peiyunh
Do you have any plans to use tensorflow or caffe to implement this paper?
Your working is very nice !!! Do you have any plans to use tensorflow or caffe to implement this paper?
I think caffe is not a good choice because the matconvnet has net.conserveMemory mechanism to aggressively conserve memory when inferencing a large upsampled image(5000x5000) while caffe have not any memory management schema. CNTK or mxnet may be a good choice as they gracefully support DAG memory sharing technology(same or not same to matconvnet's conserveMemory) to deal with the huge resnet101-fcn like model when processing large photo.
@peiyunh I have already converted matconvnet model to caffemodel. But the problem is just like what @chinakook said, I can't test a image whose size is more than 1000 * 600, because of out of gpu memory. So please tell me how you solve the problem if you have any spare time. Thank you very much！
@MaxLingwei I have ported a MXNet implementation which is high performant and momory efficiency as the peiyun's Matconvnet implementation. please refer to https://github.com/chinakook/hr101_mxnet
@chinakook Thank you for your information. I just resized the img and got a good result as using its original size. And I can use caffe after resized it.
If someone wants to collaborate to implement it with Keras (TensorFlow backend), let me know ! I began to work on it but I don't know Matlab enough
@jmu201521121021 @helxsz i have written a script which can transform a matconvnet model to cafemodel. https://github.com/MaxLingwei/MatConvNet2caffe
hi @peiyunh appreciate ur nice work, very happy to see ur plan on releasing the version of caffe. I plan to realize ur tiny face on caffe framework, so it would be helpful to let me know when will you release it?
A newer face detection based on tiny face named SSH is open. https://github.com/mahyarnajibi/SSH
@peiyunh @chinakook I have ported a TensorFlow Tiny Face Detector based on peiyunh's MatConvNet and chinakook's MXNet versions. Note my port supports only inference parts, not training. https://github.com/cydonia999/Tiny_Faces_in_Tensorflow
@cydonia999 nice work! I've already finished the MXNet version of tiny including training. I will make it public later and the data preparation code can be shared across different deep learning frameworks.
@chinakook nice work SSH~ still waiting for the caffe version of tiny face.
@MaxLingwei Thank you for your matconvnet2caffe. Do you compare the result between caffe and matconvnet? Is the result of caffe same with the matconvnet?
@zmlmanly It's nearly the same. But there's another problem that caffe has no method for memory saving. It costs too much GPU memory. So I can't test on large images using caffe.
@MaxLingwei Thank you for your reply. I think you can reduce the height and width of input_dim in prototxt, for example, you can adjust input_dim:1 input_dmi:3 input_dim:224 input_dim:224 to input_dim:1 input_dmi:3 input_dim:64 input_dim:64 I have no idea if the method is correct, you can try it. And I have a problem about your matconvnet2caffe, how to convert the ConvTranspose_Layer in matconvnet to caffe? In matconvnet, there is a "crop [1,2,1,2]" in ConvTranspose_Layer in hr_res101.mat, but there is no "crop" in Deconvolution_Layer in caffe. How to realize the "crop [1,2,1,2]" in caffe? Thank you very much.
@zmlmanly I just use both the Deconvolution layer and Crop layer in caffe, adding the Crop layer after the Deconvolution layer.
@MaxLingwei How to set the Crop layer after the Deconvolution layer in caffe? For example, the input of deconvolution layer is [N,C,H,W], the output of deconvolution layer in caffe is [N,C,2H,2W], but the output of deconvolution layer in matconvnet is [N,C,2H-1,2W-1]. Could you give me display the crop layer of prototxt in caffe? Thang you very much.
@zmlmanly There are two bottoms in Crop layer. To my knowledge it will crop one bottom to match another automaticly. I have upload the prototxt in my repository. https://github.com/MaxLingwei/MatConvNet2caffe