faster rcnn最后的误差是rpn Loss和fast rcnn Loss之和。 rpn Loss和fast rcnn Loss的前向传播过程基本一致,它们都包含分类误差和定位误差,分类误差使用交叉熵分类误差(CrossEntropy),定位误差使用Smooth L1误差。 在Loss传播过程中,有一个比较关键的地方是,如何将网络的预测与真实地面框相联系起来,这也是误差计算...
'loss_loc_0': '0.004803', 'loss_cls_1': '2.924091', 'loss_loc_1': '0.001301', 'loss_cls_2': '2.735808', 'loss_loc_2': '0.001601', 'loss_rpn_cls': '30.671740', 'loss_rpn_bbox': '0.021397', 'loss': '43.600033', eta: 0:00:00, batch_cost: 0.00000 sec, ips: inf...
、、、 [11/29 20:16:31 d2.utils.events]: eta: 0:24:04 iter: 19 total_loss: 9.6 loss_cls: 1.5 loss_box_reg: 0.001034 loss_mask: 0.6936 loss_rpn_cls: 6.773 loss_rpn_loc: 0.5983 time: 1.4664 data_time: 0.0702 浏览11提问于2021-11-30得票数 7 回答已采纳 1回答 为什么我的误差图...
、、、 [11/29 20:16:31 d2.utils.events]: eta: 0:24:04 iter: 19 total_loss: 9.6 loss_cls: 1.5 loss_box_reg: 0.001034 loss_mask: 0.6936 loss_rpn_cls: 6.773 loss_rpn_loc: 0.5983 time: 1.4664 data_time: 0.0702 浏览11提问于2021-11-30得票数 7 回答已采纳 1回答 为什么我的误差图...
host.set_ylabel("RPN loss") #par1.set_ylabel("validation accuracy") # plot curves p1, = host.plot(train_iterations, train_loss, label="train RPN loss") . host.legend(loc=1) # set label color host.axis["left"].label.set_color(p1.get_color()) ...
p1, = host.plot(train_iterations, train_loss, label="train RPN loss") #p2, = par1.plot(test_iterations, test_accuracy, label="validation accuracy") # set location of the legend, # 1->rightup corner, 2->leftup corner, 3->leftdown corner ...
smooth_l1_loss(rpn_pred_deltas, target_deltas) else: loss = torch.FloatTensor([0]).cuda() return loss Example #4Source File: models.py From distributed_rl with MIT License 6 votes def calc_priorities(self, target_net, transitions, alpha=0.6, gamma=0.999, device=torch.device("cpu"))...
www.nature.com/scientificreports OPEN received: 06 November 2015 accepted: 08 January 2016 Published: 18 February 2016 Abnormal tau induces cognitive impairment through two different mechanisms: synaptic dysfunction and neuronal loss J. Di1,*,#, L. S. Cohen1,*, C. P. Corbo2, G. ...
loss = (loc_loss+cls_loss)/num_posreturnloss 三、OHEM应用到faster RCNN 如何将OHEM(on line hard example/negative mining)用到faster RCNN中? 可以在原始的faster RCNN代码实现中加入了OHEM,值得注意的是,OHEM是在计算RPN的classification loss时使用的,在计算RCNN的classification loss使用的是全部的2000个...
实践中应该要如何调整呢？ 其实很简单：假设我们有两个task, 用A和B表示。 假设网络设计足够好， ...