1adrianb / binary-face-alignment Goto Github PK

Hello!

How convert this model to pytorch or other popular frameworks (caffe, keras, tensorflow)? Opensource converters and pytorch utils can not do this.

I followed the instructions in the README.md file and this error appears:

jgoenetxea@Trantor:~/git/binary-face-alignment$ cd bnn.torch/; luarocks make; cd ..;
Error: Argument missing: please specify a rockspec to use on current directory.

I am new with lua, and I do not know how to fix this.

I was trying to convert this model into CPU supported device(nn), was able to convert some of the layers using cudnn.convert, but getting stuck at layers from bnn.torch, has anyone converted bnn layers to nn or cudnn.
any help would be appreciated.
Thank you

I draw the net structure from your torch model, but i am not sure correctness.
Can share your net structure?
net structure
NOTE: I didn't add binary layer

Is it possible to plot/display the bounding box around the face according with the test data's scale and center?

Thank you.

Hello Adrian,
Do you have any plans for publishing the training code for the binary face alignment model?
Thank you.

When I run in mode=eval mode, main.lua reports an error when it executes utils.calcDistance(predictions,groundTruth). Parsing groundTruth from your code is fileLists in main.lua. . . . The data structure of filesLists does not contain the points member variable. . . . . . So, where is the groundTruth in eval?

(perhaps this is a better place to ask than 1adrianb/bnn.torch#2? Sorry for the duplicate!)

Firstly, thank you so much for releasing your code, it helps a lot in understanding your awesome paper!

If I understand the code correctly, for face-alignment, if I want to do everything from the scratch, I have to first train the floating point model using your codebase (https://github.com/1adrianb/face-alignment-training). However, I am missing the link of how you convert this model to the binary model that is available in https://github.com/1adrianb/binary-face-alignment which uses bnn.BinarySpatialConvolution

It seems to me that I need to call the function BinarySpatialConvolution:binarise(convLayer) somehow, but I am a bit confused. Do you have a script which replaces the floating conv modules with their binary counterparts? Do you also train in binary?

I will be very grateful if you can help me out, thanks a lot in advance.

I have run your code with the samples under the floder /dataset/AFLW2000/ . But I find that each '.jpg' image file contains corresponding '.t7' file. But when I process a new image how can I generate '.t7' file. I have seen the 'utils.lua' under the 'binary-face-alignment/' directory, the corresponding '.t7' contains scale and center. What does it mean when I do face alignment? I have no idea regarding the above. Thanks very much!

https://www.adrianbulat.com/downloads/datasets/AFLWPIFA-val.tar, seems this resource is not existed.

Hello, I tried to train the binary network in the way of the paper XNOR-Net （https://github.com/allenai/XNOR-Net), however, the training result is very poor, the structure of my model is the same as the pretrained model you give ,and the training parameters are based on the parameters given in the paper. I used BCECriterion, the loss is always around 0.027 when i trained more then 47 epoch, it doesn't drop anymore, and the prediction is poor too. I‘m very confused why the result is so poor ,could you tell me some training details？ Thank you very much!

I described this separately in a Stackoverflow question, but the short version is, why does this network for those with 'limited resources' consume approximately 700mb of GPU memroy just to run the main.lua script?

I tried running the main.lua on this maching with a NVidia Quadro K600 with 900 MB of memory, and it fails at
output:copy(model:forward(img))
for the first image!

Maybe I missunderstood, but if the network is ~1.5 mb, and the image is ~1.5mb, why does the GPU need to load ~200x times that much?