* Add example of semantic segmentation using the PASCAL VOC2012 dataset

* Add note about Debug Information Format when using MSVC

* Make the upsampling layers residual as well

* Fix declaration order

* Use a wider net

* trainer.set_iterations_without_progress_threshold(5000); // (was 20000)

* Add residual_up

* Process entire directories of images (just easier to use)

* Simplify network structure so that builds finish even on Visual Studio (faster, or at all)

* Remove the training example from CMakeLists, because it's too much for the 32-bit MSVC++ compiler to handle

* Remove the probably-now-unnecessary set_dnn_prefer_smallest_algorithms call

* Review fix: remove the batch normalization layer from right before the loss

* Review fix: point out that only the Visual C++ compiler has problems.
Also expand the instructions how to run MSBuild.exe to circumvent the problems.

* Review fix: use dlib::match_endings

* Review fix: use dlib::join_rows. Also add some comments, and instructions where to download the pre-trained net from.

* Review fix: make formatting comply with dlib style conventions.

* Review fix: output training parameters.

* Review fix: remove #ifndef __INTELLISENSE__

* Review fix: use std::string instead of char*

* Review fix: update interpolation_abstract.h to say that extract_image_chips can now take the interpolation method as a parameter

* Fix whitespace formatting

* Add more comments

* Fix finding image files for inference

* Resize inference test output to the size of the input; add clarifying remarks

* Resize net output even in calculate_accuracy

* After all crop the net output instead of resizing it by interpolation

* For clarity, add an empty line in the console output

* Determine lapack fortran linking convention in CMake

Looks for lapack function with and without trailing underscore - allows use of CLAPACK on windows where functions are decorated but fortran_id.h otherwise assumes they are not

* Use enable_preprocessor_switch for LAPACK decoration detection

* Add lapack decoration defines to config.h.in

* Use correct variable for lapack_libraries

find_global_maximum() and global_function_search.

general templates in dlib::relational_operators.  I did this because the
templates in dlib::relational_operators sometimes cause clashes with other
code in irritating ways.

* Add capability to train scale-variant MMOD models

* Review fixes: change bool scale_invariant to strongly typed enum, etc

* Add serialization and deserialization of assumed_input_layer_type

* Fix code formatting

* Rename things as per review feedback

* Review fix: move enum use_image_pyramid outside mmod_options

* Continue execution with net, if deserialization of shape predictor fails

* Revert "Continue execution with net, if deserialization of shape predictor fails"

This reverts commit 8ea4482c043b5b98b97ed5b78bfc6916a1e2a453.

output 2 more statistics, which are the mean absolute error and the standard
deviation of the absolute error.  This means these functions now return 4D
rather than 2D vectors.

I also made test_regression_function() take a non-const reference to the
regression function so that DNN objects can be tested.

* Remove explicit specification of library path in dlib.cmake
Enables side-by-side multi configuration build on windows

* Add dlib_LIBS

For backwards compatability

* This fix suggested by davisking make unit tests more reliable. Fix issue https://github.com/davisking/dlib/issues/925

* This fix suggested by davisking make unit tests more reliable. Fix issue https://github.com/davisking/dlib/issues/925

0.5*MSE.  The only thing this effects is the logging messages that print during
training, which were confusing since the reported loss was half the size you
would expect.

atomics.  This makes the timing code a lot more precise.

minutes in the output.

as input layer specifications.  This will create input tensors with K
channels.

be smaller.  Instead, they now behave like std::vector in that they just change
their nominal size but keep the same memory, only reallocating if they are
resized to something larger than their underlying memory block.

This change makes some uses of dlib faster, in particular, running networks on
a large set of images of differing sizes will now run faster since there won't
be any GPU reallocations, which are notoriously slow.