Comments (5)
darknet: l.delta[obj_index] = 0 - l.output[obj_index];
caffe-yolo9000: diff[index + 4] = -1 * noobject_scale_ * (0 - swap_data[index + 4]) * (swap_data[index + 4]) * (1 - swap_data[index + 4]);
按我理解,这部分是是否是前景的loss,这个loss就是L2 loss,求导应该是它本身才对,就像darknet一样,不明白为什么你的偏导会这么复杂,希望得到你的帮助,谢谢
from caffe-yolo9000.
caffe-yolo9000: diff[index + 4] = -1 * noobject_scale_ * (0 - swap_data[index + 4]) * (swap_data[index + 4]) * (1 - swap_data[index + 4]);
due to
p(obj) = sigmoid(swap_data[index + 4])
from caffe-yolo9000.
哦,后面两项时sigmoid的导数,谢谢。
//if (iter < 12800 / bottom[0]->num()){
// vector truth;
// truth.clear();
// truth.push_back((i + .5) / side_); //center of i,j
// truth.push_back((j + .5) / side_);
// truth.push_back((biases_[2 * n]) / side_); //anchor boxes
// truth.push_back((biases_[2 * n + 1]) / side_);
// delta_region_box(truth, swap_data, biases_, n, index, i, j, side_, side_, diff, .01);
//}
再请教个问题,这部分代码是什么意思,darknet是有求delta_region_box,delta_region_class的。
from caffe-yolo9000.
just like warm-up ... you can refer to darknet.
from caffe-yolo9000.
明白了,谢谢
from caffe-yolo9000.
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from caffe-yolo9000.