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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
AI based multi-label girl image classification system, implemented by using TensorFlow.
| Date | Stars |
|---|---|
| 2026-07-24 | 2932 |
| 2026-07-25 | 2932 |
| 2026-07-28 | 2932 |
| 2026-07-30 | 2932 |
| 2026-08-06 | 2932 |
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# DeepDanbooru [](https://www.python.org/doc/versions/) [](https://opensource.org/licenses/MIT) [](http://kanotype.iptime.org:8003/deepdanbooru/) **DeepDanbooru** is anime-style girl image tag estimation system. You can estimate your images on my live demo site, [DeepDanbooru Web](https://apps.kanotype.net/deepdanbooru/). ## Requirements DeepDanbooru is written by Python 3.11. Following packages are need to be installed. - Click>=8.1.7 - numpy>=1.26.4 - requests>=2.32.3 - scikit-image>=0.24.0 - six>=1.16.0 - tensorflow>=2.17.0 - tensorflow-io>=0.31.0 Or just use `requirements.txt`. ``` > pip install -r requirements.txt ``` alternatively you can install it with pip. Note that by default, tensorflow is not included. To install it with tensorflow, add `tensorflow` extra package. ``` > # default installation > pip install . > # with tensorflow package > pip install .[tensorflow] ``` ## Usage 1. Prepare dataset. If you don't have, you can use [DanbooruDownloader](https://github.com/KichangKim/DanbooruDownloader) for download the dataset of [Danbooru](https://danbooru.donmai.us/). If you want to make your own dataset, see [Dataset Structure](#dataset-structure) section. 2. Create training project folder. ``` > deepdanbooru create-project [your_project_folder] ``` 3. Prepare tag list. If you want to use latest tags, use following command. It downloads tag from Danbooru server. (Need Danbooru account and API key) ``` > deepdanbooru download-tags [your_project_folder] --username [your_danbooru_account] --api-key [your_danbooru_api_key] ``` 4. (Option) Filtering dataset. If you want to train with optional tags (rating and score), you should convert it as system tags. ``` > deepdanbooru make-training-database [your_dataset_sqlite_path] [your_filtered_sqlite_path] ``` 5. Modify `project.json` in the project folder. You should change `database_path` setting to your actual sqlite file path. 6. Start training. ``` > deepdanbooru train-project [your_project_folder] ``` 7. Enjoy it. ``` > deepdanbooru evaluate [image_file_path or folder]... --project-path [your_project_folder] --allow-folder ``` ## Dataset Structure DeepDanbooru uses following folder structure for input dataset. SQLite file can be any name, but must be located in same folder to `images` folder. All of image files are located in sub-folder which named first 2 characters of its filename. ``` MyDataset/ ├── images/ │ ├── 00/ │ │ ├── 00000000000000000000000000000000.jpg │ │ ├── ... │ ├── 01/ │ │ ├── 01000000000000000000000000000000.jpg │ │ ├── ... │ └── ff/ │ ├── ff000000000000000000000000000000.jpg │ ├── ... └── my-dataset.sqlite ``` The core is SQLite database file. That file must be contains following table structure. ``` posts ├── id (INTEGER) ├── md5 (TEXT) ├── file_ext (TEXT) ├── tag_string (TEXT) └── tag_count_general (INTEGER) ``` The filename of image must be `[md5].[file_ext]`. If you use your own images, `md5` don't have to be actual MD5 hash value. `tag_string` is space splitted tag list, like `1girl ahoge long_hair`. `tag_count_general` is used for the project setting, `minimum_tag_count`. Images which has equal or larger value of `tag_count_general` are used for training. ## Project Structure **Project** is minimal unit for training on DeepDanbooru. You can modify various parameters for training. ``` MyProject/ ├── project.json └── tags.txt ``` `tags.txt` contains all tags for estimating. You can make your own list or download latest tags from Danbooru server. It is simple newline-separated file like this: ``` 1girl ahoge ... ```
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Kichang Kim · Japan
61
rachmadani haryono · Germany
13
WASEDA University
9
3
Ikko Eltociear Ashimine · Japan
1
1
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5fb52ff6a86bde5a, topic:tensorflow