imatge-upc / detection-2016-nipsws

Hierarchical Object Detection with Deep Reinforcement Learning
http://imatge-upc.github.io/detection-2016-nipsws/
MIT License
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about weights of model_vgg #19

Open ghost opened 6 years ago

ghost commented 6 years ago

Thank you for your code. I have two questions.

  1. Is the vgg16_weights.h5, which downloaded from the link provided by you, pretrained on ImageNet?
  2. It seems that the model_vgg is trained at every epoch, but do not be saved. At test time, the weights of model_vgg is still the Initial weights(i.e. vgg16_weights.h5). Why not save it and use it at test time?
mani5h commented 6 years ago

Yes, pretrained CNN is used here,using pretrained cnn (vgg16 here) has 2 benefits:First, learning the Q function is faster because we need to up-date the parameters of the Q-Network only, while using the deep CNN just as a feed-forward feature extractor. Second, the hierarchy of features is trained with a larger dataset,leveraging generic discriminative features in this method.

zoupei123456 commented 6 years ago

Have you made any changes to the data set voc2012?Why does this error occur when I run the program? xml.etree.ElementTree.parseError:not well-formed(invalid token):line1,column 0。Could you tell me how to solve this problem?

XiaoYigwr commented 6 years ago

Hi,there is a problem that makes me very confused, in the DQN model of the paper, Why did you use 10241024 full connection, instead of 40964096,or other dimensions. Does 1024*1024 have any special meaning?

zhongkedazhouman commented 5 years ago
thank you! i have a question. It seems that the model_vgg is trained at every epoch, but do not be saved. At test time, the weights of model_vgg is still the Initial weights(i.e. vgg16_weights.h5). Why not save it and use it at test time?