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ResNeSt_RegNet_en.md

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ResNeSt and RegNet series


Catalogue

1. Overview

The ResNeSt series was proposed in 2020. The original resnet network structure has been improved by introducing K groups and adding an attention module similar to SEBlock in different groups, the accuracy is greater than that of the basic model ResNet, but the parameter amount and flops are almost the same as the basic ResNet.

RegNet was proposed in 2020 by Facebook to deepen the concept of design space. Based on AnyNetX, the model performance is gradually improved by shared bottleneck ratio, shared group width, adjusting network depth or width and other strategies. What's more, the design space structure is simplified, whose interpretability is also be improved. The quality of design space is improved while its diversity is maintained. Under similar conditions, the performance of the designed RegNet model performs better than EfficientNet and 5 times faster than EfficientNet.

2. Accuracy, FLOPs and Parameters

Models Top1 Top5 Reference
top1
Reference
top5
FLOPs
(G)
Parameters
(M)
ResNeSt50_fast_1s1x64d 0.8035 0.9528 0.8035 - 8.68 26.3
ResNeSt50 0.8083 0.9542 0.8113 - 10.78 27.5
RegNetX_4GF 0.7850 0.9416 0.7860 - 8.0 22.1

3. 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)
ResNeSt50_fast_1s1x64d 224 256 3.46466 5.56647 9.11848 3.45405 8.72680 15.48710
ResNeSt50 224 256 7.05851 8.97676 13.34704 6.16248 12.0633 21.49936
RegNetX_4GF 224 256 6.69042 8.01664 11.60608 6.46478 11.19862 16.89089