【BoF】《Bag of Freebies for Training Object Detection Neural Networks》

arXiv-2019

https://github.com/dmlc/gluon-cv


文章目录

  • [1 Background and Motivation](#1 Background and Motivation)
  • [2 Related Work](#2 Related Work)
  • [3 Advantages / Contributions](#3 Advantages / Contributions)
  • [4 Method](#4 Method)
    • [4.1 Visually Coherent Image Mixup for Object Detection](#4.1 Visually Coherent Image Mixup for Object Detection)
    • [4.2 Classification Head Label Smoothing](#4.2 Classification Head Label Smoothing)
    • [4.3 Data Preprocessing](#4.3 Data Preprocessing)
    • [4.4 Training Schedule Revamping](#4.4 Training Schedule Revamping)
    • [4.5 Synchronized Batch Normalization](#4.5 Synchronized Batch Normalization)
    • [4.6 Random shapes training for singlestage object detection networks](#4.6 Random shapes training for singlestage object detection networks)
  • [5 Experiments](#5 Experiments)
    • [5.1 Datasets and Metrics](#5.1 Datasets and Metrics)
    • [5.2 Incremental trick evaluation on Pascal VOC](#5.2 Incremental trick evaluation on Pascal VOC)
    • [5.3 Bag of Freebies on MS COCO](#5.3 Bag of Freebies on MS COCO)
    • [5.4 Impact of mixup on different phases of training detection network](#5.4 Impact of mixup on different phases of training detection network)
  • [6 Conclusion(own) / Future work](#6 Conclusion(own) / Future work)

1 Background and Motivation

分类任务出了篇 【BoT】《Bag of Tricks for Image Classification with Convolutional Neural Networks》(CVPR-2019),目标检测任务比图像分类任务复杂,作者基于目标检测任务,来借鉴整合了些 bag of freebies,inference free,有明显涨点

  • Scattering tricks from Image Classification

    • Learning rate warmup
    • Label smoothing
    • mixup
    • Cosine annealing strategy
  • Deep Object Detection Pipelines

    • one stage
    • two stage

3 Advantages / Contributions

整理了一些目标检测的 bag of freebies(proposed a visually coherent image mixup methods),使 yolov3 在 coco 数据集上提了 5 个点

4 Method

4.1 Visually Coherent Image Mixup for Object Detection

原版的 【Mixup】《Mixup:Beyond Empirical Risk Minimization》(ICLR-2018)在分类任务中的应用

beta 分布取得是 α = β = 0.5 \alpha=\beta=0.5 α=β=0.5,混合比例比较极端,基本非 A 即 B

beta 分布的这种分布应用在目标检测任务中的结果如下

贴在画面中的大象很容易漏检

作者把 mixup 应用在目标检测的时候,把 beta 分布的参数改为了 α = β = 1.5 \alpha=\beta=1.5 α=β=1.5

混合的更充分,作者对这种混合形式的语言描述如下

similar to the transition frames commonly observed when we are watching low FPS movies or surveillance videos.

混合效果如下

networks are encouraged to observe unusual crowded patches

4.2 Classification Head Label Smoothing

正常的 label smoothing,用在分类分支上,来自 【Inception-v3】《Rethinking the Inception Architecture for Computer Vision》(CVPR-2016)

标签的 one-shot 的分布(缺点 This encourages the model to be too confident)改为上述公式分布

4.3 Data Preprocessing

(1)Random geometry transformation

  • random cropping (with constraints)

  • random expansion

  • random horizontal flip

  • random resize (with random interpolation)

two-stage 的目标检测相比 one stage,多了一个 roi pooling 以及之后的过程,所以 two-stage 的时候,not use random cropping techniques during data augmentation.

(2)Random color jittering

  • brightness

  • hue

  • saturation

  • contrast

4.4 Training Schedule Revamping

传统 step learning rate 的缺点

Step schedule has sharp learning rate transition which may cause the optimizer to re-stabilize the learning momentum in the next few iterations.

作者采用余弦学习率(the higher frequency of learning rate adjustment) + warm up(avoid gradient explosion during the initial training iterations.)

4.5 Synchronized Batch Normalization

跨机器 synchronized batch normalization in object detection

4.6 Random shapes training for singlestage object detection networks

H = W = { 320 ; 352 ; 384 ; 416 ; 448 ; 480 ; 512 ; 544 ; 576 ; 608 } H =W = \{320; 352; 384; 416; 448; 480; 512; 544; 576; 608\} H=W={320;352;384;416;448;480;512;544;576;608}

5 Experiments

  • yolov3

  • faster rcnn

5.1 Datasets and Metrics

  • PASCAL VOC

    Pascal VOC 2007 trainval and 2012 trainval for training and 2007 test set for validation.

  • COCO

5.2 Incremental trick evaluation on Pascal VOC

mixup 改进提升点


看看其他 bag of freebies 的提升情况

可以看到 one-stage 对 data augmentation 更依赖

two-stage sampling based proposals can effectively replace random cropping,对 data augmentation 的依赖更少

5.3 Bag of Freebies on MS COCO

对 yolov3 的提升还是很猛的

全类别,基本都是提升的红色

5.4 Impact of mixup on different phases of training detection network

mix up 有两个地方涉及到

  1. pre-training classification network backbone with traditional mixup

  2. training detection networks using proposed visually coherent image mixup for object detection

    预训练和训练的时候都用 mix up 提升最明显

作者的解释

We expect by applying mixup in both training phases, shallow layers of networks are receiving statistically similar inputs, resulting in less perturbations for low level filters.

6 Conclusion(own) / Future work

  • Rosenfeld A, Zemel R, Tsotsos J K. The elephant in the room[J]. arXiv preprint arXiv:1808.03305, 2018.
  • a large amount of anchor size(up to 30k) is effectively contributing to batch size implicitly
相关推荐
谢眠12 分钟前
机器学习day6-线性代数2-梯度下降
人工智能·机器学习
sp_fyf_20241 小时前
【大语言模型】ACL2024论文-19 SportsMetrics: 融合文本和数值数据以理解大型语言模型中的信息融合
人工智能·深度学习·神经网络·机器学习·语言模型·自然语言处理
CoderIsArt1 小时前
基于 BP 神经网络整定的 PID 控制
人工智能·深度学习·神经网络
开源社1 小时前
一场开源视角的AI会议即将在南京举办
人工智能·开源
FreeIPCC1 小时前
谈一下开源生态对 AI人工智能大模型的促进作用
大数据·人工智能·机器人·开源
机器之心2 小时前
全球十亿级轨迹点驱动,首个轨迹基础大模型来了
人工智能·后端
z千鑫2 小时前
【人工智能】PyTorch、TensorFlow 和 Keras 全面解析与对比:深度学习框架的终极指南
人工智能·pytorch·深度学习·aigc·tensorflow·keras·codemoss
EterNity_TiMe_2 小时前
【论文复现】神经网络的公式推导与代码实现
人工智能·python·深度学习·神经网络·数据分析·特征分析
机智的小神仙儿2 小时前
Query Processing——搜索与推荐系统的核心基础
人工智能·推荐算法
AI_小站2 小时前
RAG 示例:使用 langchain、Redis、llama.cpp 构建一个 kubernetes 知识库问答
人工智能·程序人生·langchain·kubernetes·llama·知识库·rag