Closed mender05 closed 9 years ago
Hi, have you solved this problem? I also follow to use AlexNet to compute the coordinates of the body joints and get the same results. However, when I use a simple Network like LeNet, it performs better and can produce reasonable results. So, I guess AlexNet is too deep, but I still do not know why the paper can achieve this with AlexNet.
I got the same problem while I was implementing the DeepPose. Then I read every comment and found them all really helpful. I've changed my model deploy for many times according to many of these nice advice and finally the problem disappeared. Because of so many changes, I can't say which particular operation is the key and make things work. But I really suggest you, my friends who still suffer from the problem, try these advice no matter they work or not. Thank you all !
I try to ues caffe to implement the DeepPose proposed in this paper: http://arxiv.org/abs/1312.4659 DeepPose has 3 stages. And each stage is almost the same as AlexNet (DeepPose changes the loss layer in AlexNet to euclidean loss). It is a regression problem in fact.
The train.prototxt is:
The solve.prototxt is:
After trainning completed, I use python interface to do prediction on testset. The test.prototxt is:
but the output is very strange. Dumpping the output of "fc8" layer, I find that all the images produce the same output:
In fact, no mater what the inputs are, the outputs are always the same with the values above. How the problem caused?