Update README.md

README.md

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 ### 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.
+- 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.
+-  Modification: This model, animeGender-dvgg-0.7, uses all weights from the convolutional neural network of the original vgg-16 model released by an Oxford team, but has changed the network structure of the last sequantials, say, 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.
 
 -  Input: While the original model vgg-16 has been designed with an input with 224 * 224 in terms of resolution, and 3 dementions in RGB colorspace, in our model animeGender-dvgg-0.7, we aim to only use 64 * 64 with RGB colorspace only, as the classification task is not too tough. Please note when feeding a picture into the model, please ensure that the input illustration only consists of the head and face of the character you want to identify, in order to make the result from the model most precise and reliable. Moreover, we have designed some Python functions in our open source codes to help you resize, crop, and transform your pictures into 64 * 64 RGB ones, and more information is available in the file folder.