# SEResNeXt与Res2Net系列 ## 概述 ResNeXt是ResNet的典型变种网络之一，ResNeXt发表于2017年的CVPR会议。在此之前，提升模型精度的方法主要集中在将网络变深或者变宽，这样增加了参数量和计算量，推理速度也会相应变慢。ResNeXt结构提出了通道分组（cardinality）的概念，作者通过实验发现增加通道的组数比增加深度和宽度更有效。其可以在不增加参数复杂度的前提下提高准确率，同时还减少了参数的数量，所以是比较成功的ResNet的变种。 SENet是2017年ImageNet分类比赛的冠军方案，其提出了一个全新的SE结构，该结构可以迁移到任何其他网络中，其通过控制scale的大小，把每个通道间重要的特征增强，不重要的特征减弱，从而让提取的特征指向性更强。 Res2Net是2019年提出的一种全新的对ResNet的改进方案，该方案可以和现有其他优秀模块轻松整合，在不增加计算负载量的情况下，在ImageNet、CIFAR-100等数据集上的测试性能超过了ResNet。Res2Net结构简单，性能优越，进一步探索了CNN在更细粒度级别的多尺度表示能力。Res2Net揭示了一个新的提升模型精度的维度，即scale，其是除了深度、宽度和基数的现有维度之外另外一个必不可少的更有效的因素。该网络在其他视觉任务如目标检测、图像分割等也有相当不错的表现。 该系列模型的FLOPS、参数量以及T4 GPU上的FP32预测耗时如下图所示。 ![](../../images/models/T4_benchmark/t4.fp32.bs4.SeResNeXt.flops.png) ![](../../images/models/T4_benchmark/t4.fp32.bs4.SeResNeXt.params.png) ![](../../images/models/T4_benchmark/t4.fp32.bs4.SeResNeXt.png) 目前PaddleClas开源的这三类的预训练模型一共有24个，其指标如图所示，从图中可以看出，在同样Flops和Params下，改进版的模型往往有更高的精度，但是推理速度往往不如ResNet系列。另一方面，Res2Net表现也较为优秀，相比ResNeXt中的group操作、SEResNet中的SE结构操作，Res2Net在相同Flops、Params和推理速度下往往精度更佳。 ## 精度、FLOPS和参数量 | 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_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 | | Res2Net200_vd_26w_4s | 0.812 | 0.957 | | | 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 | 0.791 | 0.942 | 0.793 | 0.950 | 15.020 | 46.280 | | SENet154_vd | 0.814 | 0.955 | | | 45.830 | 114.290 | ## 基于V100 GPU的预测速度 | 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 | ## 基于T4 GPU的预测速度 | 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 |