liuyuisanai / rsa-for-object-detection Goto Github PK

1. 论文中提到使用half-channel resnet18作为lrn net，在代码中并没有看到half-channel，两者在精度上相差多少
2. 带有关键点的widerface训练集是你们内部没开源的数据集么
3. 我使用多scale[2048~64]的原图去训练lrn，再基于训练好的lrn第一部分网络训练rsa，这种思路对么，可否告知论文中提到的end-to-end训练方式
4. 如果使用box作为groudtruth代替keypoint，影响大么

望大神解答！多谢！

@ScienceFans @liyuanyaun Could you please send me the train code? I want use RSA in my own DataSet.
My email is [email protected]

@ScienceFans :
Dear sir,
in your readme you have written that
train/: Training code for modules scale-forecast network and RSA

but the train folder itself is missing on your repository, either you forgot to upload OR its not public.

My speed:

>> script_start
Cleared 1 solvers and 0 stand-alone nets
Step 1 fcn: 1/4...时间已过 0.255855 秒。
Step 1 fcn: 2/4...时间已过 0.317631 秒。
Step 1 fcn: 3/4...时间已过 0.167352 秒。
Step 1 fcn: 4/4...时间已过 0.322786 秒。
Cleared 1 solvers and 0 stand-alone nets
Step 2 rsa:  1/4...时间已过 0.064430 秒。
Step 2 rsa:  2/4...时间已过 0.057944 秒。
Step 2 rsa:  3/4...时间已过 0.039679 秒。
Step 2 rsa:  4/4...时间已过 0.055250 秒。
Cleared 1 solvers and 0 stand-alone nets
Step 3 rpn:  1/4...时间已过 0.339639 秒。
Step 3 rpn:  2/4...时间已过 0.431017 秒。
Step 3 rpn:  3/4...时间已过 0.279860 秒。
Step 3 rpn:  4/4...时间已过 0.278757 秒。

Speed in comments:

script_gen_featmap; % GPU runtime: 5ms per pic on Titan Xp @2048px
script_featmap_transfer; % GPU runtime: 0.3ms per pic on Titan Xp
script_featmap_2_result; % GPU runtime: 3.2ms per pic on Titan Xp

My speed is 100x slower than the comments. My graphic card is GTX 1080. I think there should not be so much difference. Could anyone help?

Hi @ScienceFans
thank you for your work and paper
Do you know any approximate date to release your code because I want to look into and try it.

Regards,
Anand

(1)in RSA section of your paper, i do not understand This paragraph："During inference, we first have the possible scales of the input from the scale-forecast network. The image is then resized accordingly to the extent that the smallest scale (corresponding to the largest feature map) is resized to the range of [64; 128]."
is "the smallest scale" of faces or image? and do you mean that resize operation is to ensure "the smallest scale" in the range of [64; 128].?

(2)for Scale forecast Network in figure 3 of you paper, when it output 28 in [20,30] which means the size of face is in the range of [128,256], corresponding m is equal to 2. this means after RSA, the size of the face is in range of [32,64]. and when it output 35 in [30,40] which means the size of is in the range of [256,512], corresponding m is equal to 3. this means after RSA, the size of the face is in range of [32,64],too. so RSA is to ensure the size of face in the range of [32,64]?

look forward your answers, thanks!