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yolov4训练visdrone记录

作者:互联网

准备

参见这篇,不再赘述,
注意labels有没有错误,如w,h为0,重复标注等,在转换代码中加入判断滤除即可。
数据只用了task1的图片集。

配置

anchors

./[darknet](https://github.com/AlexeyAB/darknet) detector calc_anchors data/visdrone.data -num_of_clusters 8 -width 800 -height 800

5, 10, 9, 23, 19, 17, 17, 41, 37, 32, 31, 72, 65, 65, 97,134
显存不够的将网络大小改为416,608等

visdrone.cfg

由于visdrone数据集大部分是中小目标,所以去掉yolov4最后一个yolo层,直接删除第二个yolo层以下即可,更新anchor,
classes改为10,num改为8表示两个scale,每个scale包含4个anchor,相应filters从255改为60((10+5)*4),最后一层配置如下:

[convolutional]
size=1
stride=1
pad=1
filters=60
activation=linear


[yolo]
mask = 4,5,6,7
anchors = 5, 10,   9, 23,  19, 17,  17, 41,  37, 32,  31, 72,  65, 65,  97,134
classes=10
num=8
jitter=.3
ignore_thresh = .7
truth_thresh = 1
scale_x_y = 1.1
iou_thresh=0.213
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
nms_kind=greedynms

训练(2080Ti, 32G, i5)

使用ultralytics的实现,内存小的去掉cache-images,指定img-size大小和网络大小相同可避免内部再次resize,需屏蔽以下行

assert math.fmod(imgsz_min, gs) == 0, '--img-size %g must be a %g-multiple' % (imgsz_min, gs)

如果中途中断则使用resume(官方不建议,直接重新train),应该是权重文件中存有optimizer等信息,如果担心修改参数后对再次训练有影响,可转换为darknet权重,
由于存在很多难分样本,将fl_gamma设为2可启用focal loss

python3 train_visdrone.py --cfg cfg/visdrone.cfg --data data/visdrone.data --weights weights/yolov4.conv.137 --cache-images  --img-size 800 800 --epochs 300 --batch-size 2
python3 train_visdrone.py --cfg cfg/visdrone.cfg --data data/visdrone.data --resume --cache-images  --img-size 800 800 --epochs 300 --batch-size 2
from models import *
convert('cfg/visdrone.cfg', 'weights/best.pt')

为了方便在Tensorboard中对比查看,可作如下改动:

# Tensorboard
if tb_writer:
    # tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss',
    #         'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/F1',
    #         'val/giou_loss', 'val/obj_loss', 'val/cls_loss']
    # for x, tag in zip(list(mloss[:-1]) + list(results), tags):
    #     tb_writer.add_scalar(tag, x, epoch)

    result_value = list(mloss[:-1]) + list(results)
    tb_writer.add_scalars('giou_loss', {'train': result_value[0], 'val': result_value[7]}, epoch)
    tb_writer.add_scalars('obj_loss', {'train': result_value[1], 'val': result_value[8]}, epoch)
    tb_writer.add_scalars('cls_loss', {'train': result_value[2], 'val': result_value[9]}, epoch)
    tb_writer.add_scalars('metrics_PR', {'precision': result_value[3], 'recall': result_value[4]}, epoch)
    tb_writer.add_scalars('metrics_AF', {'mAP_0.5': result_value[5], 'F1': result_value[6]}, epoch)

验证:

python3 test.py --cfg cfg/visdrone.cfg --data data/visdrone.data --weights weights/best.pt --img-size 800
               Class    Images   Targets         P         R   mAP@0.5        F1: 
                 all       548  3.88e+04     0.236     0.379     0.262     0.288
          pedestrian       548  8.84e+03      0.17     0.253     0.152     0.203
              people       548  5.12e+03     0.107     0.151    0.0496     0.125
             bicycle       548  1.29e+03     0.125      0.19    0.0895     0.151
                 car       548  1.41e+04     0.458     0.644     0.578     0.535
                 van       548  1.98e+03     0.196     0.539     0.376     0.287
               truck       548       750     0.259     0.489     0.342     0.338
            tricycle       548  1.04e+03     0.284     0.366     0.234      0.32
     awning-tricycle       548       532     0.166      0.28     0.108     0.208
                 bus       548       251     0.397     0.554     0.504     0.463
               motor       548  4.89e+03     0.198     0.325     0.187     0.246
Speed: 20.0/148.7/168.7 ms inference/NMS/total per 800x800 image at batch-size 16

修改visdrone.data中valid的路径为VisDrone2019-DET-test-dev所在路径可验证测试集

               Class    Images   Targets         P         R   mAP@0.5        F1: 
                 all  1.61e+03  7.51e+04     0.224     0.333     0.231     0.264
          pedestrian  1.61e+03   2.1e+04     0.121     0.141    0.0814      0.13
              people  1.61e+03  6.38e+03    0.0615    0.0593    0.0141    0.0604
             bicycle  1.61e+03   1.3e+03     0.141     0.144    0.0709     0.143
                 car  1.61e+03  2.81e+04      0.38     0.581     0.486      0.46
                 van  1.61e+03  5.77e+03     0.213     0.463     0.309     0.292
               truck  1.61e+03  2.66e+03      0.26     0.549     0.411     0.353
            tricycle  1.61e+03       530     0.134     0.275    0.0989      0.18
     awning-tricycle  1.61e+03       599     0.236     0.289      0.15     0.259
                 bus  1.61e+03  2.94e+03     0.536     0.611      0.58     0.571
               motor  1.61e+03  5.84e+03     0.161     0.222     0.112     0.187
Speed: 20.2/178.3/198.5 ms inference/NMS/total per 800x800 image at batch-size 16



可以看出在小目标上效果很差,有时间用rcnn试试。
最终配置和权重下载,提取码: 74s4

标签:03,yolov4,训练,cfg,result,visdrone,548,data
来源: https://www.cnblogs.com/chenzhengxi/p/13033124.html