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  • Rank 243,368 (Top 5 %)
  • Language
    Python
  • Created over 3 years ago
  • Updated almost 2 years ago

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Repository Details

A new version of YOLOv1

PyTorch_YOLOv1

这个YOLOv1项目是配合我在知乎专栏上连载的《YOLO入门教程》而创建的:

https://zhuanlan.zhihu.com/c_1364967262269693952

感兴趣的小伙伴可以配合着上面的专栏来一起学习,入门目标检测。

当然,这里也诚挚推荐我的另一个YOLO项目,训练更加稳定,性能更好呦

https://github.com/yjh0410/PyTorch_YOLO-Family

配置环境

  • 我们建议使用anaconda来创建虚拟环境:
conda create -n yolo python=3.6
  • 然后,激活虚拟环境:
conda activate yolo
  • 配置环境: 运行下方的命令即可一键配置相关的深度学习环境:
pip install -r requirements.txt 

网络结构

  • Backbone: ResNet-18
  • Neck: SPP

训练所使用的tricks

  • 多尺度训练 (multi-scale)

数据集

VOC2007与VOC2012数据集

读者可以从下面的百度网盘链接来下载VOC2007和VOC2012数据集

链接:https://pan.baidu.com/s/1qClcQXSXjP8FEnsP_RrZjg

提取码:zrcj

读者会获得 VOCdevkit.zip压缩包, 分别包含 VOCdevkit/VOC2007VOCdevkit/VOC2012两个文件夹,分别是VOC2007数据集和VOC2012数据集.

实验结果

VOC2007 test 测试集

Model Input size mAP Weight
YOLOv1 320×320 64.6 -
YOLOv1 416×416 69.6 -
YOLOv1 512×512 72.2 -
YOLOv1 608×608 73.3 github

大家可以点击表格中的github来下载模型权重文件。

训练模型

运行下方的命令可开始在VOC数据集上进行训练:

python train.py \
        --cuda \
        -d voc \
        -ms \
        -bs 16 \
        -accu 4 \
        --lr 0.001 \
        --max_epoch 150 \
        --lr_epoch 90 120 \

其中,-bs 16表示我们设置batch size为16,-accu 4表示我们累加梯度4次,以此来近似使用64 batch size的训练效果。 倘若使用者将-bs设置更小,如8,请务必将-accu也做相应的调整,如8,以确保-bs x -accu = 64,否则,可能会出现训练不稳定的问题。

测试模型

运行下方的命令可开始在VOC数据集上进行训练:

python test.py \
        --cuda \
        -d voc \
        -size 416 \
        --weight path/to/weight \

验证模型

运行下方的命令可开始在VOC数据集上进行训练:

python eval.py \
        --cuda \
        -d voc \
        -size 416 \
        --weight path/to/weight \

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