search

derronqi / yolov7-face

yolov7 face detection with landmark
GNU General Public License v3.0
406 stars 102 forks source link

Retraining of Yolov7 Model on Given Dataset #19

Open NaeemKhanNiazi opened 1 year ago

NaeemKhanNiazi commented 1 year ago

@derronqi Thank you for very good work .I am retraining the your model with your given dataset. I am running following command

!python3 train.py --device 0 --data data/widerface.yaml --img 640 640 --cfg cfg/yolov7-face.yaml --weights yolov7-w6.pt --cache-images --hyp data/hyp.face.yaml --batch-size 8

But I am getting following logs 

     0/299     7.46G    0.1189   0.05726         0   0.05056  0.008458    0.2352        49       640         0         0         0         0         0  0.006643         0
     1/299     7.43G    0.1111   0.04434         0   0.03364  0.007088    0.1962        40       640         0         0         0         0         0  0.007927         0
     2/299     7.43G   0.08521   0.03891         0   0.01996  0.005892      0.15       107       640         0         0         0         0         0   0.01211         0
     3/299     7.43G   0.07363    0.0367         0   0.01529   0.00532    0.1309        50       640         0         0         0         0         0   0.01354         0
     4/299     7.43G   0.06879   0.03564         0   0.01332  0.004998    0.1227       115       640         0         0         0         0         0   0.01686         0
     5/299     7.43G   0.06605   0.03398         0   0.01222  0.004732     0.117       184       640         0         0         0         0         0   0.01902         0
     6/299     7.43G   0.06441   0.03353         0   0.01157  0.004533     0.114        26       640         0         0         0         0         0   0.01897         0
     7/299     7.43G    0.0632    0.0342         0   0.01113  0.004326    0.1129       108       640         0         0         0         0         0   0.01888         0
     8/299     7.43G   0.06179   0.03387         0   0.01074  0.004169    0.1106       177       640         0         0         0         0         0   0.01998         0
     9/299     7.43G   0.06074   0.03323         0   0.01038  0.004025    0.1084        66       640         0         0         0         0         0   0.02078         0
    10/299     7.43G   0.06036   0.03261         0   0.01021  0.003909    0.1071        69       640         0         0         0         0         0   0.01992         0
    11/299     7.43G   0.05991   0.03347         0  0.009964  0.003877    0.1072        64       640         0         0         0         0         0   0.02125         0
    12/299     7.43G   0.05891   0.03231         0  0.009774  0.003759    0.1048        73       640         0         0         0         0         0   0.02161         0
    13/299     7.43G   0.05861    0.0327         0  0.009598   0.00373    0.1046        73       640         0         0         0         0         0   0.02158         0
    14/299     7.43G   0.05811   0.03283         0  0.009474   0.00368    0.1041       106       640         0         0         0         0         0   0.02172         0
    15/299     7.43G   0.05784   0.03212         0  0.009277  0.003649    0.1029        58       640         0         0         0         0         0   0.02172         0
    16/299     7.43G   0.05803   0.03266         0  0.009239  0.003596    0.1035        52       640         0         0         0         0         0    0.0223         0
    17/299     7.43G   0.05772   0.03241         0  0.009141   0.00359    0.1029        71       640         0         0         0         0         0   0.02173         0
    18/299     7.43G   0.05722   0.03202         0  0.008997  0.003549    0.1018        75       640         0         0         0         0         0   0.02224         0
    19/299     7.43G   0.05693   0.03212         0  0.008923  0.003521    0.1015        22       640         0         0         0         0         0   0.02232         0
    20/299     7.43G   0.05669   0.03197         0  0.008885  0.003536    0.1011        47       640         0         0         0         0         0   0.02232         0
    21/299     7.43G   0.05651   0.03232         0  0.008761  0.003488    0.1011        79       640         0         0         0         0         0   0.02256         0
    22/299     7.43G   0.05688   0.03258         0  0.008749  0.003523    0.1017        42       640         0         0         0         0         0    0.0224         0

Accuracy is not improving after 22 epoches. Can you give any Readme file to retrain the your network on your given dataset. Thanks
ashish-roopan commented 5 months ago

@NaeemKhanNiazi which dataset did you use?