这是目标检测、定位和跟踪的基础。例如，最高级的目标检测器通常由一个bbox回归分支和一个分类分支组成...
百度试题 结果1 题目RPN中reg loss,即rpn_loss_bbox层计算的softmax loss,用于bounding box regression网络训练。 A. 正确 B. 错误 相关知识点: 试题来源: 解析 B 反馈 收藏
_anchor_targets['rpn_bbox_outside_weights'] rpn_loss_box = self._smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights, sigma=sigma_rpn, dim=[1, 2, 3]) # RCNN, class loss cls_score = self._predictions["cls_score"] label = tf....
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loss_rpn_cls、loss_rpn_bbox和loss都在下降,但loss_cls和loss_bbox一直为0,eval的结果也是map为0。如下 2020-10-19 10:11:44,500-INFO: Start evaluate...2020-10-19 10:11:44,501-INFO: Accumulating evaluatation results...2020-10-19 10:11:44,501-INFO: mAP(0.50, 11point) = 0.00%2020-...
回归损失只计算对应的区域是目标label>0的损失,这个是通过参数rpn_bbox_inside_weights和bbox_inside_weights实现的,前者只把rpn的anchor中label=1为前景的权重设为1,其他的为0;后者把rcnn中roi的label>0的目标的权重设为1,其实对应的是这一项里面的pi*.至于剩下的参数λ和Nreg论文和代码有出入。先说自己的理...
rpn_head: FPNRPNHead roi_extractor: FPNRoIAlign bbox_head: CascadeBBoxHead bbox_assigner: CascadeBBoxAssigner ResNet: norm_type: bn depth: 50 feature_maps: [2, 3, 4, 5] freeze_at: 2 variant: d dcn_v2_stages: [3, 4, 5] ...
rpn_bbox_inside_weights = rpn_data[2] rpn_bbox_outside_weights = rpn_data[3] rpn_bbox_pred = tf.gather(tf.reshape(rpn_bbox_pred, [-1, 4]), rpn_keep) # shape (N, 4) rpn_bbox_targets = tf.gather(tf.reshape(rpn_bbox_targets, [-1, 4]), rpn_keep) ...
CTPN通过CNN和BLSTM学到一组“空间 + 序列”特征后,在"FC"卷积层后接入RPN网络。这里的RPN与Faster R-CNN类似,分为两个分支: 左边分支用于Bounding Box Regression。由于FC Feature map每个点配备了10个Anchor,同时只回归中心y坐标与...
I0530 08:54:19.183145 10143 solver.cpp:245] Train net output #1: rpn_loss_bbox = 0.071999 (* 1 = 0.071999 loss) I0530 08:54:19.183148 10143 sgd_solver.cpp:106] Iteration 22000, lr = 0.001 通过发现,我们只需获得 Iteration 和loss就行 ...