Code for "Salient Deconvolutional Networks, Aravindh Mahendran, Andrea Vedaldi, ECCV 2016"

Last update: Jan 25, 2021

Related tags

CLI Tools deconvnet_analysis

Overview

deconvnet_analysis

Code for "Salient Deconvolutional Networks, Aravindh Mahendran, Andrea Vedaldi, ECCV 2016"

Parts of this code

Generate figures in the paper
Segmentation code for table 1 and .mat files required to generate figure 8.
Supplementary material figures.

Parts 1 and 2 are complete and documented. Part 3 is mainly an extension of part 1 with more images

How to run this code

Follow these steps. ROOT refers to the main directory containing the file generate_all_figures.m

Copy and install matconvnet into ROOT/matconvnet . Delete ROOT/matconvnet/matlab/simplenn/vl_simplenn.m
Copy vlfeat into ROOT/vlfeat
Download the alexnet model - imagenet-caffe-alex from http://www.vlfeat.org/matconvnet/pretrained/ into ROOT/models
Similarly download the vgg-verydeep-16 model - imagenet-vgg-verydeep-16 into ROOT/models
Download the imagenet validation dataset into ROOT/imagenet12-val .
Download and install gsc-1.2 from https://www.robots.ox.ac.uk/~vgg/software/iseg/ into ROOT/saliency/gsc
Download gtsegs_ijcv.mat from http://groups.inf.ed.ac.uk/calvin/proj-imagenet/data/ into saliency/data/ferrari
Start matlab and change directory to ROOT/saliency. Run exp_seg_unpack() - this will unpack gtsegs_ijcv.mat and compute the imdb.mat for the Ferrari dataset [4].
Explore ROOT/saliency/run_2.m to run different segmentation experiments to get numbers in Table 1 of the paper.
Explore and run ROOT/generate_all_figures.m to get figures from the paper. They will be saved into the ROOT/genfigs folder.

Note that step 9 assumes all the experiments for segmentation in ROOT/saliency/run_2.m were run. This assumption applies only when generating figure 8. You can also uncomment segmentation_qualitative_results_figure to skip it.

Research code

If anything doesn't work then please post the issue and I'll try to fix it. This is research code and comes with no WARRANTY or GUARANTY of any sort.

References

Guillaumin, M., Küttel, D., Ferrari, V.: Imagenet auto-annotation with segmentation propagation. In: IJCV (2014)
Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proc. ICCV (2015)
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: Visualising image classification models and saliency maps. In: ICLR (2014)
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: The all convolutional net. In: ICLR Workshop (2015)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Proc. ECCV (2014)
V. Gulshan, C. Rother, A. Criminisi, A. Blake and A. Zisserman.: Geodesic star convexity for interactive image segmentation. In: Proc. CVPR (2010)

Comments

Some setting when using the code

I just run the code yesterday, and found some things need to pay attention before running. I am using matlab2018a, Windows10 64, VS2015 64.

First, When install gsc-1.2, you'd better look this document from matlab. And change some code in compile_mex.m(deconvnet_analysis-master\saliency\gsc\packages+bj) (https://ww2.mathworks.cn/help/matlab/matlab_external/upgrading-mex-files-to-use-64-bit-api.html)

Second, This file needs to make some change(saliency\ksseg.m). Code(95) change into : if max(max(labels))>0 segH.start(labels); else segH.start(labelsClamp); end cuz, in start there is only one parameter for input.

Third, version of matconvnet 25 is OK. Lower version I found some functions parameters wrong.

Forth, When using run_2.m, add run ../matconvnet/matlab/vl_setupnn.m in your file is better.

Last, When try to visualize multi-class model, you should see advice of aravindhm. "Please try either network inversion, excitation backprop, layerwise relevance propagation or grad-CAM."

opened by GlowingHorse 11

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Code for "Salient Deconvolutional Networks, Aravindh Mahendran, Andrea Vedaldi, ECCV 2016"

Related tags

Overview

deconvnet_analysis

Parts of this code

How to run this code

Research code

References

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Comments

Some setting when using the code

Owner

Aravindh Mahendran

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