Wei Dong [email protected] Yuanfang Guan [email protected]

Download the binary release from http://a2genomics.com/static/aaalgo-adbs2.tar.bz2 Our binary release can be run from any X86_64 linux machine. It depends on the ImageMagick package to produce the gif animation (if you have the "convert" command then it's already satisfied). No other software or hardware dependency.

./study test/10/study  output --gif

Then use a browser to open output/index.html to see the visualization.

Our binary release is a compressed version of our final submission and it binary-reproduces the rank 9 score of 0.011645.

Our model (scientific approach) consists of a set of binary programs (study, touchup) and a set of offline pre-trained model files (models/, pre/). These pre-trained models are frozen at model submission and are not to be retrained for the final test set. The bash scripts included in the submission specify the running order and parameters of study and touchup. They might need to be slightly altered to accomodate test data layout.

Hardware: any x86_64 machine with > 4GB memory. Software: 64-bit Linux with a modern kernel (Centos > 2.6, Ubuntu > 12.04)

The package doesn't depend on other software to produce the submission files. The study program needs ImageMagick to produce GIF visualization, but this is not needed for producing the submission files. for producing the submission files.

Training and validation studies are numbered from 1-700. It is assumed that testing data are also similarly numbered, not using numbers between 1-700 which are already used.

Prepare a file named TEST and list the test study numbers, like the provided TRAIN FILE.

Prepare a directory (or a symbolic link to a directory) named "raw", containing all the training, validating and testing data, like the following structure:

raw/1/study/2ch_21/... raw/1/study/sax_10/... ... raw/700/study/2ch_15/.. raw/700/study/sax_10/.. ....

There is a train.csv file in the directory, which contains the groundtruth data. When validation set is released, the groundtruth data should be merged into this file.

./run-study.sh
./run-submit1.sh
./run-submit2.sh

The first script does common computation of the two submissions; the following two scripts generate two versions of final submissions. Each of the run-submit scripts generates a series of submit files with priorities specified as below:

{pre/}ws_full1/submit {pre/}ws_full0/submit {pre/}ws_one/submit {pre/}ws_cli/submit

(run-submit1.sh output do not have "pre", run-submit2.sh output has "pre".)

The top submit file in the specified rank that is successfully produced should be used as final submission. In the rare/unexpected event when a high ranked submit file has NaN/Inf entries due to unforeseen failure modes, lines containing such bad entries in the submit file should be manually replaced with the corresponding lines in the next highest ranked submit file that doesn't have any NaN/Inf in the corresponding lines. This manual check and possible replacement is considered one step in our scientific approach.

Our caffe models included in the submission are trained with annotated training set only. These models are considered frozen with the submission and is not to be retrained after the release of test set.

Following recipe is just for reference.

Build the code, run.

caffe/bound/import.sh
caffe/bound/train.sh
caffe/contour/import.sh
caffe/contour/train.sh

We pick the bound parameter of the 562000th iteration and contour parameter of the 450000th iteration.

We haven't tested the binary reproducibility of this process. Our submitted models should be used to produce the final submissions as they are.