RetinaDetection Object Detector

Introduction

RetinaDetector是基于RetinaFace修改的检测方法，原论文is a practical single-stage SOTA face detector which is initially described in arXiv technical report

Data

0. Organise the dataset directory as follows:

  data/retinaface/
    train/
      images/
      label.txt
    val/
      images/
      label.txt
    test/
      images/
      label.txt

Install

1. Install MXNet with GPU support.
2. Install Deformable Convolution V2 operator from Deformable-ConvNets if you use the DCN based backbone.
3. Type make to build cxx tools.

Training

Please check train.py for training.

1. Copy rcnn/sample_config.py to rcnn/config.py

为了获得更好的训练效果，可针对性的修改一些参数，如下：

config.TRAIN.MIN_BOX_SIZE = 10 #最小bbox
config.FACE_LANDMARK = False #使用landmark
config.USE_BLUR = False
config.BBOX_MASK_THRESH = 0
config.COLOR_MODE = 2 #增强
config.COLOR_JITTERING = 0.125

无效人脸的过滤，如下：

if (x2 - x1) < config.TRAIN.MIN_BOX_SIZE or (y2 - y1) < config.TRAIN.MIN_BOX_SIZE:
   continue
if self._split.startswith('train'):
   blur[ix] = values[19]
   if blur[ix] < 0.25:
      continue
if config.BBOX_MASK_THRESH > 0:
   if (x2 - x1) < config.BBOX_MASK_THRESH or (y2 - y1) < config.BBOX_MASK_THRESH:
      boxes_mask.append(np.array([x1, y1, x2, y2], np.float))
      continue
   if self._split.startswith('train'):
      if blur[ix] < 0.35:
         boxes_mask.append(np.array([x1, y1, x2, y2], np.float))
         continue

2. Download pretrained models and put them into model/.

   ImageNet ResNet50 (baidu cloud and dropbox).

   ImageNet ResNet152 (baidu cloud and dropbox).

3. Start training with sh train_model.sh.
   Before training, you can check the resnet network configuration (e.g. pretrained model path, anchor setting and learning rate policy etc..) in rcnn/config.py.

Testing

Please check test.py for testing.

Result

1. 缺陷检测

2. 人脸检测+人脸对齐

Models

人脸检测模型，比原版误检更低，角度较大和模糊超过0.6的face会自动忽略，更适合人脸识别的应用：click here.

ToDo

由于缺陷检测数据涉及私密性，缺陷检测的模型暂时不会释放

References

@inproceedings{[email protected],
year={2019}
}