X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=folded-ctf.git;a=blobdiff_plain;f=README.txt;h=3bca7f07e2381dcccf88b7f0d5e63247a6d50aa4;hp=e74ec226de95134ee61c26168296a7fc981f2543;hb=4953a336630d0ee905c949bf1fe846b85cabb744;hpb=82c3a0366dd58ea2b3a2fc3f90be3f1a42ac8341

diff --git a/README.txt b/README.txt
index e74ec22..3bca7f0 100644
--- a/README.txt
+++ b/README.txt
@@ -1,5 +1,6 @@
 
-INTRODUCTION
+######################################################################
+## INTRODUCTION
 
   This is the C++ implementation of the folded hierarchy of
   classifiers for cat detection described in
@@ -9,7 +10,8 @@ INTRODUCTION
 
   Please cite this paper when referring to this software.
 
-INSTALLATION
+######################################################################
+## INSTALLATION
 
   This program was developed on Debian GNU/Linux computers with the
   following main tool versions
@@ -36,12 +38,12 @@ INSTALLATION
   You can also run the full thing with the following commands if you
   have wget installed
 
-     wget http://www.idiap.ch/folded-ctf/not-public-yet/data/folding-gpl.tgz
-     tar zxvf folding-gpl.tgz
-     cd folding
-     wget http://www.idiap.ch/folded-ctf/not-public-yet/data/rmk.tgz
-     tar zxvf rmk.tgz
-     ./run.sh
+   > wget http://www.idiap.ch/folded-ctf/not-public-yet/data/folding-gpl.tgz
+   > tar zxvf folding-gpl.tgz
+   > cd folding
+   > wget http://www.idiap.ch/folded-ctf/not-public-yet/data/rmk.tgz
+   > tar zxvf rmk.tgz
+   > ./run.sh
 
   Note that every one of the twenty rounds of training/testing takes
   more than three days on a powerful PC. However, the script detects
@@ -54,7 +56,8 @@ INSTALLATION
 
   You are welcome to send bug reports and comments to fleuret@idiap.ch
 
-PARAMETERS
+######################################################################
+## PARAMETERS
 
   To set the value of a parameter during an experiment, just add an
   argument of the form --parameter-name=value before the commands that
@@ -73,7 +76,7 @@ PARAMETERS
 
   * pictures-for-article ("no")
 
-    Should the pictures be generated to be clear in b&w
+    Should the pictures be generated for printing in black and white.
 
   * pool-name (no default)
 
@@ -90,14 +93,14 @@ PARAMETERS
 
   * result-path ("/tmp/")
 
-    In what directory should we save all the produced file during the
+    In what directory should we save all the produced files during the
     computation.
 
   * loss-type ("exponential")
 
-    What kind of loss to use for the boosting. While different loss are
-    implementer in the code, only the exponential has been thoroughly
-    tested.
+    What kind of loss to use for the boosting. While different losses
+    are implemented in the code, only the exponential has been
+    thoroughly tested.
 
   * nb-images (-1)
 
@@ -107,7 +110,7 @@ PARAMETERS
   * tree-depth-max (1)
 
     Maximum depth of the decision trees used as weak learners in the
-    classifier.
+    classifier. The default value corresponds to stumps.
 
   * proportion-negative-cells-for-training (0.025)
 
@@ -116,14 +119,14 @@ PARAMETERS
 
   * nb-negative-samples-per-positive (10)
 
-    How many negative cell to sample for every positive cell during
+    How many negative cells to sample for every positive cell during
     training.
 
   * nb-features-for-boosting-optimization (10000)
 
     How many pose-indexed features to use at every step of boosting.
 
-  * force-head-belly-independence (no)
+  * force-head-belly-independence ("no")
 
     Should we force the independence between the two levels of the
     detector (i.e. make an H+B detector)
@@ -139,8 +142,8 @@ PARAMETERS
 
   * nb-levels (1)
 
-    How many levels in the hierarchy, this is 2 for the JMLR paper
-    experiments.
+    How many levels in the hierarchy. This should be 2 for the JMLR
+    paper experiments.
 
   * proportion-for-train (0.5)
 
@@ -207,17 +210,45 @@ PARAMETERS
 
     Should we display a progress bar.
 
-COMMANDS
+######################################################################
+## COMMANDS
 
-   open-pool
-   train-detector
-   compute-thresholds
-   test-detector
-   sequence-test-detector
-   write-detector
-   read-detector
-   write-pool-images
+   * open-pool
 
-  --
-  Francois Fleuret
-  October 200
+     Open the pool of scenes.
+
+   * train-detector
+
+     Create a new detector from the training scenes.
+
+   * compute-thresholds
+
+     Compute the thresholds of the detector classifiers to obtain the
+     required wanted-true-positive-rate
+
+   * test-detector
+
+     Run the detector on the test scenes.
+
+   * sequence-test-detector
+
+     Visit nb-wanted-true-positive-rates rates between 0 and
+     wanted-true-positive-rate, for each compute the detector
+     thresholds on the validation set, estimate the error rate on the
+     test set.
+
+   * write-detector
+
+     Write the current detector to the file detector-name
+
+   * read-detector
+
+     Read a detector from the file detector-name
+
+   * write-pool-images
+
+     Write PNG images of the scenes in the pool.
+
+--
+Francois Fleuret
+October 2008