* Add instance segmentation example - first version of training code
* Add MMOD options; get rid of the cache approach, and instead load all MMOD rects upfront
* Improve console output
* Set filter count
* Minor tweaking
* Inference - first version, at least compiles!
* Ignore overlapped boxes
* Ignore even small instances
* Set overlaps_ignore
* Add TODO remarks
* Revert "Set overlaps_ignore"
This reverts commit 65adeff1f8.
* Set result size
* Set label image size
* Take ignore-color into account
* Fix the cropping rect's aspect ratio; also slightly expand the rect
* Draw the largest findings last
* Improve masking of the current instance
* Add some perturbation to the inputs
* Simplify ground-truth reading; fix random cropping
* Read even class labels
* Tweak default minibatch size
* Learn only one class
* Really train only instances of the selected class
* Remove outdated TODO remark
* Automatically skip images with no detections
* Print to console what was found
* Fix class index problem
* Fix indentation
* Allow to choose multiple classes
* Draw rect in the color of the corresponding class
* Write detector window classes to ostream; also group detection windows by class (when ostreaming)
* Train a separate instance segmentation network for each classlabel
* Use separate synchronization file for each seg net of each class
* Allow more overlap
* Fix sorting criterion
* Fix interpolating the predicted mask
* Improve bilinear interpolation: if output type is an integer, round instead of truncating
* Add helpful comments
* Ignore large aspect ratios; refactor the code; tweak some network parameters
* Simplify the segmentation network structure; make the object detection network more complex in turn
* Problem: CUDA errors not reported properly to console
Solution: stop and join data loader threads even in case of exceptions
* Minor parameters tweaking
* Loss may have increased, even if prob_loss_increasing_thresh > prob_loss_increasing_thresh_max_value
* Add previous_loss_values_dump_amount to previous_loss_values.size() when deciding if loss has been increasing
* Improve behaviour when loss actually increased after disk sync
* Revert some of the earlier change
* Disregard dumped loss values only when deciding if learning rate should be shrunk, but *not* when deciding if loss has been going up since last disk sync
* Revert "Revert some of the earlier change"
This reverts commit 6c852124ef.
* Keep enough previous loss values, until the disk sync
* Fix maintaining the dumped (now "effectively disregarded") loss values count
* Detect cats instead of aeroplanes
* Add helpful logging
* Clarify the intention and the code
* Review fixes
* Add operator== for the other pixel types as well; remove the inline
* If available, use constexpr if
* Revert "If available, use constexpr if"
This reverts commit 503d4dd335.
* Simplify code as per review comments
* Keep estimating steps_without_progress, even if steps_since_last_learning_rate_shrink < iter_without_progress_thresh
* Clarify console output
* Revert "Keep estimating steps_without_progress, even if steps_since_last_learning_rate_shrink < iter_without_progress_thresh"
This reverts commit 9191ebc776.
* To keep the changes to a bare minimum, revert the steps_since_last_learning_rate_shrink change after all (at least for now)
* Even empty out some of the previous test loss values
* Minor review fixes
* Can't use C++14 features here
* Do not use the struct name as a variable name
* Add concat_prev layer, and U-net example for semantic segmentation
* Allow to supply mini-batch size as command-line parameter
* Decrease default mini-batch size from 30 to 24
* Resize t1, if needed
* Use DenseNet-style blocks instead of residual learning
* Increase default mini-batch size to 50
* Increase default mini-batch size from 50 to 60
* Resize even during the backward step, if needed
* Use resize_bilinear_gradient for the backward step
* Fix function call ambiguity problem
* Clear destination before adding gradient
* Works OK-ish
* Add more U-tags
* Tweak default mini-batch size
* Define a simpler network when using Microsoft Visual C++ compiler; clean up the DenseNet stuff (leaving it for a later PR)
* Decrease default mini-batch size from 24 to 23
* Define separate dnn filename for MSVC++ and not
* Add documentation for the resize_to_prev layer; move the implementation so that it comes after mult_prev
* Fix previous typo
* Minor formatting changes
* Reverse the ordering of levels
* Increase the learning-rate stopping criterion back to 1e-4 (was 1e-8)
* Use more U-tags even on Windows
* Minor formatting
* Latest MSVC 2017 builds fast, so there's no need to limit the depth any longer
* Tweak default mini-batch size again
* Even though latest MSVC can now build the extra layers, it does not mean we should add them!
* Fix naming
the code, but it helps visual studio use less RAM when building the example,
and might make appveyor not crash. It's also a
slightly cleaner way to write the code anyway.
* 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
