## YOLOV4:You Only Look Once目标检测模型在pytorch当中的实现
---
## 目录
1. [仓库更新 Top News](#仓库更新)
2. [相关仓库 Related code](#相关仓库)
3. [性能情况 Performance](#性能情况)
4. [实现的内容 Achievement](#实现的内容)
5. [所需环境 Environment](#所需环境)
6. [文件下载 Download](#文件下载)
7. [训练步骤 How2train](#训练步骤)
8. [预测步骤 How2predict](#预测步骤)
9. [评估步骤 How2eval](#评估步骤)
10. [参考资料 Reference](#Reference)
## Top News
**`2022-04`**:**支持多GPU训练,新增各个种类目标数量计算,新增heatmap。**
**`2022-03`**:**进行了大幅度的更新,修改了loss组成,使得分类、目标、回归loss的比例合适、支持step、cos学习率下降法、支持adam、sgd优化器选择、支持学习率根据batch_size自适应调整、新增图片裁剪。**
BiliBili视频中的原仓库地址为:https://github.com/bubbliiiing/yolov4-pytorch/tree/bilibili
**`2021-10`**:**进行了大幅度的更新,增加了大量注释、增加了大量可调整参数、对代码的组成模块进行修改、增加fps、视频预测、批量预测等功能。**
## 相关仓库
| 模型 | 路径 |
| :----- | :----- |
YoloV3 | https://github.com/bubbliiiing/yolo3-pytorch
Efficientnet-Yolo3 | https://github.com/bubbliiiing/efficientnet-yolo3-pytorch
YoloV4 | https://github.com/bubbliiiing/yolov4-pytorch
YoloV4-tiny | https://github.com/bubbliiiing/yolov4-tiny-pytorch
Mobilenet-Yolov4 | https://github.com/bubbliiiing/mobilenet-yolov4-pytorch
YoloV5-V5.0 | https://github.com/bubbliiiing/yolov5-pytorch
YoloV5-V6.1 | https://github.com/bubbliiiing/yolov5-v6.1-pytorch
YoloX | https://github.com/bubbliiiing/yolox-pytorch
YoloV7 | https://github.com/bubbliiiing/yolov7-pytorch
YoloV7-tiny | https://github.com/bubbliiiing/yolov7-tiny-pytorch
## 性能情况
| 训练数据集 | 权值文件名称 | 测试数据集 | 输入图片大小 | mAP 0.5:0.95 | mAP 0.5 |
| :-----: | :-----: | :------: | :------: | :------: | :-----: |
| VOC07+12+COCO | [yolo4_voc_weights.pth](https://github.com/bubbliiiing/yolov4-pytorch/releases/download/v1.0/yolo4_voc_weights.pth) | VOC-Test07 | 416x416 | - | 89.0
| COCO-Train2017 | [yolo4_weights.pth](https://github.com/bubbliiiing/yolov4-pytorch/releases/download/v1.0/yolo4_weights.pth) | COCO-Val2017 | 416x416 | 46.1 | 70.2
## 实现的内容
- [x] 主干特征提取网络:DarkNet53 => CSPDarkNet53
- [x] 特征金字塔:SPP,PAN
- [x] 训练用到的小技巧:Mosaic数据增强、Label Smoothing平滑、CIOU、学习率余弦退火衰减
- [x] 激活函数:使用Mish激活函数
- [ ] ……balabla
## 所需环境
torch==1.2.0
## 文件下载
训练所需的yolo4_weights.pth可在百度网盘中下载。
链接: https://pan.baidu.com/s/1oXz13QwLx1lnXct538qL2Q
提取码: 16qc
yolo4_weights.pth是coco数据集的权重。
yolo4_voc_weights.pth是voc数据集的权重。
VOC数据集下载地址如下,里面已经包括了训练集、测试集、验证集(与测试集一样),无需再次划分:
链接: https://pan.baidu.com/s/19Mw2u_df_nBzsC2lg20fQA
提取码: j5ge
## 训练步骤
### a、训练VOC07+12数据集
1. 数据集的准备
**本文使用VOC格式进行训练,训练前需要下载好VOC07+12的数据集,解压后放在根目录**
2. 数据集的处理
修改voc_annotation.py里面的annotation_mode=2,运行voc_annotation.py生成根目录下的2007_train.txt和2007_val.txt。
3. 开始网络训练
train.py的默认参数用于训练VOC数据集,直接运行train.py即可开始训练。
4. 训练结果预测
训练结果预测需要用到两个文件,分别是yolo.py和predict.py。我们首先需要去yolo.py里面修改model_path以及classes_path,这两个参数必须要修改。
**model_path指向训练好的权值文件,在logs文件夹里。
classes_path指向检测类别所对应的txt。**
完成修改后就可以运行predict.py进行检测了。运行后输入图片路径即可检测。
### b、训练自己的数据集
1. 数据集的准备
**本文使用VOC格式进行训练,训练前需要自己制作好数据集,**
训练前将标签文件放在VOCdevkit文件夹下的VOC2007文件夹下的Annotation中。
训练前将图片文件放在VOCdevkit文件夹下的VOC2007文件夹下的JPEGImages中。
2. 数据集的处理
在完成数据集的摆放之后,我们需要利用voc_annotation.py获得训练用的2007_train.txt和2007_val.txt。
修改voc_annotation.py里面的参数。第一次训练可以仅修改classes_path,classes_path用于指向检测类别所对应的txt。
训练自己的数据集时,可以自己建立一个cls_classes.txt,里面写自己所需要区分的类别。
model_data/cls_classes.txt文件内容为:
```python
cat
dog
...
```
修改voc_annotation.py中的classes_path,使其对应cls_classes.txt,并运行voc_annotation.py。
3. 开始网络训练
**训练的参数较多,均在train.py中,大家可以在下载库后仔细看注释,其中最重要的部分依然是train.py里的classes_path。**
**classes_path用于指向检测类别所对应的txt,这个txt和voc_annotation.py里面的txt一样!训练自己的数据集必须要修改!**
修改完classes_path后就可以运行train.py开始训练了,在训练多个epoch后,权值会生成在logs文件夹中。
4. 训练结果预测
训练结果预测需要用到两个文件,分别是yolo.py和predict.py。在yolo.py里面修改model_path以及classes_path。
**model_path指向训练好的权值文件,在logs文件夹里。
classes_path指向检测类别所对应的txt。**
完成修改后就可以运行predict.py进行检测了。运行后输入图片路径即可检测。
## 预测步骤
### a、使用预训练权重
1. 下载完库后解压,在百度网盘下载yolo_weights.pth,放入model_data,运行predict.py,输入
```python
img/street.jpg
```
2. 在predict.py里面进行设置可以进行fps测试和video视频检测。
### b、使用自己训练的权重
1. 按照训练步骤训练。
2. 在yolo.py文件里面,在如下部分修改model_path和classes_path使其对应训练好的文件;**model_path对应logs文件夹下面的权值文件,classes_path是model_path对应分的类**。
```python
_defaults = {
#--------------------------------------------------------------------------#
# 使用自己训练好的模型进行预测一定要修改model_path和classes_path!
# model_path指向logs文件夹下的权值文件,classes_path指向model_data下的txt
# 如果出现shape不匹配,同时要注意训练时的model_path和classes_path参数的修改
#--------------------------------------------------------------------------#
"model_path" : 'model_data/yolo_weights.pth',
"classes_path" : 'model_data/coco_classes.txt',
#---------------------------------------------------------------------#
# anchors_path代表先验框对应的txt文件,一般不修改。
# anchors_mask用于帮助代码找到对应的先验框,一般不修改。
#---------------------------------------------------------------------#
"anchors_path" : 'model_data/yolo_anchors.txt',
"anchors_mask" : [[6, 7, 8], [3, 4, 5], [0, 1, 2]],
#---------------------------------------------------------------------#
# 输入图片的大小,必须为32的倍数。
#---------------------------------------------------------------------#
"input_shape" : [416, 416],
#---------------------------------------------------------------------#
# 只有得分大于置信度的预测框会被保留下来
#---------------------------------------------------------------------#
"confidence" : 0.5,
#---------------------
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