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EfficientNet and ResNeXt101_wsl series

Overview

EfficientNet is a lightweight NAS-based network released by Google in 2019. EfficientNetB7 refreshed the classification accuracy of ImageNet-1k at that time. In this paper, the author points out that the traditional methods to improve the performance of neural networks mainly start with the width of the network, the depth of the network, and the resolution of the input picture. However, the author found that balancing these three dimensions is essential for improving accuracy and efficiency through experiments. Therefore, the author summarized how to balance the three dimensions at the same time through a series of experiments. At the same time, based on this scaling method, the author built a total of 7 networks B1-B7 in the EfficientNet series on the basis of EfficientNetB0, and with the same FLOPS and parameters, the accuracy reached state-of-the-art effect.

ResNeXt is an improved version of ResNet that proposed by Facebook in 2016. In 2019, Facebook researchers studied the accuracy limit of the series network on ImageNet through weakly-supervised-learning. In order to distinguish the previous ResNeXt network, the suffix of this series network is WSL, where WSL is the abbreviation of weakly-supervised-learning. In order to have stronger feature extraction capability, the researchers further enlarged the network width, among which the largest ResNeXt101_32x48d_wsl has 800 million parameters. It was trained under 940 million weak-labeled images, and the results were finetune trained on imagenet-1k. Finally, the acc-1 of imagenet-1k reaches 85.4%, which is also the network with the highest precision under the resolution of 224x224 on imagenet-1k so far. In Fix-ResNeXt, the author used a larger image resolution, made a special Fix strategy for the inconsistency of image data preprocessing in training and testing, and made ResNeXt101_32x48d_wsl have a higher accuracy. Since it used the Fix strategy, it was named Fix-ResNeXt101_32x48d_wsl.

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 14 pretrained models of the two types of models that PaddleClas open source. It can be seen from the above figure that the advantages of the EfficientNet series network are very obvious. The ResNeXt101_wsl series model uses more data, and the final accuracy is also higher. EfficientNet_B0_small removes SE_block based on EfficientNet_B0, which has faster inference speed.

Accuracy, FLOPS and Parameters

Models Top1 Top5 Reference
top1
Reference
top5
FLOPS
(G)
Parameters
(M)
ResNeXt101_
32x8d_wsl
0.826 0.967 0.822 0.964 29.140 78.440
ResNeXt101_
32x16d_wsl
0.842 0.973 0.842 0.972 57.550 152.660
ResNeXt101_
32x32d_wsl
0.850 0.976 0.851 0.975 115.170 303.110
ResNeXt101_
32x48d_wsl
0.854 0.977 0.854 0.976 173.580 456.200
Fix_ResNeXt101_
32x48d_wsl
0.863 0.980 0.864 0.980 354.230 456.200
EfficientNetB0 0.774 0.933 0.773 0.935 0.720 5.100
EfficientNetB1 0.792 0.944 0.792 0.945 1.270 7.520
EfficientNetB2 0.799 0.947 0.803 0.950 1.850 8.810
EfficientNetB3 0.812 0.954 0.817 0.956 3.430 11.840
EfficientNetB4 0.829 0.962 0.830 0.963 8.290 18.760
EfficientNetB5 0.836 0.967 0.837 0.967 19.510 29.610
EfficientNetB6 0.840 0.969 0.842 0.968 36.270 42.000
EfficientNetB7 0.843 0.969 0.844 0.971 72.350 64.920
EfficientNetB0_
small
0.758 0.926 0.720 4.650

Inference speed based on V100 GPU

Models Crop Size Resize Short Size FP32
Batch Size=1
(ms)
ResNeXt101_
32x8d_wsl
224 256 19.127
ResNeXt101_
32x16d_wsl
224 256 23.629
ResNeXt101_
32x32d_wsl
224 256 40.214
ResNeXt101_
32x48d_wsl
224 256 59.714
Fix_ResNeXt101_
32x48d_wsl
320 320 82.431
EfficientNetB0 224 256 2.449
EfficientNetB1 240 272 3.547
EfficientNetB2 260 292 3.908
EfficientNetB3 300 332 5.145
EfficientNetB4 380 412 7.609
EfficientNetB5 456 488 12.078
EfficientNetB6 528 560 18.381
EfficientNetB7 600 632 27.817
EfficientNetB0_
small
224 256 1.692

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)
ResNeXt101_
32x8d_wsl
224 256 18.19374 21.93529 34.67802 18.52528 34.25319 67.2283
ResNeXt101_
32x16d_wsl
224 256 18.52609 36.8288 62.79947 25.60395 71.88384 137.62327
ResNeXt101_
32x32d_wsl
224 256 33.51391 70.09682 125.81884 54.87396 160.04337 316.17718
ResNeXt101_
32x48d_wsl
224 256 50.97681 137.60926 190.82628 99.01698256 315.91261 551.83695
Fix_ResNeXt101_
32x48d_wsl
320 320 78.62869 191.76039 317.15436 160.0838242 595.99296 1151.47384
EfficientNetB0 224 256 3.40122 5.95851 9.10801 3.442 6.11476 9.3304
EfficientNetB1 240 272 5.25172 9.10233 14.11319 5.3322 9.41795 14.60388
EfficientNetB2 260 292 5.91052 10.5898 17.38106 6.29351 10.95702 17.75308
EfficientNetB3 300 332 7.69582 16.02548 27.4447 7.67749 16.53288 28.5939
EfficientNetB4 380 412 11.55585 29.44261 53.97363 12.15894 30.94567 57.38511
EfficientNetB5 456 488 19.63083 56.52299 - 20.48571 61.60252 -
EfficientNetB6 528 560 30.05911 - - 32.62402 - -
EfficientNetB7 600 632 47.86087 - - 53.93823 - -
EfficientNetB0_small 224 256 2.39166 4.36748 6.96002 2.3076 4.71886 7.21888