mirror of https://github.com/davisking/dlib.git
Add input_rgb_image_pair layer and new net visitors documentation (#2497)
* Add input_rgb_image layer documentation * fix typo * Add index entries for missing network visitors * update Barlow Twins loss links * add missing yolo rect docs, links to yolo paper and better Barlow Twins formatting
This commit is contained in:
Adrià Arrufat
GitHub
parent
21651f498e
commit
3d4c14e633
|
|
@ -98,6 +98,7 @@
|
|||
<item>evaluate_detectors</item>
|
||||
<item>full_object_detection</item>
|
||||
<item>mmod_rect</item>
|
||||
<item>yolo_rect</item>
|
||||
<item>scan_image</item>
|
||||
<item>scan_image_movable_parts</item>
|
||||
<item>find_points_above_thresh</item>
|
||||
|
|
@ -2551,6 +2552,19 @@
|
|||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>yolo_rect</name>
|
||||
<file>dlib/image_processing.h</file>
|
||||
<spec_file link="true">dlib/image_processing/full_object_detection_abstract.h</spec_file>
|
||||
<description>
|
||||
This is a simple struct that is used to give training data and receive detections
|
||||
from the <a href="ml.html#loss_yolo_">YOLO loss layer for object detection</a>.
|
||||
</description>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
|
|
|
|||
|
|
@ -126,6 +126,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
|
|||
<item>input_rgb_image</item>
|
||||
<item>input_rgb_image_sized</item>
|
||||
<item>input_rgb_image_pyramid</item>
|
||||
<item>input_rgb_image_pair</item>
|
||||
<item>input_grayscale_image_pyramid</item>
|
||||
<item>
|
||||
<name>EXAMPLE_INPUT_LAYER</name>
|
||||
|
|
@ -355,7 +356,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
|
|||
</item>
|
||||
<item>
|
||||
<name>loss_barlow_twins</name>
|
||||
<link>dlib/dnn/loss_abstract.h.html#loss_barlow_twins_</link>
|
||||
<link>#loss_barlow_twins_</link>
|
||||
</item>
|
||||
</sub>
|
||||
</item>
|
||||
|
|
@ -817,6 +818,28 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
|
|||
</description>
|
||||
</component>
|
||||
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>input_rgb_image_pair</name>
|
||||
<file>dlib/dnn.h</file>
|
||||
<spec_file link="true">dlib/dnn/input_abstract.h</spec_file>
|
||||
<description>
|
||||
This is a simple input layer type for use in a deep neural network
|
||||
which takes a pair of RGB images as input and loads it into a network.
|
||||
It is useful when you want to input image pairs that are related to each other,
|
||||
for instance, they are different distorted views of the same original image.
|
||||
This input layer is meant to be used with a loss layer such as the
|
||||
<a href="#loss_barlow_twins_">Barlow Twins loss layer</a>. You can also
|
||||
convert between <a href="#input_rgb_image">input_rgb_image</a> and
|
||||
this input layer by copy construction or assignment.
|
||||
</description>
|
||||
<examples>
|
||||
<example>dnn_self_supervised_learning_ex.cpp.html</example>
|
||||
</examples>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
|
|
@ -855,13 +878,13 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
|
|||
<file>dlib/dnn.h</file>
|
||||
<spec_file link="true">dlib/dnn/loss_abstract.h</spec_file>
|
||||
<description>
|
||||
This object is a <a href="dlib/dnn/loss_abstract.h.html#EXAMPLE_LOSS_LAYER_">loss layer</a>
|
||||
for a deep neural network. In particular, it implements the YOLO detection loss defined in
|
||||
This object is a <a href="dlib/dnn/loss_abstract.h.html#EXAMPLE_LOSS_LAYER_">loss layer</a>
|
||||
for a deep neural network. In particular, it implements the YOLO detection loss defined in
|
||||
the paper:
|
||||
<blockquote>YOLOv3: An Incremental Improvement by Joseph Redmon and Ali Farhadi.</blockquote>
|
||||
|
||||
<blockquote><a href="https://arxiv.org/abs/1804.02767">YOLOv3: An Incremental Improvement</a> by Joseph Redmon and Ali Farhadi.</blockquote>
|
||||
|
||||
This means you use this loss if you want to detect the locations of objects
|
||||
in images.
|
||||
in images.
|
||||
</description>
|
||||
<examples>
|
||||
<example>dnn_yolo_train_ex.cpp.html</example>
|
||||
|
|
@ -916,6 +939,32 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
|
|||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>loss_barlow_twins_</name>
|
||||
<file>dlib/dnn.h</file>
|
||||
<spec_file link="true">dlib/dnn/loss_abstract.h</spec_file>
|
||||
<description>
|
||||
This object is a <a href="dlib/dnn/loss_abstract.h.html#EXAMPLE_LOSS_LAYER_">loss layer</a>
|
||||
for a deep neural network. In particular, it implements the Barlow Twins loss layer presented
|
||||
in the paper:
|
||||
|
||||
<blockquote>
|
||||
<a href="https://arxiv.org/abs/2103.03230">Barlow Twins: Self-Supervised Learning
|
||||
via Redundancy Reduction</a>, by Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, Stéphane Deny.
|
||||
</blockquote>
|
||||
|
||||
This means you use this loss to learn useful representations from data that has no label
|
||||
information. Useful representations mean that can be used to train another downstream task,
|
||||
such as classification. In particular, this loss function applies the redundancy reduction
|
||||
principle to the representations learned by the network it sits on top of.
|
||||
</description>
|
||||
<examples>
|
||||
<example>dnn_self_supervised_learning_ex.cpp.html</example>
|
||||
</examples>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
|
|
|
|||
|
|
@ -39,6 +39,9 @@
|
|||
<term file="dlib/dnn/utilities_abstract.h.html" name="randomize_parameters" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/utilities_abstract.h.html" name="input_tensor_to_output_tensor" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/utilities_abstract.h.html" name="output_tensor_to_input_tensor" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/utilities_abstract.h.html" name="count_parameters" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/utilities_abstract.h.html" name="set_all_learning_rate_multipliers" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/utilities_abstract.h.html" name="set_learning_rate_multipliers_range" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/core_abstract.h.html" name="tuple_head" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/core_abstract.h.html" name="tuple_tail" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/core_abstract.h.html" name="get_learning_rate_multiplier" include="dlib/dnn.h"/>
|
||||
|
|
@ -101,6 +104,8 @@
|
|||
<term file="dlib/dnn/layers_abstract.h.html" name="CONV_MODE" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/layers_abstract.h.html" name="FC_MODE" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/layers_abstract.h.html" name="set_all_bn_running_stats_window_sizes" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/layers_abstract.h.html" name="disable_duplicative_biases" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/layers_abstract.h.html" name="fuse_layers" include="dlib/dnn.h"/>
|
||||
<term file="dlib/cuda/tensor_abstract.h.html" name="tensor" include="dlib/cuda/tensor.h"/>
|
||||
<term file="dlib/cuda/tensor_abstract.h.html" name="resizable_tensor" include="dlib/cuda/tensor.h"/>
|
||||
<term file="dlib/cuda/tensor_abstract.h.html" name="alias_tensor_instance" include="dlib/cuda/tensor.h"/>
|
||||
|
|
@ -123,6 +128,7 @@
|
|||
<term file="ml.html" name="input_rgb_image" include="dlib/dnn.h"/>
|
||||
<term file="ml.html" name="input_rgb_image_sized" include="dlib/dnn.h"/>
|
||||
<term file="ml.html" name="input_rgb_image_pyramid" include="dlib/dnn.h"/>
|
||||
<term file="ml.html" name="input_rgb_image_pair" include="dlib/dnn.h"/>
|
||||
<term file="ml.html" name="input_grayscale_image_pyramid" include="dlib/dnn.h"/>
|
||||
|
||||
<term file="ml.html" name="dnn_trainer" include="dlib/dnn.h"/>
|
||||
|
|
@ -149,7 +155,7 @@
|
|||
<term file="dlib/dnn/loss_abstract.h.html" name="mmod_options" include="dlib/dnn.h"/>
|
||||
<term file="ml.html" name="loss_yolo_" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/loss_abstract.h.html" name="yolo_options" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/loss_abstract.h.html" name="loss_barlow_twins_" include="dlib/dnn.h"/>
|
||||
<term file="ml.html" name="loss_barlow_twins_" include="dlib/dnn.h"/>
|
||||
|
||||
<term file="dlib/dnn/solvers_abstract.h.html" name="EXAMPLE_SOLVER" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/solvers_abstract.h.html" name="sgd" include="dlib/dnn.h"/>
|
||||
|
|
@ -1649,6 +1655,7 @@
|
|||
<term file="dlib/image_processing/object_detector_abstract.h.html" name="full_detection" include="dlib/image_processing.h"/>
|
||||
<term file="imaging.html" name="full_object_detection" include="dlib/image_processing.h"/>
|
||||
<term file="imaging.html" name="mmod_rect" include="dlib/image_processing.h"/>
|
||||
<term file="imaging.html" name="yolo_rect" include="dlib/image_processing.h"/>
|
||||
<term file="dlib/image_processing/full_object_detection_abstract.h.html" name="all_parts_in_rect" include="dlib/image_processing.h"/>
|
||||
|
||||
<term file="imaging.html" name="scan_image_movable_parts" include="dlib/image_processing.h"/>
|
||||
|
|
|
|||
Loading…
Reference in New Issue