Skip to content

Latest commit

 

History

History
executable file
·
131 lines (79 loc) · 4.28 KB

README.md

File metadata and controls

executable file
·
131 lines (79 loc) · 4.28 KB
Raw

DSFD

introduction

A tensorflow implement dsfd, and there is something different with the origin paper.

It‘s a ssd-like object detect framework, but slightly different, combines lots of tricks for face detection, such as dual-shot, dense anchor match, FPN,FEM and so on.

Now it is mainly optimised about face detection,and borrows some codes from other repos.

Ps, the code maybe not that clear, please be patience, i am still working on it, and forgive me for my poor english :)

** contact me if u have question [email protected] **

The evaluation results are based on vgg with batchsize(2x6),pretrained model can be download from

widerface val set

Easy MAP Medium MAP hard MAP
0.942 0.935 0.880
fddb
0.987

requirment

  • tensorflow1.12

  • tensorpack (for data provider)

  • opencv

  • python 3.6

useage

train

  1. download widerface data from http://shuoyang1213.me/WIDERFACE/ and release the WIDER_train, WIDER_val and wider_face_split into ./WIDER, then run python prepare_wider_data.pyit will produce train.txt and val.txt (if u like train u own data, u should prepare the data like this: ...../9_Press_Conference_Press_Conference_9_659.jpg| 483(xmin),195(ymin),735(xmax),543(ymax),1(class) ...... one line for one pic, caution! class should start from 1, 0 means bg)

  2. download the imagenet pretrained vgg16 model from http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz release it in the root dir,

  3. but if u want to train from scratch set config.MODEL.pretrained_model=None,

  4. if recover from a complet pretrained model set config.MODEL.pretrained_model='yourmodel.ckpt',config.MODEL.continue_train=True

  5. then, run:

    python train.py

    and if u want to check the data when training, u could set vis in train_config.py as True

  6. After training, run:

    python tools/auto_freeze.py

    it reads the checkpoint file and produces detector.pb .

evaluation

** fddb **

    python model_eval/fddb.py [--model [TRAINED_MODEL]] [--data_dir [DATA_DIR]]
                          [--split_dir [SPLIT_DIR]] [--result [RESULT_DIR]]
    --model              Path of the saved model,default ./model/detector.pb
    --data_dir           Path of fddb all images
    --split_dir          Path of fddb folds
    --result             Path to save fddb results

example python model_eval/fddb.py --model model/detector.pb --data_dir 'fddb/img/' --split_dir fddb/FDDB-folds/ --result 'result/'

** widerface **

    python model_eval/wider.py [--model [TRAINED_MODEL]] [--data_dir [DATA_DIR]]
                           [--result [RESULT_DIR]]
    --model              Path of the saved model,default ./model/detector.pb
    --data_dir           Path of WIDER
    --result             Path to save WIDERface results

example python model_eval/wider.py --model model/detector.pb --data_dir 'WIDER/WIDER_val/' --result 'result/'

visualization

A demo

(caution: i dont know where the demo picture comes from, if u think it's a tort, i would like to delete it.)

if u get a trained model, run python tools/auto_freeze.py, it will read the checkpoint file in ./model, and produce detector.pb, then

python vis.py

u can check th code in vis.py to make it runable, it's simple.

details

anchor

if u like to show the anchor stratergy, u could simply run :

python lib/core/anchor/anchor.py

it will draw the anchor one by one,

References

DSFD: Dual Shot Face Detector