ScaleNet

By Yi Li, Zhanghui Kuang, Yimin Chen, Wayne Zhang

SenseTime.

Table of Contents

0. Introduction
1. Citation
2. Approach
3. Trained models
4. Experiments
5. GPU time

Introduction

This is a PyTorch implementation of Data-Driven Neuron Allocation for Scale Aggregation Networks.(CVPR2019) with pretrained models.

Citation

If you use these models in your research, please cite:

@inproceedings{Li2019ScaleNet,
    title={Data-Driven Neuron Allocation for Scale Aggregation Networks},
    author={Li, Yi and Kuang, Zhanghui and Chen, Yimin and Zhang, Wayne},
    booktitle={CVPR},
    year={2019}
}

Approach

Figure 1: architecture of ScaleNet-50.

Figure 2: scale aggregation block.

Trained models

Model	Top-1 err.	Top-5 err.
ScaleNet-50-light	22.80	6.57
ScaleNet-50	22.02	6.05
ScaleNet-101	20.82	5.42
ScaleNet-152	20.06	5.18

Pytorch:

from pytorch.scalenet import *

model = scalenet50(structure_path='structures/scalenet50.json', ckpt=None) # train from stratch

model = scalenet50(structure_path='structures/scalenet50.json', ckpt='weights/scalenet50.pth') # load pretrained model

The weights are available on BaiduYun with extract code: f1c5

Unlike the paper, we used better training settings: increase the epochs to 120 and replace multi-step learning rate by cosine learning rate.

Experiments

Figure 3: experiments on imagenet classification.

Figure 4: experiments on ms-coco detection.

GPU time

Model	Top-1 err.	FLOPs(10^9)	GPU time(ms)
ResNet-50	24.02	4.1	95
SE-ResNet-50	23.29	4.1	98
ResNeXt-50	22.2	4.2	147
ScaleNet-50	22.2	3.8	93

TensorFlow: (empty models of ResNet, SE-ResNet, ResNeXt, ScaleNet for speed test)

python3 tensorflow/test_speed.py scale|res|se|next

All networks were tested using Tensorflow with GTX 1060 GPU and i7 CPU at batch size 16 and image side 224 on 1000 runs.

Some static-graph frameworks like Tensorflow and TensorRT execute multi-branch models in parallel, while Pytorch and Caffe do not. So we suggest to deploy ScaleNets on Tensorflow and TensorRT.