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face_detection

README.md

Face Detection

This subdir includes face detection related codes. Some descriptions has been presented in repo README.md.

Recent Update

  • 2019.10.14 The model v2 can be tried to train nightly.
  • 2019.10.16 The model v2 can be trained normally.

Brief Introduction to Model Version

  • v1 - refer to the paper for details
  • v2 - the detection scale is 10-320 (vs 10-560 in v1), the number of layers is 20, the backbone is modified for faster inference. Refer to ./net_farm/naivenet_structures.xlsx for details.

Accuracy

on the way

Inference Latency

on the way

User Instructions

Now only for traning v2 nightly.

First, we introduce the functionality of each sub directory.

  • net_farm. This folder contains net definitions for all model versions.
  • metric_farm. This folder contains the metrics for training monitoring.
  • data_provider_farm. This folder contains the code of raw data processing/formatting/packing&unpacking.
  • data_iterator_farm. This folder contains the code of multi-threaded data prefetching. This is the most important part, since it describe the essence of LFFD!!!
  • config_farm. This folder contains the configurations of all model versions. The training is started by running the corresponding config python script.

Second, we present a common procedure for running the code for training (taking v2 as an example).

  1. prepare net model net_farm/naivenet.py
  2. prepare the training data by using the code in data_provider_farm. We provide a packed training data of WIDERFACE trainset. Please download from Data Download.
  3. adjust the code around the line 241 in data_iterator_farm/multithread_dataiter_for_cross_entropy_v2.
  4. set the variables in configuration py script in config_farm.
  5. run python configuration_10_320_20L_5scales_v2.py in config_farm directory.

Data Download

We have packed the training data of WIDERFACE train set. In the data, the faces less than 8 pixels are ignored, and some pure negative images cropped from the training images are also added. We provide three ways to download the packed data:

After you download the data, you can put it anywhere. Remember to set param_trainset_pickle_file_path variable in the configuration file. (we usually put the data into the folder: ./data_provider_farm/data_folder/)