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OpenDAS
dlib
Commits
0cba4bac
Commit
0cba4bac
authored
Jan 24, 2022
by
Davis King
Browse files
updated docs
parent
acf1b9c4
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-2
docs/docs/imaging.xml
docs/docs/imaging.xml
+17
-0
docs/docs/main_menu.xml
docs/docs/main_menu.xml
+8
-0
docs/docs/ml.xml
docs/docs/ml.xml
+32
-0
docs/docs/term_index.xml
docs/docs/term_index.xml
+16
-2
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docs/docs/imaging.xml
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0cba4bac
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@@ -226,6 +226,7 @@
...
@@ -226,6 +226,7 @@
<item>
transform_image
</item>
<item>
transform_image
</item>
<item>
rotate_image
</item>
<item>
rotate_image
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<item>
resize_image
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<item>
resize_image
</item>
<item>
letterbox_image
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<item>
flip_image_left_right
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<item>
flip_image_left_right
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<item>
flip_image_up_down
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<item>
flip_image_up_down
</item>
<item>
add_image_left_right_flips
</item>
<item>
add_image_left_right_flips
</item>
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@@ -1883,6 +1884,22 @@
...
@@ -1883,6 +1884,22 @@
</description>
</description>
</component>
</component>
<!-- ************************************************************************* -->
<component>
<name>
letterbox_image
</name>
<file>
dlib/image_transforms.h
</file>
<spec_file
link=
"true"
>
dlib/image_transforms/interpolation_abstract.h
</spec_file>
<description>
Scales an image so that it fits into a size * size square, while preserving the aspect
ratio of the actual contents by appropriate 0 padding.
<examples>
<example>
dnn_yolo_train_ex.cpp.html
</example>
</examples>
</description>
</component>
<!-- ************************************************************************* -->
<!-- ************************************************************************* -->
<component>
<component>
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docs/docs/main_menu.xml
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0cba4bac
...
@@ -296,6 +296,14 @@
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@@ -296,6 +296,14 @@
<name>
Deep Learning Max-Margin Object Detection
</name>
<name>
Deep Learning Max-Margin Object Detection
</name>
<link>
dnn_mmod_ex.cpp.html
</link>
<link>
dnn_mmod_ex.cpp.html
</link>
</item>
</item>
<item>
<name>
Self-supervised deep feature learning
</name>
<link>
dnn_self_supervised_learning_ex.cpp.html
</link>
</item>
<item>
<name>
Training a YOLO object detector
</name>
<link>
dnn_yolo_train_ex.cpp.html
</link>
</item>
<item>
<item>
<name>
Random Cropper
</name>
<name>
Random Cropper
</name>
...
...
docs/docs/ml.xml
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0cba4bac
...
@@ -220,6 +220,14 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
...
@@ -220,6 +220,14 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<name>
mish
</name>
<name>
mish
</name>
<link>
dlib/dnn/layers_abstract.h.html#mish_
</link>
<link>
dlib/dnn/layers_abstract.h.html#mish_
</link>
</item>
</item>
<item>
<name>
clipped_relu
</name>
<link>
dlib/dnn/layers_abstract.h.html#clipped_relu_
</link>
</item>
<item>
<name>
elu
</name>
<link>
dlib/dnn/layers_abstract.h.html#elu_
</link>
</item>
<item>
<item>
<name>
relu
</name>
<name>
relu
</name>
<link>
dlib/dnn/layers_abstract.h.html#relu_
</link>
<link>
dlib/dnn/layers_abstract.h.html#relu_
</link>
...
@@ -317,6 +325,10 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
...
@@ -317,6 +325,10 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<name>
loss_mmod
</name>
<name>
loss_mmod
</name>
<link>
#loss_mmod_
</link>
<link>
#loss_mmod_
</link>
</item>
</item>
<item>
<name>
loss_yolo
</name>
<link>
#loss_yolo_
</link>
</item>
<item>
<item>
<name>
loss_metric
</name>
<name>
loss_metric
</name>
<link>
#loss_metric_
</link>
<link>
#loss_metric_
</link>
...
@@ -832,6 +844,26 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
...
@@ -832,6 +844,26 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
</examples>
</examples>
</component>
</component>
<!-- ************************************************************************* -->
<component>
<name>
loss_yolo_
</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 YOLO detection loss defined in
the paper:
<blockquote>
YOLOv3: An Incremental Improvement by Joseph Redmon and Ali Farhadi.
</blockquote>
This means you use this loss if you want to detect the locations of objects
in images.
</description>
<examples>
<example>
dnn_yolo_train_ex.cpp.html
</example>
</examples>
</component>
<!-- ************************************************************************* -->
<!-- ************************************************************************* -->
<component>
<component>
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docs/docs/term_index.xml
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0cba4bac
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@@ -174,6 +174,8 @@
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@@ -174,6 +174,8 @@
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