/jinnan_yolo_baseline

使用YOLO目标检测做津南算法大赛

Primary LanguageJupyter NotebookMIT LicenseMIT

津南数字制造算法挑战赛YOLO Baseline

This YOLO V2 train on VOC datasets get more than 77mAp

Result:

the result training on jinnan datasets.

Model Ap.
Test Online 0.3319
Test Offline 0.363

Local Val Result:

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.363
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.720
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.344
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.417
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.372
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.412
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.285
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.421
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.422
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.422
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.422
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.422

Dependence:

  • Python3
  • Pytorch 1.0 or higher
  • cv2
  • coco API

Training:

download the pretrain model:

wget https://pjreddie.com/media/files/darknet53.conv.74

configure the config.py to set the dataset paths

python tools/split_datasets.py
python train_yolov2.py

valid the model:

python valid.py

Problem:

The yolov3 is on the bad performs.They are still some bugs.