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Introduction

this repository is the implementation of MTCNN in MXnet

  • core: core routines for MTCNN training and testing.
  • tools: utilities for training and testing
  • data: Refer to Data Folder Structure for dataset reference. Usually dataset contains images and imglists
  • model: Folder to save training symbol and model
  • prepare_data: scripts for generating training data for pnet, rnet and onet

Useful information

You're required to modify mxnet/src/regression_output-inl.h according to mxnet_diff.patch before using the code for training.

  • Dataset format The images used for training are stored in ./data/dataset_name/images/ The annotation file is placed in ./data/dataset_name/imglists/

    • For training: Each line of the annotation file states a training sample.
      The format is: [path to image] [cls_label] [bbox_label]
      cls_label: 1 for positive, 0 for negative, -1 for part face.
      bbox_label are the offset of x1, y1, x2, y2, calculated by (xgt(ygt) - x(y)) / width(height)
      An example would be 12/positive/28 1 -0.05 0.11 -0.05 -0.11.
      Note that all the strings are seperated by space.

    • For testing: Similar to training but only path-to-image is needed.

  • Data Folder Structure (suppose root is data)

cache (created by imdb)
-- name + image set + gt_roidb
-- results (created by detection and evaluation)
mtcnn # contains images and anno for training mtcnn
-- images
---- 12 (images of size 12 x 12, used by pnet)
---- 24 (images of size 24 x 24, used by rnet)
---- 48 (images of size 48 x 48, used by onet)
-- imglists 
---- train_12.txt
---- train_24.txt
---- train_48.txt
custom (datasets for testing) 
-- images
-- imglists
---- image_set.txt
  • Scripts to generate training data(from wider face dataset)
    • run wider_annotations/transform.m (or transform.py) to get the annotation file of the format we need.
    • gen_pnet_data.py: obtain training samples for pnet
    • gen_hard_example.py: prepare hard examples. you can set test_mode to "pnet" to get training data for rnet, or set test_mode to "rnet" to get training data for onet.
    • gen_imglist.py: ramdom sample images generated by gen_pnet_data.py or gen_hard_example.py to form training set.

Results

image

License

MIT LICENSE

Reference

Kaipeng Zhang, Zhanpeng Zhang, Zhifeng Li, Yu Qiao , " Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks," IEEE Signal Processing Letter