From 50e411320563894d411b0c37d37cb16105a908af Mon Sep 17 00:00:00 2001
From: carp <25677564+carp@users.noreply.github.com>
Date: Mon, 13 Jul 2020 13:40:11 -0400
Subject: removing submodule

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-# Anime-Face-Detector
-A Faster-RCNN based anime face detector.
-
-This detector is trained on 6000 training samples and 641 testing samples, randomly selected from the dataset which is crawled from top 100 [pixiv daily ranking](https://www.pixiv.net/ranking.php?mode=daily).  
-
-Thanks to [OpenCV based Anime face detector](https://github.com/nagadomi/lbpcascade_animeface) written by nagadomi, which helps labelling the data. 
-
-The original implementation of Faster-RCNN using Tensorflow can be found [here](https://github.com/endernewton/tf-faster-rcnn)
-
-## Dependencies
-- Python 3.6 or 3.7
-- `tensorflow` < 2.0
-- `opencv-python`
-- `cython` (optional, can be ignored with additional `-nms-type PY_NMS` argument)
-- Pre-trained ResNet101 model
-
-## Usage
-1. Clone this repository
-    ```bash
-    git clone https://github.com/qhgz2013/anime-face-detector.git
-    ```
-2. Download the pre-trained model  
-    Google Drive: [here](https://drive.google.com/open?id=1WjBgfOUqp4sdRd9BHs4TkdH2EcBtV5ri)    
-    Baidu Netdisk: [here](https://pan.baidu.com/s/1bvpCp1sbD7t9qnta8IhpmA)  
-3. Unzip the model file into `model` directory
-4. Build the CPU NMS model (skip this step if use PY_NMS with argument: `-nms-type PY_NMS`)
-    ```bash
-    make clean
-    make
-    ```
-   If using Windows Power Shell, type `cmd /C make.bat` to run build script.
-5. Run the demo as you want
-    - Visualize the result (without output path):
-        ```bash
-        python main.py -i /path/to/image.jpg
-        ```
-    - Save results to a json file
-        ```bash
-        python main.py -i /path/to/image.jpg -o /path/to/output.json
-        ```
-        Format: `{"image_path": [{"score": predicted_probability, "bbox": [min_x, min_y, max_x, max_y]}, ...], ...}`
-        Sample output file:
-        ```json
-        {"/path/to/image.jpg": [{"score": 0.9999708, "bbox": [551.3375, 314.50253, 729.2599, 485.25674]}]}
-        ```
-    - Detecting a whole directory with recursion
-        ```bash
-        python main.py -i /path/to/dir -o /path/to/output.json
-        ```
-    - Customize threshold
-        ```bash
-        python main.py -i /path/to/image.jpg -nms 0.3 -conf 0.8
-        ```
-    - Customize model path
-        ```bash
-        python main.py -i /path/to/image.jpg -model /path/to/model.ckpt
-        ```
-    - Customize nms type (supports CPU_NMS and PY_NMS, not supports GPU_NMS because of the complicated build process for Windows platform)
-        ```bash
-        python main.py -i /path/to/image.jpg -nms-type PY_NMS
-        ```
-
-## Results
-**Mean AP for this model: 0.9086**
-
-![](./asset/sample1.png)
-Copyright info: [東方まとめ](https://www.pixiv.net/member_illust.php?mode=medium&illust_id=54275439) by [羽々斬](https://www.pixiv.net/member.php?id=2179695)
-
-![](./asset/sample2.png)
-Copyright info: [【C94】桜と刀](https://www.pixiv.net/member_illust.php?mode=medium&illust_id=69797346) by [幻像黒兎](https://www.pixiv.net/member.php?id=4462245)
-
-![](./asset/sample3.png)
-Copyright info: [アイドルマスター　シンデレラガールズ](https://www.pixiv.net/member_illust.php?mode=medium&illust_id=69753772) by [我美蘭＠１日目 東A-40a](https://www.pixiv.net/member.php?id=2003931)
-
-## About training
-
-This model is directly trained by [Faster-RCNN](https://github.com/endernewton/tf-faster-rcnn), with following argument:
-```bash
-python tools/trainval_net.py --weight data/imagenet_weights/res101.ckpt --imdb voc_2007_trainval --imdbval voc_2007_test --iters 60000 --cfg experiments/cfgs/res101.yml --net res101 --set ANCHOR_SCALES "[4,8,16,32]" ANCHOR_RATIOS "[1]" TRAIN.STEPSIZE "[50000]"
-```
-
-## Dataset
-
-We've uploaded the dataset to Google drive [here](https://drive.google.com/open?id=1nDPimhiwbAWc2diok-6davhubNVe82pr), dataset structure is similar to VOC2007 (used in original Faster-RCNN implementation).
-
-## Citation and declaration
-
-Feel free to cite this repo and dataset.  
-This work is not related to my research team and lab, just my personal interest.
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