From: Francois Fleuret Date: Sat, 17 Jun 2017 17:38:53 +0000 (+0200) Subject: Update with information about the convnets. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=3feef9000c7201dc25b872d9a604a0faf1caca3b;p=pysvrt.git Update with information about the convnets. --- diff --git a/README.md b/README.md index 735bee3..9e350b8 100644 --- a/README.md +++ b/README.md @@ -20,6 +20,23 @@ The returned ByteTensor has three dimensions: * Pixel row * Pixel col +# Installation and test # + +Executing + +``` +make -j -k +./test-svrt.py +``` + +should generate an image example.png in the current directory. + +Note that the image generation does not take advantage of GPUs or +multi-core, and can be as fast as 10,000 vignettes per second and as +slow as 40 on a 4GHz i7-6700K. + +# Vignette compression # + The two additional functions ``` @@ -36,23 +53,24 @@ provide a lossless compression scheme adapted to the ByteStorage of the vignette ByteTensor (i.e. expecting a lot of 255s, a few 0s, and no other value). -They allow to reduce the memory footprint by a factor ~50, and may be -usefull to deal with very large data-sets and avoid re-generating -images at every batch. +This compression reduces the memory footprint by a factor ~50, and may +be usefull to deal with very large data-sets and avoid re-generating +images at every batch. It induces a little overhead for decompression, +and moving from CPU to GPU memory. See vignette_set.py for a class CompressedVignetteSet using it. -# Installation and test # +# Testing convolution networks # -Executing +The file ``` -make -j -k -./test-svrt.py +cnn-svrt.py ``` -should generate an image example.png in the current directory. +provides the implementation of two deep networks, and use the +compressed vignette code to allow the training with several millions +vignettes on a PC with 16Gb and a GPU with 8Gb. -Note that the image generation does not take advantage of GPUs or -multi-core, and can be as fast as 10,000 vignettes per second and as -slow as 40 on a 4GHz i7-6700K. +The networks were designed by Afroze Baqapuri during an internship at +Idiap.