SEResNeXt and Res2Net series

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Catalogue

• 1. Overview
• 2. Accuracy, FLOPs and Parameters
• 3. Inference speed based on V100 GPU
• 4. Inference speed based on T4 GPU

1. Overview

ResNeXt, one of the typical variants of ResNet, was presented at the CVPR conference in 2017. Prior to this, the methods to improve the model accuracy mainly focused on deepening or widening the network, which increased the number of parameters and calculation, and slowed down the inference speed accordingly. The concept of cardinality was proposed in ResNeXt structure. The author found that increasing the number of channel groups was more effective than increasing the depth and width through experiments. It can improve the accuracy without increasing the parameter complexity and reduce the number of parameters at the same time, so it is a more successful variant of ResNet.

SENet is the winner of the 2017 ImageNet classification competition. It proposes a new SE structure that can be migrated to any other network. It controls the scale to enhance the important features between each channel, and weaken the unimportant features. So that the extracted features are more directional.

Res2Net is a brand-new improvement of ResNet proposed in 2019. The solution can be easily integrated with other excellent modules. Without increasing the amount of calculation, the performance on ImageNet, CIFAR-100 and other data sets exceeds ResNet. Res2Net, with its simple structure and superior performance, further explores the multi-scale representation capability of CNN at a more fine-grained level. Res2Net reveals a new dimension to improve model accuracy, called scale, which is an essential and more effective factor in addition to the existing dimensions of depth, width, and cardinality. The network also performs well in other visual tasks such as object detection and image segmentation.

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

At present, there are a total of 24 pretrained models of the three categories open sourced by PaddleClas, and the indicators are shown in the figure. It can be seen from the diagram that under the same Flops and Params, the improved model tends to have higher accuracy, but the inference speed is often inferior to the ResNet series. On the other hand, Res2Net performed better. Compared with group operation in ResNeXt and SE structure operation in SEResNet, Res2Net tended to have better accuracy in the same Flops, Params and inference speed.

2. Accuracy, FLOPs and Parameters

Models	Top1	Top5	Reference top1	Reference top5	FLOPs (G)	Parameters (M)
Res2Net50_26w_4s	0.793	0.946	0.780	0.936	8.520	25.700
Res2Net50_vd_26w_4s	0.798	0.949			8.370	25.060
Res2Net50_vd_26w_4s_ssld	0.831	0.966			8.370	25.060
Res2Net50_14w_8s	0.795	0.947	0.781	0.939	9.010	25.720
Res2Net101_vd_26w_4s	0.806	0.952			16.670	45.220
Res2Net101_vd_26w_4s_ssld	0.839	0.971			16.670	45.220
Res2Net200_vd_26w_4s	0.812	0.957			31.490	76.210
Res2Net200_vd_26w_4s_ssld	0.851	0.974			31.490	76.210
ResNeXt50_32x4d	0.778	0.938	0.778		8.020	23.640
ResNeXt50_vd_32x4d	0.796	0.946			8.500	23.660
ResNeXt50_64x4d	0.784	0.941			15.060	42.360
ResNeXt50_vd_64x4d	0.801	0.949			15.540	42.380
ResNeXt101_32x4d	0.787	0.942	0.788		15.010	41.540
ResNeXt101_vd_32x4d	0.803	0.951			15.490	41.560
ResNeXt101_64x4d	0.784	0.945	0.796		29.050	78.120
ResNeXt101_vd_64x4d	0.808	0.952			29.530	78.140
ResNeXt152_32x4d	0.790	0.943			22.010	56.280
ResNeXt152_vd_32x4d	0.807	0.952			22.490	56.300
ResNeXt152_64x4d	0.795	0.947			43.030	107.570
ResNeXt152_vd_64x4d	0.811	0.953			43.520	107.590
SE_ResNet18_vd	0.733	0.914			4.140	11.800
SE_ResNet34_vd	0.765	0.932			7.840	21.980
SE_ResNet50_vd	0.795	0.948			8.670	28.090
SE_ResNeXt50_32x4d	0.784	0.940	0.789	0.945	8.020	26.160
SE_ResNeXt50_vd_32x4d	0.802	0.949			10.760	26.280
SE_ResNeXt101_32x4d	0.7939	0.9443	0.793	0.950	15.020	46.280
SENet154_vd	0.814	0.955			45.830	114.290

3. Inference speed based on V100 GPU

Models	Crop Size	Resize Short Size	FP32 Batch Size=1 (ms)
Res2Net50_26w_4s	224	256	4.148
Res2Net50_vd_26w_4s	224	256	4.172
Res2Net50_14w_8s	224	256	5.113
Res2Net101_vd_26w_4s	224	256	7.327
Res2Net200_vd_26w_4s	224	256	12.806
ResNeXt50_32x4d	224	256	10.964
ResNeXt50_vd_32x4d	224	256	7.566
ResNeXt50_64x4d	224	256	13.905
ResNeXt50_vd_64x4d	224	256	14.321
ResNeXt101_32x4d	224	256	14.915
ResNeXt101_vd_32x4d	224	256	14.885
ResNeXt101_64x4d	224	256	28.716
ResNeXt101_vd_64x4d	224	256	28.398
ResNeXt152_32x4d	224	256	22.996
ResNeXt152_vd_32x4d	224	256	22.729
ResNeXt152_64x4d	224	256	46.705
ResNeXt152_vd_64x4d	224	256	46.395
SE_ResNet18_vd	224	256	1.694
SE_ResNet34_vd	224	256	2.786
SE_ResNet50_vd	224	256	3.749
SE_ResNeXt50_32x4d	224	256	8.924
SE_ResNeXt50_vd_32x4d	224	256	9.011
SE_ResNeXt101_32x4d	224	256	19.204
SENet154_vd	224	256	50.406

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)
Res2Net50_26w_4s	224	256	3.56067	6.61827	11.41566	4.47188	9.65722	17.54535
Res2Net50_vd_26w_4s	224	256	3.69221	6.94419	11.92441	4.52712	9.93247	18.16928
Res2Net50_14w_8s	224	256	4.45745	7.69847	12.30935	5.4026	10.60273	18.01234
Res2Net101_vd_26w_4s	224	256	6.53122	10.81895	18.94395	8.08729	17.31208	31.95762
Res2Net200_vd_26w_4s	224	256	11.66671	18.93953	33.19188	14.67806	32.35032	63.65899
ResNeXt50_32x4d	224	256	7.61087	8.88918	12.99674	7.56327	10.6134	18.46915
ResNeXt50_vd_32x4d	224	256	7.69065	8.94014	13.4088	7.62044	11.03385	19.15339
ResNeXt50_64x4d	224	256	13.78688	15.84655	21.79537	13.80962	18.4712	33.49843
ResNeXt50_vd_64x4d	224	256	13.79538	15.22201	22.27045	13.94449	18.88759	34.28889
ResNeXt101_32x4d	224	256	16.59777	17.93153	21.36541	16.21503	19.96568	33.76831
ResNeXt101_vd_32x4d	224	256	16.36909	17.45681	22.10216	16.28103	20.25611	34.37152
ResNeXt101_64x4d	224	256	30.12355	32.46823	38.41901	30.4788	36.29801	68.85559
ResNeXt101_vd_64x4d	224	256	30.34022	32.27869	38.72523	30.40456	36.77324	69.66021
ResNeXt152_32x4d	224	256	25.26417	26.57001	30.67834	24.86299	29.36764	52.09426
ResNeXt152_vd_32x4d	224	256	25.11196	26.70515	31.72636	25.03258	30.08987	52.64429
ResNeXt152_64x4d	224	256	46.58293	48.34563	56.97961	46.7564	56.34108	106.11736
ResNeXt152_vd_64x4d	224	256	47.68447	48.91406	57.29329	47.18638	57.16257	107.26288
SE_ResNet18_vd	224	256	1.61823	3.1391	4.60282	1.7691	4.19877	7.5331
SE_ResNet34_vd	224	256	2.67518	5.04694	7.18946	2.88559	7.03291	12.73502
SE_ResNet50_vd	224	256	3.65394	7.568	12.52793	4.28393	10.38846	18.33154
SE_ResNeXt50_32x4d	224	256	9.06957	11.37898	18.86282	8.74121	13.563	23.01954
SE_ResNeXt50_vd_32x4d	224	256	9.25016	11.85045	25.57004	9.17134	14.76192	19.914
SE_ResNeXt101_32x4d	224	256	19.34455	20.6104	32.20432	18.82604	25.31814	41.97758
SENet154_vd	224	256	49.85733	54.37267	74.70447	53.79794	66.31684	121.59885