sfd.pytorch
sfd implementation for face recognition in pytorch. Paper at: SFD: Single Shot Scale-invariant Face Detector
Requirements
- Python 3.6
- Pytorch 0.4
TODOs
- Training on wider faces
- Inference tools and API
- Non-maximum suppression at reference.
- Evaluation.
- Image augmentation.
- Multi-class detection.
Detection
The detector.py is executable and programmable, see inference.ipynb for a quick look at how to use the detector API. Using the following command for directly use it in the command line.
python3 detector.py --image ./image/test.jpg --model ./epoch_41.pth.tar
The trained model epoch_41.pth.tar can be downloaded from Baidu Yun or Google Drive.
The detector will draw bounding boxes and the result is showing bellow
Train
To train with the wider_face dataset, download and extract everything in one directory named wider_face. The file trees should then look like this,
└── wider_face
├── Submission_example.zip
├── wider_face_split
│ ├── readme.txt
│ ├── wider_face_test_filelist.txt
│ ├── wider_face_test.mat
│ ├── wider_face_train_bbx_gt.txt
│ ├── wider_face_train.mat
│ ├── wider_face_val_bbx_gt.txt
│ └── wider_face_val.mat
├── wider_face_split.zip
├── WIDER_test
│ └── images
├── WIDER_test.zip
├── WIDER_train
│ └── images
├── WIDER_train.zip
├── WIDER_val
│ └── images
└── WIDER_val.zip
Now in the config.py, set the DATASET_DIR to the path of wider_face, and set the LOG_DIR to whatever but a existed directory. Now it's ready to train with the following command,
python3 main.py # there is no stdout
The training logging is in LOG_DIR/log.txt, and models will be saved at LOG_DIR/models/epoch_xx.pth. There are many options in config.py(including learning rate or resumption) for you to tweak to get a better model.