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The selfie dataset appears to be this one: https://www.crcv.ucf.edu/data/Selfie/

Since the paper says they scraped anime-planet themselves to get the anime images, it seems like we'll have to yield to them for their dataset. Alternatively, there appears to be a dataset made available here that might work. http://www.nurs.or.jp/~nagadomi/animeface-character-dataset/

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1. We released 50 epoch and 100 epoch checkpoints so that people could test more widely.

2. Also, We published the selfie2anime datasets we used in the paper.

3. And, we fixed code in smoothing

4. In the test image, I recommend that your face be in the center.

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数据集和模型下载

==========================
链接：https://pan.baidu.com/s/1dP1mXuU-rA9dPvFe8YS8jQ
提取码：k6rc

已添加checkpoint文件

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You can also try this anime_faces dataset that I parsed from the tfrecords of twingan project.

As for a replacement of selfie dataset, you can try celebA and even CelebAMask-HQ(this can help you generate a high-res portraits dataset without backgrounds)

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Note that the nagadomi faces are also on Kaggle, and there's at least 2 anime face datasets derived from Danbooru2018: "Danbooru 2018 Anime Character Recognition Dataset" and SeePrettyFace.com: face dataset, in addition to the ones I made for my StyleGAN/BigGAN projects but haven't formally released.

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Here's my dataset:

• selfie
• anime

And I did some preparations:

According to the paper 5.2. Dataset, I wrote this script and selected 3400 female selfies(but the selfie labels seems contains some errors so there's still some male selfies) as trainset and 100 as testset. relatively, I choose the biggest 3500 anime pictures as anime trainset and testset.

You can download this 2*3500 dataset from here.

This dataset still can be improved, especially the selfie dataset. If the angle and position can be more corresponding with the anime, I think the preference will be better. as a matter of fact, these samples manually picked above are those selfies relatively good.

from ugatit.

Here's my dataset:

• selfie
• anime

And I did some preparations:

According to the paper 5.2. Dataset, I wrote this script and selected 3400 female selfies(but the selfie labels seems contains some errors so there's still some male selfies) as trainset and 100 as testset. relatively, I choose the biggest 3500 anime pictures as anime trainset and testset.

You can download this 2*3500 dataset from here.
This dataset still can be improved, especially the selfie dataset. If the angle and position can be more corresponding with the anime, I think the preference will be better. as a matter of fact, these samples manually picked above are those selfies relatively good.

That's very nice summary, I think the dataset the author use was not so good for selfies, it's so disperse. As you said, maybe we should train with specific selfies, such as registration photo？

So, did you retrain the model with your new datasets? How's the result look like?

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Dataset for Anime images 5K from http://www.seeprettyface.com

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https://www.gwern.net/Danbooru2018 with some filters would be a great anime face dataset.

from ugatit.

Note that the nagadomi faces are also on Kaggle, and there's at least 2 anime face datasets derived from Danbooru2018: "Danbooru 2018 Anime Character Recognition Dataset" and SeePrettyFace.com: face dataset, in addition to the ones I made for my StyleGAN/BigGAN projects but haven't formally released.

The one of SeePrettyFace.com are a pain in the ass to download. Require's me to download baidu's downloader and register in Chinese (I believe).

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Hi, I have a question, I run python main.py --dataset selfie2anime but I not sure if it is working.
I know it is weird quiestion, I new in these

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Hi, I have a question, I run python main.py --dataset selfie2anime but I not sure if it is working.
I know it is weird quiestion, I new in these

'cuz the dataset is not yet included here

from ugatit.

You can also try this anime_faces dataset that I parsed from the tfrecords of twingan project.

As for a replacement of selfie dataset, you can try celebA and even CelebAMask-HQ(this can help you generate a high-res portraits dataset without backgrounds)

In the directory structure of README.md doesn't it say that the training set should be jpg or png's? (haven't read the source code).

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1. How to setup a checkpoint? it keeps saying loading checkpoint failed.
2. i'm using GTX1060 6GB. Should I readjust batch_size to avoid 'resource exhausted' issue?

Thx

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@sdy0803
You can change checkpoint save frequency in main.py. If you have any checkpoint saved, it will probably load successfully.

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@sdy0803
You can change checkpoint save frequency in main.py. If you have any checkpoint saved, it will probably load successfully.

The thing is that i find my GTX 1060 6GB cannot even run through the whole training progress with a miserable training dataset size of 140 pictures

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According to #3
Their setup was

One NVIDIA V100 32GB GPU.
It takes 4 days.

So it is not so surprising that it will take much longer with 1060.
I finished the training by significantly reducing number of epoch and training iteration (obviously produced terrible results). If you just want to finish training, then you can try the same.

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thx. let me give it one more try

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You can also try this anime_faces dataset that I parsed from the tfrecords of twingan project.

As for a replacement of selfie dataset, you can try celebA and even CelebAMask-HQ(this can help you generate a high-res portraits dataset without backgrounds)

Thanks for your sharing. Did you retrain the model with your anime dataset? Since they are in low resolution, did it affects?

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Dataset for Anime images 5K from http://www.seeprettyface.com

link of google drives is dead, can you update the link?

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