X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=dyncnn.git;a=blobdiff_plain;f=README.txt;fp=README.txt;h=0000000000000000000000000000000000000000;hp=18526634022928cbb6dec35a85a5028badd92588;hb=aa2b6b28aef52cac1bdc30dc289b6460ea5e2132;hpb=8cce872485111eaa79ce60041715227a8ff4d45f

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-
-This is an implementation of a deep residual network for predicting
-the dynamics of 2D shapes as described in
-
-  F. Fleuret. Predicting the dynamics of 2d objects with a deep
-  residual network. CoRR, abs/1610.04032, 2016.
-
-  https://arxiv.org/abs/1610.04032
-
-This package is composed of a simple 2d physics simulator called
-'flatland' written in C++, to generate the data-set, and a deep
-residual network 'dyncnn' written in the Lua/Torch7 framework.
-
-You can run the reference experiment by executing the run.sh shell
-script.
-
-It will
-
-  (1) Generate the data-set of 40k triplets of images,
-
-  (2) Train the deep network, and output validation results every 100
-      epochs. This takes ~30h on a GTX 1080 with cuda 8.0, cudnn 5.1,
-      and recent torch.
-
-  (3) Generate two pictures of the internal activations.
-
-  (4) Generate a graph with the loss curves if gnuplot is installed.
-
---
-Francois Fleuret
-Nov 24, 2016
-Martigny