Skip to content

visualize training result for mmdetection 訓練文件可視化, PR curve绘制, F1-score计算

License

Notifications You must be signed in to change notification settings

cyz-951024/mmdetection_visualize

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

16 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

mmdetection_visualize_v1

It's a very simple version for visualizing the training result produced by mmdetection

Update

2019.8.16 ----- PR_curve, F_measure for VOC dataset

Readme

The program supports drawing six training result and the most important evaluation tool:PR curve(only for VOC now)

  1. loss_rpn_bbox

  2. loss_rpn_cls

  3. loss_bbox

  4. loss_cls

  5. loss

  6. acc

  7. PR_curve

  8. F-measure

Installation

  1. Clone it
    git clone https://github.com/Stephenfang51/mmdetection_visualize

There will be total 5 files(json directory, output directory, visualize.py, mean_ap_visualize.py, voc_eval_visualize.py)

  • put voc_eval_visualize.py under /mmdetection/tools/

  • put mean_ap_visualize.py under mmdetection/mmdet/core/evaluation/

How to use

six training result

  1. After training finished, you will have work_dir directory in your mmdetection directory
  2. take the latest json file and put into json directory in mmditection_visualize directory
  3. command python visualize.py json/xxxxxxxlog.json in terminal
  4. check the output directory, Done !

PR curve and F-measure

  1. make sure voc_eval_visualize.py and mean_ap_visualize.py settled down
  2. command as usual like python tools/voc_eval_visualize.py {your pkl file} {your network configs file}
    • example python tools/voc_eval_visualize.py result.pkl ./configs/faster_rcnn_r101_fpn_1x.py
  3. check the /mmdetection main directory, you will see the PR_curve_each_class.png there, Done !

About

visualize training result for mmdetection 訓練文件可視化, PR curve绘制, F1-score计算

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%