arXiv-2019
https://github.com/dmlc/gluon-cv
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，有显着涨点
2 Related Work
- Scattering tricks from Image Classification
- Learning rate warmup
- Label smoothing
- mixup
- Cosine annealing strategy
- Deep Object Detection Pipelines
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，肴杂比例比较非常，根本非 A 即 B
beta 分布的这种分布应用在目标检测任务中的效果如下
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
肴杂的更充分，作者对这种肴杂形式的语言形貌如下
similar to the transition frames commonly observed when we are watching low FPS movies or surveillance videos.
肴杂效果如下
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
跨呆板 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
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 更依靠
two-stage sampling based proposals can effectively replace random cropping，对 data augmentation 的依靠更少
5.3 Bag of Freebies on MS COCO
对 yolov3 的提升还是很猛的
全种别，根本都是提升的红色
5.4 Impact of mixup on different phases of training detection network
mix up 有两个地方涉及到
- pre-training classification network backbone with traditional mixup
- 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 提升最显着
作者的表明
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
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