Comments (8)
Or we could start from FAST RCNN and then denote the changes required for Faster RCNN
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@athus1990 Can you elaborate? I have taken a leap of faith to faster rcnn without working with fast rcnn, I took a look at the paper. The experiments there also initializes from pretrained networks right? Is it only for time concern (or well better objectness)? Do you suggest that I train a fast rcnn model and then add the layers introduced in faster rcnn using the weights from the initial fast rcnn? Or can I just use do_proposal_train and then do_fast_rcnn_train with those proposals for 1st and 2nd stages? - edit:that is where I stumble do_proposal_train initializes with the pretrained models, trying to go over proposal_train to see
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update: the vgg16 on imagenet comes from caffe model zoo so I think it is this one: https://gist.github.com/ksimonyan/211839e770f7b538e2d8 The ZF one is said to be trained on MSRA, I think the architecture should be Zeiler & Fergus paper they refer to, so it seems like they are not random models. However since I have one class and it is not any of the objects in those datasets I need to somehow skip initializing
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@duygusar
May I know how you deal with the image mean part? So for my own data set, do we need to compute the image means of our own images? Do we still need to normalize the input?
Thanks a lot!
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@Astrosun
You don't "have to" but it is recommended that you do for better performance. I have written a script to calculate the mean of all training data and I used that. To be honest, since the training dataset is big, it doesn't make much difference if you assign just some average numbers, around say 100 to 120 for each channel (rgb). It does help when you subtract it from the image, so at least use those random numbers even if you don't calculate the mean. Well take this advice with a grain of salt if you have a peculiar dataset :)
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Thanks so much for your valuable advice and it will be the greatest help!
By the way, one more question, when you calculate the mean of your training data using your own script, do you add them up together then calculate the average value? I am wondering that whether it will cost a lot of time?
Thanks again!
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@Astrosun
It was a long time ago, so I don't exactly remember but I did read in a list on images so I must have done it with a loop, since it is a simple computation, it didn't take that long. You also need to change the order of rgb and reshape the output though.
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Thanks a lot for your valuable advice and I will try it!
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Related Issues (20)
- demo Check failed HOT 3
- No detection on "training image" with a trained net
- GPU memory is not enough(2g),how to deal with it
- A compiling problem with Win10+VS2017+MATLAB R2016b+CUDA9.1: undefined reference
- the error when training with VOC2007 HOT 2
- Error when compiling nms_gpu_mex
- Error '_MSC_VER': value '1600' doesn't match value '1800' in nms_gpu_mex.o running faster_rcnn_build.m file HOT 1
- training faster rcnn on ROI only ?
- Fast RCNN loss doesn't change when trained with my own dateset. Not fine tuned on pretrained model
- error at hNet = caffe_('get_net', model_file, phase_name); HOT 1
- runtime question
- Some problems when i train VOC2007 database HOT 1
- I just finished demo and try to train with pascal_voc dataset. (1) If I command /py-faster-rcnn/experiments/scripts/faster_rcnn_alt_opt.sh 0 ZF pascal_voc ./faster_rcnn_alt_opt.sh: line 46: ./tools/train_faster_rcnn_alt_opt.py: No such file or directory above error message is shown up. HOT 1
- error when run faster rcnn in (stage one of fast rcnn ) win10, GPU 1080 TI, Matlab 2018a ; i RUN demo without error ; any solve ? HOT 1
- Is there validatoin set in faster_rcnn ?
- Concatenate a fixed value to fc7 features
- how can i train faster R-CNN using a new customised network model?
- Failed when running 'faster_rcnn_build.m' HOT 1
- What is the mean image?
- Loss is not changing
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