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faster rcnn源码解析
置顶 2017年11月20日 23:50:01 爆米花好美啊 阅读数：19453 </div>
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版权声明：本文为博主原创文章，如若转载，请注明出处！ https://blog.csdn/u013010889/article/details/78574879 </div>
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之前一直是使用faster rcnn对其中的代码并不是很了解，这次刚好复现mask rcnn就仔细阅读了faster rcnn，主要参考代码是pytorch-faster-rcnn ，部分参考和借用了以下博客的图片
[1] CNN目标检测（一）：Faster RCNN详解
姊妹篇mask rcnn解析
整体框架
- 首先图片进行放缩到W*H，然后送入vgg16(去掉了pool5)，得到feature map(W/16, H/16)
- 然后feature map上每个点都对应原图上的9个anchor，送入rpn层后输出两个: 这9个anchor前背景的概率以及4个坐标的回归
- 每个anchor经过回归后对应到原图，然后再对应到feature map经过roi pooling后输出7*7大小的map
- 最后对这个7*7的map进行分类和再次回归
(此处均为大体轮廓，具体细节见后面)
数据层
- 主要利用工厂模式适配各种数据集 factory.py中利用lambda表达式(泛函)
- 自定义适配自己数据集的类，继承于imdb
- 主要针对数据集中生成roidb，对于每个图片保持其中含有的所有的box坐标(0-index)及其类别，然后顺便保存它的面积等参数，最后记录所有图片的index及其根据index获取绝对地址的方法
# factory.py
from datasets.mydataset import mydataset
for dataset in ['xxdataset']:
for split in ['train', 'val', 'test']:
name = '{}_{}'.format(dataset, split)
__sets[name] = (lambda split=split,dataset=dataset: mydataset(split, dataset))
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RPN
anchors生成
经过feature extraction后，feature map的大小是(W/16, H/16), 记为(w,h)，然后每个feature map每个点生成k个anchor，论文中设置了3中ratio, 3种scale 共产生了w*h*9个anchors
# # array([[ -83., -39., 100., 56.],
# [-175., -87., 192., 104.],
# [-359., -183., 376., 200.],
# [ -55., -55., 72., 72.],
# [-119., -119., 136., 136.],
# [-247., -247., 264., 264.],
# [ -35., -79., 52., 96.],
# [ -79., -167., 96., 184.],
# [-167., -343., 184., 360.]])
# 先以左上角(0,0)为例生成9个anchor，然后在向右向下移动，生成整个feature map所有点对应的anchor
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anchors前背景和坐标预测
正如整体框架上画的那样，feature map后先跟了一个3*3的卷积，然后分别用2个1*1的卷积，预测feature map上每个点对应的9个anchor属于前背景的概率(9*2)和4个回归的坐标(9*4)
# rpn
self.rpn_net = nn.Conv2d(self._net_conv_channels, cfg.RPN_CHANNELS, [3, 3], padding=1)
self.rpn_cls_score_net = nn.Conv2d(cfg.RPN_CHANNELS, self._num_anchors * 2, [1, 1])
self.rpn_bbox_pred_net = nn.Conv2d(cfg.RPN_CHANNELS, self._num_anchors * 4, [1, 1])
rpn = F.relu(self.rpn_net(net_conv))
rpn_cls_score = self.rpn_cls_score_net(rpn) # batch * (num_anchors * 2) * h * w
rpn_bbox_pred = self.rpn_bbox_pred_net(rpn) # batch * (num_anchors * 4) * h * w
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anchor target
对上一步产生的anchor分配target label，1前景or0背景or-1忽略，以便训练rpn(只有分配了label的才能计算loss，即参与训练)
无NMS
1. 对于每个gt box，找到与他iou最大的anchor然后设为正样本
2. 对于每个anchor只要它与任意一个gt box iou>0.7即设为正样本
3. 对于每个anchor它与任意一个gt box iou都<0.3即设为负样本
4. 不是正也不是负的anchor被忽略
注意
正样本的数量由num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE)控制,默认是256*0.5=128,即最多有128个正样本参与rpn的训练. 假如正样本有1234个,则随机抽1234-128个正样本将其label设置为-1,即忽略掉,当然正样本也有可能不足128个,那就都保留下来.
负样本的数量由num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1),同理如果超额也为多余的忽略.
TRAIN.RPN_FG_FRACTION控制参与rpn训练的正样本的数量
注意在RPN阶段需要的配置参数都有RPN前缀，与后面的fast rcnn的参数区别开
# Max number of foreground examples
# __C.TRAIN.RPN_FG_FRACTION = 0.5
# Total number of examples
#__C.TRAIN.RPN_BATCHSIZE = 256
# subsample positive labels if we have too many
num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE)
fg_inds = np.where(labels == 1)[0]
if len(fg_inds) > num_fg:
disable_inds = npr.choice(
fg_inds, size=(len(fg_inds) - num_fg), replace=False)
labels[disable_inds] = -1
# subsample negative labels if we have too many
num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1)
bg_inds = np.where(labels == 0)[0]
if len(bg_inds) > num_bg:
disable_inds = npr.choice(
bg_inds, size=(len(bg_inds) - num_bg), replace=False)
labels[disable_inds] = -1
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Fast RCNN
proposal
对RPN产生的anchor进行处理，有NMS
1. 首先利用4个坐标回归值对默认的w*h*9个anchor进行坐标变换生成proposal
2. 然后利用前景概率对这些proposal进行降序排列，然后留下RPN_PRE_NMS_TOP_N个proposal 训练是留下12000，测试是留下6000
3. 对剩下的proposal进行NMS处理，阈值是0.7
4. 对于剩下的proposal，只留下RPN_POST_NMS_TOP_N，训练是2000，测试是300
最终剩下的proposal即为rois了
proposal target
对留下的proposal(train:2000, test没有这个阶段，因为测试不知道gt无法分配)分配target label，属于具体哪一个类别，以便训练后面的分类器, 下面以train阶段的某个图片为例即该张图片有2000个proposal，gt中含有15个类别的box(不含背景) (全库有20个类别)
# Minibatch size (number of regions of interest [ROIs])
# __C.TRAIN.BATCH_SIZE = 128
# Fraction of minibatch that is labeled foreground (i.e. class > 0)
# __C.TRAIN.FG_FRACTION = 0.25 控制fast rcnn中rois的正负样本比例为1:3
num_images = 1
rois_per_image = cfg.TRAIN.BATCH_SIZE / num_images # 默认为128
fg_rois_per_image = int(round(cfg.TRAIN.FG_FRACTION * rois_per_image)) # 0.25*128
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- 计算每个roi(proposal)与15个gt box做iou，得到overlaps(2000, 15) ，然后选择最大的iou作为这个roi的gt label(坑点: gt box的顺序不一定和label对应，一定要取gt box的第4个维度作为label，因为可能包含15个gt box，但是全库是有20中label的)
- 然后记roi与其target label的ovlap>TRAIN.FG_THRESH(0.5)的为fg，0.1
if fg_inds.numel() > 0 and bg_inds.numel() > 0:
fg_rois_per_image = min(fg_rois_per_image, fg_inds.numel())
fg_inds = fg_inds[torch.from_numpy(npr.choice(np.arange(0, fg_inds.numel()), size=int(fg_rois_per_image), replace=False)).long().cuda()]
# ......
# 主要解读npr.choice(np.arange(0, fg_inds.numel()), size=int(fg_rois_per_image), replace=False)
# 在np.arange(0, fg_inds.numel())随机取int(fg_rois_per_image)个数，replace=False不允许重复
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roi pooling
上一步得到了很多大小不一的roi，对应到feature map上也是大小不一的，但是fc是需要fixed size的，于是根据SPPNet论文笔记和caffe实现说明，出来了roi pooling(spp poolingfroze 前面的卷积只更新后面的fc，why见fast rcnn的2.3段解释的)
我主要参考了这篇博客Region of interest pooling explained，但是我感觉它的示意图是有问题的，应该有overlap的
1. 我们首先根据feature map和原图的比例，把roi在原图上的坐标映射到feature map上， 然后扣出roi对应部分的feature(蓝色框为实际位置，浮点坐标(1.2,0.8)(7.2,9.7)，四舍五入量化到红色框(1,1)(7,10))
int roi_start_w = round(rois_flat[index_roi + 1] * spatial_scale); // spatial_scale 1/16
int roi_start_h = round(rois_flat[index_roi + 2] * spatial_scale);
int roi_end_w = round(rois_flat[index_roi + 3] * spatial_scale);
int roi_end_h = round(rois_flat[index_roi + 4] * spatial_scale);
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2. 对红色红色框进行roipooling
float bin_size_h = (float)(roi_height) / (float)(pooled_height); // 9/7
float bin_size_w = (float)(roi_width) / (float)(pooled_width); // 7/7=1
for (ph = 0; ph < pooled_height; ++ph){
for (pw = 0; pw < pooled_width; ++pw){
int hstart = (floor((float)(ph) * bin_size_h));
int wstart = (floor((float)(pw) * bin_size_w));
int hend = (ceil((float)(ph + 1) * bin_size_h));
int wend = (ceil((float)(pw + 1) * bin_size_w));
hstart = fminf(fmaxf(hstart + roi_start_h, 0), data_height);
hend = fminf(fmaxf(hend + roi_start_h, 0), data_height);
wstart = fminf(fmaxf(wstart + roi_start_w, 0), data_width);
wend = fminf(fmaxf(wend + roi_start_w, 0), data_width);
// ......
// 经过计算后w步长为1，窗口为1，没有overlap，h窗口步长不定都有overlap，注意在ph=3时窗口为3了
// 注意边界 pw=pooled_width-1时 wend=(ceil((float)(pw + 1) * bin_size_w))
// =(ceil((float)pooled_width * (float)(roi_width) / (float)
// =(pooled_width)))=ceil(roi_width)=roi_width
// 刚好把所有roi对应的feature map覆盖完，hend同理
// roi_height roi_width小于pooled_height pooled_width时overlap就多一点呗
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3. 对每个划分的pool bin进行max或者average pooling最后得到7*7的feature map
分类和回归
roi pooling后就得到fixed size的feature map(7*7)，然后送入cls_score_net得到分类，送入bbox_pred_net粗暴的坐标回归和rpn时一样
self.cls_score_net = nn.Linear(self._fc7_channels, self._num_classes)
self.bbox_pred_net = nn.Linear(self._fc7_channels, self._num_classes * 4)
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Loss采用smooth L1 Loss(和fast rcnn一致，rcnn采用的是L2 Loss)。
prior_centers = center_size(prior_boxes) #(cx, cy, w, h)
gt_centers = center_size(gt_boxes) #(cx, cy, w, h)
# tx=(gx-px)/pw gx是gt的中心坐标x，px是proposal的中心坐标x，pw是预测的宽。ty同理
center_targets = (gt_centers[:, :2] - prior_centers[:, :2]) / prior_centers[:, 2:]
# 参照tw,th的公式
# 加了log, 降低w,h产生的loss的数量级, 让它在loss里占的比重小些, 不至于因为w,h的loss太大而让x,y产生的loss无用
# 因为若是x,y没预测准确, w,h再准确也没有用.
size_targets = torch.log(gt_centers[:, 2:]) - torch.log(prior_centers[:, 2:])
all_targets = torch.cat((center_targets, size_targets), 1)
loss = F.smooth_l1_loss(deltas, all_targets, size_average=False)/(eps + prior_centers.size(0))
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smoothed L1 Loss is a robust L1 loss that is less sensitive to outliers than the L2 loss used in R-CNN and SPPnet.
上述是Fast RCNN解释为什么采用smoothed L1, 因为它对噪音点不那么敏感，即对离目标太远的点不敏感。因为L2loss求导后 0.5*(t-v)^2 求导-> (t-v) 会有一个(t-v) 的系数在，如果v离t太远梯度很容易爆炸(需要精致地调节学习率)，而smoothed L1中当|t-v|>1, |t-v|-0.5 求导-> 系数是±1, 这样就避免了梯度爆炸, 也就是它更加鲁棒。(t是target，v是需要预测出来的中心xy和尺寸wh)
测试
继续假设全部类别数是20种
1. 图片送入网络后前传，没有给anchor proposal指定gt的部分(忽略_anchor_target_layer _proposal_target_layer)
2. 经过proposal得到300个roi，经过cls_score_net bbox_pred_net得到每个roi在20个类别的置信度和4个坐标回归值(可在测试时把这个回归值用上，也可以不用)
3. 测试时300个roi类别未知，所以可以对应20个类别，即有300*20个box，300*20个置信度
3. 对每一类，取300个roi>thresh(默认为0.)，然后进行nms获得留下的box
4. 然后对20类留下的所有box，按概率排序，留下设定的max_per_image个box
有个不解就是为什么对于每个roi，不是选择其置信度最大的类别，而可以对应到20种类别，可能是map算法，同等置信度下，多一些box得分会高一些
for j in range(1, imdb.num_classes):
inds = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[inds, j]
cls_boxes = boxes[inds, j*4:(j+1)*4]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(torch.from_numpy(cls_dets), cfg.TEST.NMS).numpy() if cls_dets.size > 0 else []
cls_dets = cls_dets[keep, :]
all_boxes[j][i] = cls_dets
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延伸
验证一下nms在训练时是不是必须的
参考An Implementation of Faster RCNN with Study for Region Sampling
• First, take the top K regions according to RPN score.
• Then, non-maximal suppression (NMS) with overlapping ratio of 0.7 is applied to perform de-duplication.
• Third, top k regions are selected as RoIs.
Intuitively, it is more likely for large regions to overlap than small regions, so large regions have a higher chance to be suppressed对这句话保留意见，nms算的是iou，没有偏向抑制大的region吧
ALL是top12000 proposal都送入后面的网络，不进行nms PRE是利用第一行已经训练好的faster rcnn直接得到最终的正负样本比例 POW: 比例和scale成反比，详细见文章。TOP是test是选择top 5000不进行nms(faster rcnn本身是选择top 6000然后nms，最后再取top300)
In fact, we find this advantage of TOP over NMS consistently exists when K is sufficiently large.
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爆米花好美啊
非学，无以致疑；非问，无以广识- 擅长领域：
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FasterRCNN代码解读
01-24 5845
之前的文章简要介绍了Faster-RCNN等物体检测的算法，本文将从代码角度详细分析介绍Faster-RCNN的实现。本文使用的代码参考了chenyuntc的实现，代码的位置看这里。需要注意的是，本文... 来自： Young_Gy
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<div class="comment-list-box"><ul class="comment-list"><li class="comment-line-box d-flex" data-commentid="8536619" data-replyname="weixin_31866177"> <a target="_blank" href="https://me.csdn/weixin_31866177"><img src="https://avatar.csdn/B/5/7/3_weixin_31866177.jpg" alt="weixin_31866177" class="avatar"></a> <div class="right-box "> <div class="info-box"> <a target="_blank" href="https://me.csdn/weixin_31866177"><span class="name ">很美味的小猪猪：</span></a> <span class="comment">请问博主，设置128,256,512的依据是什么呢？ 是因为在voc2007中图片resize之后基本大小都是800*600，所以这几个面积可以涵盖几乎所有的目标吗？那么理论上我改成200,400,600可以吗？谢谢</span><span class="date" title="2018-10-07 15:49:26">(2个月前</span><span class="floor-num">#5楼)</span><span class="opt-box"><a class="btn btn-link-blue btn-read-reply" data-type="readreply">查看回复(1)</a><a class="btn btn-link-blue btn-report" data-type="report">举报</a><a class="btn btn-link-blue btn-reply" data-type="reply">回复</a></span></div> </div> </li><li class="replay-box"><ul class="comment-list"><li class="comment-line-box d-flex" data-commentid="8540085" data-replyname="u013010889"> <a target="_blank" href="https://me.csdn/u013010889"><img src="https://avatar.csdn/2/9/4/3_u013010889.jpg" alt="u013010889" class="avatar"></a> <div class="right-box reply-box"> <div class="info-box"> <a target="_blank" href="https://me.csdn/u013010889"><span class="name mr-8">爆米花好美啊</span></a>回复 <span class="name">很美味的小猪猪：</span> <span class="comment">anchor大小根据你的数据库中目标大小进行设置就好</span><span class="date" title="2018-10-08 17:16:49">(2个月前</span>)<span class="opt-box"><a class="btn btn-link-blue btn-report" data-type="report">举报</a><a class="btn btn-link-blue btn-reply" data-type="reply">回复</a></span></div> </div> </li></ul></li></ul><ul class="comment-list"><li class="comment-line-box d-flex" data-commentid="8203178" data-replyname="weixin_38952721"> <a target="_blank" href="https://me.csdn/weixin_38952721"><img src="https://avatar.csdn/D/5/1/3_weixin_38952721.jpg" alt="weixin_38952721" class="avatar"></a> <div class="right-box "> <div class="info-box"> <a target="_blank" href="https://me.csdn/weixin_38952721"><span class="name ">weixin_38952721：</span></a> <span class="comment">对应的代码在哪个位置，作者能标出来吗，完全找不到对应的代码在哪个位置</span><span class="date" title="2018-07-11 13:40:17">(5个月前</span><span class="floor-num">#4楼)</span><span class="opt-box"><a class="btn btn-link-blue btn-read-reply" data-type="readreply">查看回复(1)</a><a class="btn btn-link-blue btn-report" data-type="report">举报</a><a class="btn btn-link-blue btn-reply" data-type="reply">回复</a></span></div> </div> </li><li class="replay-box"><ul class="comment-list"><li class="comment-line-box d-flex" data-commentid="8204999" data-replyname="u013010889"> <a target="_blank" href="https://me.csdn/u013010889"><img src="https://avatar.csdn/2/9/4/3_u013010889.jpg" alt="u013010889" class="avatar"></a> <div class="right-box reply-box"> <div class="info-box"> <a target="_blank" href="https://me.csdn/u013010889"><span class="name mr-8">爆米花好美啊</span></a>回复 <span class="name">weixin_38952721：</span> <span class="comment">绝大部分代码都来自于https://github/ruotianluo/pytorch-faster-rcnn，你可以搜索一下</span><span class="date" title="2018-07-11 19:11:47">(5个月前</span>)<span class="opt-box"><a class="btn btn-link-blue btn-report" data-type="report">举报</a><a class="btn btn-link-blue btn-reply" data-type="reply">回复</a></span></div> </div> </li></ul></li></ul><ul class="comment-list"><li class="comment-line-box d-flex" data-commentid="7842589" data-replyname="huashaopan"> <a target="_blank" href="https://me.csdn/huashaopan"><img src="https://avatar.csdn/8/3/5/3_huashaopan.jpg" alt="huashaopan" class="avatar"></a> <div class="right-box "> <div class="info-box"> <a target="_blank" href="https://me.csdn/huashaopan"><span class="name ">PanSH2016：</span></a> <span class="comment">@有道云笔记</span><span class="date" title="2018-04-19 00:48:13">(8个月前</span><span class="floor-num">#3楼)</span><span class="opt-box"><a class="btn btn-link-blue btn-report" data-type="report">举报</a><a class="btn btn-link-blue btn-reply" data-type="reply">回复</a></span></div> </div> </li></ul><ul class="comment-list"><li class="comment-line-box d-flex" data-commentid="7700610" data-replyname="u012457308"> <a target="_blank" href="https://me.csdn/u012457308"><img src="https://avatar.csdn/1/5/5/3_u012457308.jpg" alt="u012457308" class="avatar"></a> <div class="right-box "> <div class="info-box"> <a target="_blank" href="https://me.csdn/u012457308"><span class="name ">RuibinBai：</span></a> <span class="comment">roi pooling 部分："我们首先根据roi的位置映射到原图，然后根据feature map和原图的比例 ...." roi pooling不是应该把roi在原图的坐标映射到feature map， 然后在feature map上做pooling吗？</span><span class="date" title="2018-03-16 13:19:35">(9个月前</span><span class="floor-num">#2楼)</span><span class="opt-box"><a class="btn btn-link-blue btn-read-reply" data-type="readreply">查看回复(1)</a><a class="btn btn-link-blue btn-report" data-type="report">举报</a><a class="btn btn-link-blue btn-reply" data-type="reply">回复</a></span></div> </div> </li><li class="replay-box"><ul class="comment-list"><li class="comment-line-box d-flex" data-commentid="7701519" data-replyname="u013010889"> <a target="_blank" href="https://me.csdn/u013010889"><img src="https://avatar.csdn/2/9/4/3_u013010889.jpg" alt="u013010889" class="avatar"></a> <div class="right-box reply-box"> <div class="info-box"> <a target="_blank" href="https://me.csdn/u013010889"><span class="name mr-8">爆米花好美啊</span></a>回复 <span class="name">RuibinBai：</span> <span class="comment">是的 我这里表述不清楚，谢谢指正，已修改
ps 你在引用我图片时还给了link，不错 哈哈(9个月前)举报回复
- jack_tkk： 请问博主，为什么你在文中说：“train阶段的某个图片为例即该张图片有3000个proposal”，NMS后不是只留下了2000proposal，为什么该张图片中有3000个呢？ (1年前 #1楼) 查看回复(1)举报回复
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- 爆米花好美啊回复 jack_tkk： 笔误，已改正，谢谢！ (1年前) 举报回复
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Faster RCNN代码详解（一）：算法整体结构
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本系列博客介绍Faster RCNN算法的细节，以MXNet框架的代码为例。希望可以通过该系列博客让更多同学了解Faster RCNN算法中关于RPN网络的构建、损失函数的定义、正负样本的定义等细节，... 来自： AI之路
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<span class="desc oneline">Mask R-CNN
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针对问题：object instance Segmentation
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<span class="desc oneline">该版本为tensorflow+keras版本的，官方版本刚开源10个小时（caffe2），以后再更新..
一、工具
cuda与cudnn安装请参考我之前博客：
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PASCAL VOC为图像识别和分类提供了一整套标准化的优秀的数据集，从2005年到2012年每年都会举行一场图像识别challenge。
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<span class="desc oneline">转载自：http://blog.csdn/sunyiyou9/article/details/52207486
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<a href="https://blog.csdn/Jason__sz/article/details/79179107" target="_blank" title="faster-rcnn中的nms代码解读">
<span class="desc oneline">最近在读faster-rcnn的代码，感觉理解透还是比较难的，需要将思路整理一下。下面将简单梳理一下nms部分的代码思路。
代码如下：
import numpy as np
def py…
来自： Jason__sz的博客
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<span class="desc oneline">目录
-
总函数 generate_anchors
-
函数分功能写，首先是ratios的实现，其次是scale的实现
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anchor2WHXY函数+WsHsXsYs2anchors函…
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基于TensorFlow的<em>Faster</em>_R-CNN<em>源码</em> </h4>
<span class="data float-right">08-15</span>
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基于TensorFlow的Faster R-CNN源码
目录结构
----data
----experiments
----faster_rcnn
----libs
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<span class="desc oneline">一. 论文
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
官方版本github …
来自： Mmm_Zzz的博客
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<span class="desc oneline">读论文：《Faster R-CNN: Towards Real-Time ObjectDetection with Region Proposal Networks》
摘要：算法主要解决两个问题：…
来自： hunterlew的专栏
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<span class="desc oneline">在前面几篇博文中基本上将Faster RCNN的算法细节都介绍完了，这一篇博客主要来介绍评价函数。自定义评价函数有助于我们灵活观察模型训练过程中的变量变化情况，比如RPN网络中的分类和回归损失在每次迭...</span>
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<span class="desc oneline">Keras版Faster-RCNN代码学习（IOU，RPN）1
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<span class="desc oneline">Faster RCNN github : https://github/rbgirshick/py-faster-rcnnFaster RCNN paper : https://arxiv.o...</span>
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<span class="desc oneline">Faster R-CNN是一种object detection算法，这里记录下Faster R-CNN的Caffe实现。
git地址：https://github/rbgirshick/py…
来自： AI之路
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<span class="desc oneline">上一篇中介绍faster rcnn，这次mask 基本在上次的基础上加了点代码，参考和引用1. mask rcnn slides 2. kaiming he maskrcnn 3. Ardian ...</span>
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本文主要通过通过keras版本的代码来讲解:https://github/yhenon/keras-frcnn
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faster-rcnn的原理
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<span class="desc oneline">对于我喜欢和常用的网站我会特别介绍一下的，其他大家自己看着办吧。
还有一点，一般专门的BT论坛会比这些搜索引擎收录快，发布站式的BT站也会比抓取的搜索引擎收录快。BT发布站我也会另外开的，这里偶尔带…
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<span class="desc oneline">知识点
知识点–Python-文件操作、异常、模块
文件操作
1>打开文件在python，使用open函数，可以打开一个已经存在的文件，或者创建一个新文件
open(文件…
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<span class="desc oneline">
现在越来越流行在线看视频了，但是对于我得收藏癖爱好者，还是希望可以有比较好的资源网站的，尤其是种子、磁力链网站。所以就整理了一份干净、好用的TOP10出来：
先推荐一个下载磁力链的工具：
马…
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安装完成，打开Webstorm，在弹出的License Activation窗口中选择“License server”，在输入框输入下面的网址：
htt…
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<span class="desc oneline">大家好，我是小九
最近公司的一个小程序项目要用到统计类折线图
于是准备用wx-charts来做这个趋势图
首先从gethub上或者从码云上下载wx-charts.js,传送门:https…
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<span class="desc oneline">需要电子档书籍或者源码可以Q群：828202939 希望可以和大家一起学习、一起进步！！
如有错别字或有理解不到位的地方，可以留言或者加微信15250969798，博主会及时修改！！！！！
最…
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$ git config --global alias.st status
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来自： 老K的博客
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（七）RabbitMQ消息队列-通过fanout模式将消息推送到多个Queue中 </h4>
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<span class="date hover-show">04-26</span>
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<a href="https://blog.csdn/Super_RD/article/details/70802894" target="_blank" title="（七）RabbitMQ消息队列-通过fanout模式将消息推送到多个Queue中">
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有时我们会遇到这样的情况，多个功能模块都希望得到完整的消息数据。例如一个lo…
来自： Super_RD的博客
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（十四）RabbitMQ消息队列-启用SSL安全通讯 </h4>
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<span class="date hover-show">05-08</span>
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<a href="https://blog.csdn/Super_RD/article/details/71403800" target="_blank" title="（十四）RabbitMQ消息队列-启用SSL安全通讯">
<span class="desc oneline">如果RabbitMQ服务在内网中，只有内网的应用连接，我们认为这些连接都是安全的，但是个别情况我们需要让RabbitMQ对外提供服务。这种情况有两种解决方案：
在RabbitMQ外层在封装一层应用，应…
来自： Super_RD的博客
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<span class="date hover-show">08-15</span>
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<a href="https://blog.csdn/weixin_39452320/article/details/81699290" target="_blank" title="项目部分展示-3D在线试衣系统解决方案">
<span class="desc oneline">郑重声明：发布此博客纯属技术展示和交流！未得本人同意，禁止转载！禁止商业目的！
需要电子档书籍可以Q群：828202939 希望可以和大家一起学习、一起进步！！
部分项目展示： 因为转化…
来自： 谷子的博客
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<span class="date hover-show">12-16</span>
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<a href="https://blog.csdn/ling_wang/article/details/78821630" target="_blank" title="军事理论课答案（西安交大版）">
<span class="desc oneline">1.1
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【单选题】我国陆地领土面积排名世界第几？（C）
A、1
B、2
C、3
D、4
2
【单选题】以下哪个国家不属于金砖五国（BRICS）？（B）
A、中国
B、日本
C…
来自： ling_wang的博客
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<span class="date hover-show">05-17</span>
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<a href="https://blog.csdn/shadandeajian/article/details/80349939" target="_blank" title="Proxyee-down的下载与安装教程">
<span class="desc oneline">Proxyee-down是monkeyWie在Github上的一个开源项目，向作者致敬。
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来自： shadandeajian的博客
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<span class="date hover-show">06-09</span>
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<a href="https://blog.csdn/qq_28735663/article/details/80634300" target="_blank" title="最新最详细的黑苹果10.13.4安装教程">
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<span class="date hover-show">08-01</span>
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<span class="desc oneline">链接：https://pan.baidu/s/1Ww1rGly81_b40B__tBeYFQ 密码：6naq</span>
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<span class="date hover-show">12-17</span>
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<span class="desc oneline">第一步上github导项目
BugIy热更新-项目
BugIy官网热更新-教材
如果你想深入了解BugIy我推荐你从官网学习，但是和我一样只为了初步了解一下怎么使用可以看看我的操作流程
下载好…
来自： 齐码闯天涯
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2018最好用百度云破解版，百度网盘不限速下载，教你如何解决百度网盘限速的方法。亲测完美使用 </h4>
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<span class="date hover-show">12-06</span>
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<span class="desc oneline">百度网盘不限速 点击下载 提取码：jsk0
百度网盘不限速 点击下载 提取码：jsk0
对于大多数人来说，每次在百度网盘下载东西的时候总会被限速，如果不想被限速就要充值百度网盘的SVIP，…
来自： qq_41925894的博客
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Android 自定义View（inflate()模式） </h4>
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<span class="date hover-show">12-18</span>
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<a href="https://blog.csdn/qq_41816123/article/details/85067360" target="_blank" title="Android 自定义View（inflate()模式）">
<span class="desc oneline">1.创建LayoutInflater实例
有两种方式
1.
LayoutInflater layoutInflater= LayoutInflater.from(MainActivity.thi…
来自： 齐码闯天涯
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Json解析-和风天气 </h4>
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<span class="date hover-show">12-07</span>
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<a href="https://blog.csdn/qq_41816123/article/details/84873672" target="_blank" title="Json解析-和风天气">
<span class="desc oneline">接口怎么调用呢，横简单
这里说一下怎么转json为对象
老规矩看转载
教学文章
有啥疑问可以问我，这文章里面还有个很好用的工具
…
来自： 齐码闯天涯
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<span class="desc oneline">今天电脑开机出现一个问题,上面显示这样一段文字"Windows未能启动，原因可能是最近更改了硬件或软件，解决此问题的步骤"
,然后自己上网找方法,网上无非就是重装系统,然后就只能自己捣鼓了,然后发现…
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这里介绍最简单的获得方式
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<span class="desc oneline">需要电子档书籍可以Q群：828202939 希望可以和大家一起学习、一起进步！！纯手打！！
上一节课我们学习了动画的基础，是一个不断旋转的三角形
这一节课我们将学习复合变换动画，以此来巩固前面…
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下载OTP工具
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…
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