X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=README.md;h=cb77899c768cf5d2567e9bbc817858cc41e00db4;hb=e9f012349010d2a4f5d2ed0869974611fade32f1;hp=d0ec215a1767bd07bfc53b0e0758ce1891c39bd1;hpb=77153ad6f6acb94a5132e9930722500cd93a6960;p=pysvrt.git

diff --git a/README.md b/README.md
index d0ec215..cb77899 100644
--- a/README.md
+++ b/README.md
@@ -13,7 +13,9 @@ make -j -k
 ./test-svrt.py
 ```
 
-should generate an image example.png in the current directory.
+should generate an image
+[`example.png`](https://fleuret.org/git-extract/pysvrt/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
@@ -23,7 +25,7 @@ slow as 40 on a 4GHz i7-6700K.
 
 ## Vignette sets ##
 
-The svrtset.py implements the classes `VignetteSet` and
+The file [`svrtset.py`](https://fleuret.org/git-extract/pysvrt/svrtset.py) implements the classes `VignetteSet` and
 `CompressedVignetteSet` with the following constructor
 
 ```
@@ -36,6 +38,9 @@ and the following method to return one batch
 (torch.FloatTensor, torch.LongTensor) get_batch(b)
 ```
 
+as a pair composed of a 4d 'input' Tensor (i.e. single channel 128x128
+images), and a 1d 'target' Tensor (i.e. Boolean labels).
+
 ## Low-level functions ##
 
 The main function for genering vignettes is
@@ -81,14 +86,7 @@ See vignette_set.py for a class CompressedVignetteSet using it.
 # Testing convolution networks #
 
 The file
-
-```
-cnn-svrt.py
-```
-
-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.
-
-The networks were designed by Afroze Baqapuri during an internship at
-Idiap.
+[`cnn-svrt.py`](https://fleuret.org/git-extract/pysvrt/cnn-svrt.py)
+provides the implementation of two deep networks designed by Afroze
+Baqapuri during an internship at Idiap, and allows to train them with
+several millions vignettes on a PC with 16Gb and a GPU with 8Gb.