DOFOFFICIAL
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animeGender-dvgg-0.7

@@ -18,13 +18,13 @@ tags:
-### [Description]
 - Our proposal model, animeGender-dvgg-0.7, which is a fine-tuned binary classification model created by DOF-Studio(2023) and based on the pre-trained model vgg-16, aims to identify the gender, or sex of a particular animation character (particularly designed for Japanese-style 2D anime characters). It is trained by DOF-Studio in July, 2023, on an organizational private data set that is manually collected and tagged by our staff. Although this model has shown an unprecedentedly successful and charming result of our test and verification data set, please note that this model is still not the final version of our character-gender identification model series, but only a phased result (Version 0.7) of our open-source project, which means upgraded versions will be soon released by our team in the near future, and we are confident to tell that as we have improved the network structure, so that there is going to be a magnificent amelioration in the up-coming ones. Thank you for all of your appreciation and support for our work and models.
-### [Technical Details]
 -  Modification: This model, animeGender-dvgg-0.7, uses all weights from the original vgg-16 model, but has changed the network structure of the last sequantial, the dense layers, which means we have modified it into a binary classification model with two nodes (activated by a softmax layer) output the possibility of each gender, namely female, and male. Note that although the overall network structure, particularly the convolutional neural layers have been left untrained, in the future, we have planned to deeply modify this base model, vgg16, to achieve a higher score and precision in this classification task.
@@ -36,7 +36,7 @@ tags:
-### [Results and Rankings]
 -  Based on the training data and testing data, the proposal model has achieved a result shown below:
 -  name = animeGender-dvgg-0.7
@@ -50,7 +50,7 @@ tags:
-### [Examples]
 -  Here are some sample-exteral tests that are conducted by our staff with the corresponding results shown below:
@@ -95,7 +95,7 @@ tags:
-### [Usage]
 -  We have uploaded the usage with Python in the file folder, and please note you should download them and run locally using either your CPU or with CUDA.
 -  Note that only the provided codes can be regarded as the most recommended approach to use this model, and other ways including those are automatically shown on this website are not assured to be valid and user-friendly.

+### Description
 - Our proposal model, animeGender-dvgg-0.7, which is a fine-tuned binary classification model created by DOF-Studio(2023) and based on the pre-trained model vgg-16, aims to identify the gender, or sex of a particular animation character (particularly designed for Japanese-style 2D anime characters). It is trained by DOF-Studio in July, 2023, on an organizational private data set that is manually collected and tagged by our staff. Although this model has shown an unprecedentedly successful and charming result of our test and verification data set, please note that this model is still not the final version of our character-gender identification model series, but only a phased result (Version 0.7) of our open-source project, which means upgraded versions will be soon released by our team in the near future, and we are confident to tell that as we have improved the network structure, so that there is going to be a magnificent amelioration in the up-coming ones. Thank you for all of your appreciation and support for our work and models.
+### Technical Details
 -  Modification: This model, animeGender-dvgg-0.7, uses all weights from the original vgg-16 model, but has changed the network structure of the last sequantial, the dense layers, which means we have modified it into a binary classification model with two nodes (activated by a softmax layer) output the possibility of each gender, namely female, and male. Note that although the overall network structure, particularly the convolutional neural layers have been left untrained, in the future, we have planned to deeply modify this base model, vgg16, to achieve a higher score and precision in this classification task.
+### Results and Rankings
 -  Based on the training data and testing data, the proposal model has achieved a result shown below:
 -  name = animeGender-dvgg-0.7
+### Examples
 -  Here are some sample-exteral tests that are conducted by our staff with the corresponding results shown below:
+### Usage
 -  We have uploaded the usage with Python in the file folder, and please note you should download them and run locally using either your CPU or with CUDA.
 -  Note that only the provided codes can be regarded as the most recommended approach to use this model, and other ways including those are automatically shown on this website are not assured to be valid and user-friendly.