From a358e318af713f7de97782e500c6125b15f9fec6 Mon Sep 17 00:00:00 2001 From: tink2123 Date: Fri, 12 Apr 2019 09:16:01 +0000 Subject: [PATCH] polish yolov3 loss annotation test=develop --- paddle/fluid/operators/detection/yolov3_loss_op.cc | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/paddle/fluid/operators/detection/yolov3_loss_op.cc b/paddle/fluid/operators/detection/yolov3_loss_op.cc index 6c37da17f4..e8a186b611 100644 --- a/paddle/fluid/operators/detection/yolov3_loss_op.cc +++ b/paddle/fluid/operators/detection/yolov3_loss_op.cc @@ -171,8 +171,8 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker { The output of previous network is in shape [N, C, H, W], while H and W should be the same, H and W specify the grid size, each grid point predict - given number boxes, this given number, which following will be represented as S, - is specified by the number of anchors, In the second dimension(the channel + given number bounding boxes, this given number, which following will be represented as S, + is specified by the number of anchor clusters in each scale. In the second dimension(the channel dimension), C should be equal to S * (class_num + 5), class_num is the object category number of source dataset(such as 80 in coco dataset), so in the second(channel) dimension, apart from 4 box location coordinates x, y, w, h, @@ -203,7 +203,7 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker { thresh, the confidence score loss of this anchor box will be ignored. Therefore, the yolov3 loss consist of three major parts, box location loss, - confidence score loss, and classification loss. The L2 loss is used for + confidence score loss, and classification loss. The L1 loss is used for box coordinates (w, h), and sigmoid cross entropy loss is used for box coordinates (x, y), confidence score loss and classification loss. -- GitLab