add conference name

Author: Yan Gobeil

docs/_build/doctrees/environment.pickle

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 027ecf3230c6fc4b800943a979871569
+config: 9f04f3ca686a5adab571079123708447
 tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/_build/doctrees/index.doctree

@@ -60,5 +60,7 @@ GCS Bucket (gs://) as the output folder (only if you are on colab and have authe
 You need to use this code once for your train, val (and test folders if you have one). This will delete the old records in the folders and create 
 a csv file with the names of your classes.
 
+The images are not resized by default because different models use different sizes during training. There is an option to specify a target size if desired.
+
 
 

docs/_build/html/.buildinfo

@@ -25,13 +25,13 @@ A great way of explaining the library is using an example notebook, which you ca
 
 The most recent version of this library adds a feature to leverage unlabelled images in order to improve the performance
 of image classifiers. This procedure is called semi-supervised learning (SSL) and is discussed in this `blog post <https://medium.com/decathlondevelopers/improving-performance-of-image-classification-models-using-pretraining-and-a-combination-of-e271c96808d2/>`_.
+The method was also described in a `paper <https://arxiv.org/abs/2108.08362/>`_ and presented at the ACM MMSports 2021 `conference <http://mmsports.multimedia-computing.de/mmsports2021/program.html>`_.
 
 Installation
 =============
 
 This library works with python 3.6 and above and it is based on the following dependencies:
 
-- efficientnet
 - tensorflow 2
 - matplotlib
 - scikit-optimize

docs/_build/html/_sources/data_example.rst.txt

@@ -25,8 +25,10 @@ The training is then done with the following code::
   classifier = decavision.model_training.tfrecords_image_classifier.ImageClassifier(tfrecords_folder='data/tfrecords', batch_size=16, transfer_model='B3')
   classifier.fit()
  
-You can decide the transfer model between Xception, Inception_Resnet, Resnet and B0, B3, B5 or B7 (all EffecientNets). Their respective 
-sizes and performance metrics can be found in the keras `documentation <https://keras.io/api/applications/>`_. Also, note that on the fly data augmentation is done by default so if you already generated new images manually be sure to set augment to False.
+You can decide the transfer model between Xception, Inception_Resnet, Resnet, the EffcientNet models B0, B3, B5 and B7, and the EfficientNetV2 models V2-S, V2-M, V2-L and V2-XL. Their respective
+sizes and performance metrics can be found in the keras `documentation <https://keras.io/api/applications/>`_. Many of the models use different image sizes so it is better to not resize the images prior to training. The library does it already.
+
+Also, note that on the fly data augmentation is done by default so if you already generated new images manually be sure to set augment to False.
 
 The parameters that can be specified when training are:
 

docs/_build/html/_sources/index.rst.txt

@@ -130,7 +130,7 @@ ul.search li a {
     font-weight: bold;
 }
 
-ul.search li div.context {
+ul.search li p.context {
     color: #888;
     margin: 2px 0 0 30px;
     text-align: left;
@@ -508,6 +508,63 @@ table.hlist td {
     vertical-align: top;
 }
 
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
 
 /* -- other body styles ----------------------------------------------------- */
 
@@ -634,14 +691,6 @@ dl.glossary dt {
     font-size: 1.1em;
 }
 
-.optional {
-    font-size: 1.3em;
-}
-
-.sig-paren {
-    font-size: larger;
-}
-
 .versionmodified {
     font-style: italic;
 }
@@ -770,8 +819,12 @@ div.code-block-caption code {
 
 table.highlighttable td.linenos,
 span.linenos,
-div.doctest > div.highlight span.gp {  /* gp: Generic.Prompt */
-    user-select: none;
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
 }
 
 div.code-block-caption span.caption-number {
@@ -786,16 +839,6 @@ div.literal-block-wrapper {
     margin: 1em 0;
 }
 
-code.descname {
-    background-color: transparent;
-    font-weight: bold;
-    font-size: 1.2em;
-}
-
-code.descclassname {
-    background-color: transparent;
-}
-
 code.xref, a code {
     background-color: transparent;
     font-weight: bold;

docs/_build/html/_sources/train_example.rst.txt

@@ -301,12 +301,14 @@ var Documentation = {
               window.location.href = prevHref;
               return false;
             }
+            break;
           case 39: // right
             var nextHref = $('link[rel="next"]').prop('href');
             if (nextHref) {
               window.location.href = nextHref;
               return false;
             }
+            break;
         }
       }
     });

docs/_build/html/_static/basic.css

@@ -1,6 +1,6 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '1.2.0',
+    VERSION: '1.3.0',
     LANGUAGE: 'None',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',