14.64218	27.01453
ResNet101_vd_ssld	224	256	5.05972	7.83685	11.34235	6.11704	13.76222	25.11071