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OTOMATIC ANIME GIRL MOVIE
OTOMATIC ANIME GIRL GENERATOR

News Your news from the big world of anime manga, gaming, Japan and more.Clubs A list of all existing clubs and those which are the most active.Forums Your place to go for discussions and polls within the aniSearch community.

OTOMATIC ANIME GIRL MOVIE

Streams Your gateway to a myriad of legal movie streams of your favourites.Calendar A live action calendar with publication dates that’s sorted by seasons.Trending A list of the currently most-visited movie detail pages on aniSearch.Toplist The leaderboard where all movies are listed according to their over-all rank.Popular The most-rated movies on aniSearch during the current season.Index A freely organisable overview on all movies listed on aniSearch.Calendar A manga calendar with publication dates that’s sorted by seasons.Toplist The leaderboard where all manga are listed according to their over-all rank.Popular The most-rated manga on aniSearch during the current season.

Index A freely organisable overview on all manga listed on aniSearch.Characters The overview page that is all about characters.Broadcast Your anime planner with broadcast dates for your favourite shows.Streams Your gateway to a myriad of legal anime streams of your favourites.Calendar An anime calendar with publication dates that’s sorted by seasons.Trending A list of the currently most-visited anime detail pages on aniSearch.Toplist The leaderboard where all anime are listed according to their over-all ranks.Popular The most-rated anime on aniSearch during the current season.Index A freely organisable overview on all anime listed on aniSearch.Currently I have some simple tools for face data statistics.

The training strategy of GAN was written in src/model/gan.py.

OTOMATIC ANIME GIRL GENERATOR

The discriminator and generator were defined in src/model/networks/.

Illustration2vec was used for face tagging. source codes are in src/dataset/FaceDetect/ I used the lbpcascade_animeface model for face detecting. Read the readme and codes in src/dataset/Spider/ to get more information. I'm glad to share my data-Preparing method here.Ĭrawled the images from the website as the paper proposed. :-D )Ĭause I built an extremely clean dataset for this task. But I don't have too much time for weights tuning. Using Multi-Label Soft Margin Loss for tags' loss calculating.(Cross Entropy Loss may better because of the imbalance of the images' tag distribution. Remove the sigmoid operation in adversarial loss calculating since the results with sigmoid layer may cause some problems. (More details please refer in src/model/gan.py) I weighted label's loss and tag's loss with half of λ adv beacause the loss described in the paper was so hard-core for me. I have some modifications in this structure: The SRResNet(modified as the paper described) are like this: I want to call it a DRAGAN-like SRGAN structure because I use the gradient penalty as the paper told and two SRResNet as discriminator and generator.

I'm training the models and completing the README.md under src >.< If you want to use this repo, I strongly recommand you to read codes carefully. I need to add some tricks and continue training it.Īttention This is an unfinished repo. Generated avatars from my pre-trained model. This repo contains a PyTorch from-scratch implementation of the paper Towards the Automatic Anime Characters Creation with Generative Adversarial Networks.