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diff --git a/anime-face-detector/README.md b/anime-face-detector/README.md deleted file mode 100644 index 72a242e..0000000 --- a/anime-face-detector/README.md +++ /dev/null @@ -1,89 +0,0 @@ -# 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** - - -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) - - -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) - - -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. |