DPN_DenseNet_en.md

DPN and DenseNet series

Overview

DenseNet is a new network structure proposed in 2017 and was the best paper of CVPR. The network has designed a new cross-layer connected block called dense-block. Compared to the bottleneck in ResNet, dense-block has designed a more aggressive dense connection module, that is, connecting all the layers to each other, and each layer will accept all the layers in front of it as its additional input. DenseNet stacks all dense-blocks into a densely connected network. The dense connection makes DenseNet easier to backpropagate, making the network easier to train and converge. The full name of DPN is Dual Path Networks, which is a network composed of DenseNet and ResNeXt, which proves that DenseNet can extract new features from the previous level, and ResNeXt essentially reuses the extracted features . The author further analyzes and finds that ResNeXt has high reuse rate for features, but low redundancy, while DenseNet can create new features, but with high redundancy. Combining the advantages of the two structures, the author designed the DPN network. In the end, the DPN network achieved better results than ResNeXt and DenseNet under the same FLOPS and parameters.

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

The pretrained models of these two types of models (a total of 10) are open sourced in PaddleClas at present. The indicators are shown in the figure above. It is easy to observe that under the same FLOPS and parameters, DPN has higher accuracy than DenseNet. However,because DPN has more branches, its inference speed is slower than DenseNet. Since DenseNet264 has the deepest layers in all DenseNet networks, it has the largest parameters,DenseNet161 has the largest width, resulting the largest FLOPs and the highest accuracy in this series. From the perspective of inference speed, DenseNet161, which has a large FLOPs and high accuracy, has a faster speed than DenseNet264, so it has a greater advantage than DenseNet264.

For DPN series networks, the larger the model's FLOPs and parameters, the higher the model's accuracy. Among them, since the width of DPN107 is the largest, it has the largest number of parameters and FLOPs in this series of networks.

Accuracy, FLOPS and Parameters

Models	Top1	Top5	Reference top1	Reference top5	FLOPS (G)	Parameters (M)
DenseNet121	0.757	0.926	0.750		5.690	7.980
DenseNet161	0.786	0.941	0.778		15.490	28.680
DenseNet169	0.768	0.933	0.764		6.740	14.150
DenseNet201	0.776	0.937	0.775		8.610	20.010
DenseNet264	0.780	0.939	0.779		11.540	33.370
DPN68	0.768	0.934	0.764	0.931	4.030	10.780
DPN92	0.799	0.948	0.793	0.946	12.540	36.290
DPN98	0.806	0.951	0.799	0.949	22.220	58.460
DPN107	0.809	0.953	0.802	0.951	35.060	82.970
DPN131	0.807	0.951	0.801	0.949	30.510	75.360

Inference speed based on V100 GPU

Models	Crop Size	Resize Short Size	FP32 Batch Size=1 (ms)
DenseNet121	224	256	4.371
DenseNet161	224	256	8.863
DenseNet169	224	256	6.391
DenseNet201	224	256	8.173
DenseNet264	224	256	11.942
DPN68	224	256	11.805
DPN92	224	256	17.840
DPN98	224	256	21.057
DPN107	224	256	28.685
DPN131	224	256	28.083

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)
DenseNet121	224	256	4.16436	7.2126	10.50221	4.40447	9.32623	15.25175
DenseNet161	224	256	9.27249	14.25326	20.19849	10.39152	22.15555	35.78443
DenseNet169	224	256	6.11395	10.28747	13.68717	6.43598	12.98832	20.41964
DenseNet201	224	256	7.9617	13.4171	17.41949	8.20652	17.45838	27.06309
DenseNet264	224	256	11.70074	19.69375	24.79545	12.14722	26.27707	40.01905
DPN68	224	256	11.7827	13.12652	16.19213	11.64915	12.82807	18.57113
DPN92	224	256	18.56026	20.35983	29.89544	18.15746	23.87545	38.68821
DPN98	224	256	21.70508	24.7755	40.93595	21.18196	33.23925	62.77751
DPN107	224	256	27.84462	34.83217	60.67903	27.62046	52.65353	100.11721
DPN131	224	256	28.58941	33.01078	55.65146	28.33119	46.19439	89.24904