Relation-Network (CVPR, 2018)
文章: Relation Networks for Object Detection作者: Han Hu1, Jiayuan Gu, Zheng Zhang, Jifeng Dai1, Yichen Wei备注: MSRA, Peking University, Oral Paper
摘要尽管长久以
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《牛客网算法进阶班》视频教程笔记
第四章 堆和堆排序普通队列: 先进先出, 后进后出优先队列: 出队顺序和入队顺序无关; 和优先级相关
在 N 个元素中选出前 M 个元素. 使用排序: $O(NlogN)$, 使用优先队列: $O(NlogM)$.
堆的实现: 二叉堆 Binary Heap, 堆中某个节点的值总是不大于(最大堆)其
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MaskrcnnBenchmark 源码解析-各个网络层的封装实现(layers)
batch_norm.py
misc.py
nms.py
roi_align.py
roi_pool.py
smooth_l1_loss.py
InceptionV4 and Inception-ResNet
文章:Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning作者: Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
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Inception V3
文章: Rethinking the Inception Architecture for Computer Vision作者: Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna备注
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RFB Net (ECCV, 2018)
文章: Receptive Field Block Net for Accurate and Fast Object Detection作者: Songtao Liu, Di Huang, and Yunhong Wang备注: Beihang University
核心亮点本文从感受野大小的角度出
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IdentityMappings (ECCV, 2016)
文章: Identity Mappings in Deep Residual Networks作者: Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun备注: MSRA
核心亮点摘要Identity Mappings在第一篇ResNet论文中提
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R2CNN++ (Arxiv, 2018)
文章: R2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust Anchor Strategy作者: Xue Yang, Kun Fu, Hao Sun, Jirui Yang, Zhi
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R2CNN (Arxiv, 2017)
文章: R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection.作者: Yingying Jiang, Xiangyu Zhu, Xiaobing Wang, Shuli Yang, Wei Li, Hua
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ResNeXt (CVPR, 2017)
文章: Aggregated Residual Transformations for Deep Neural Networks作者: Saining Xie, Ross Girshick, Piotr Dollar, Zhuowen Tu, Kaiming He备注: UC San Diego,
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Detectron 源码解析-roidb数据结构
roidb数据结构roidb的类型是list, 其中的每个元素的数据类型都是dict, roidb列表的长度为数据集的数量(即图片的数量), roidb中每个元素的详细情况如下表所示:
for entry in roidb
数据类型
详细说明
entry['id']
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Detectron 源码解析-数据加载
Coordinator 类由于 RoIDataLoader 类将 Coordinator 类对象作为成员变量, 因此我们先看一下这个类的作用和底层实现, 该类位于detectron/utils/coordinator.py文件中, 定义如下:
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Cascade R-CNN (CVPR, 2018)
文章: Cascade R-CNN: Delving into High Quality Object Detection作者: Zhaowei Cai, Nuno Vasconcelos机构: UC San Diego
核心亮点本文针对检测问题中区分正负样本的 IoU 阈值选择问题提出了一种新的目
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