yolov5 训练crowded human 【visible body detection】
2024-06-11 03:56:33
0 前言
我使用的GPU平台：https://cloud.videojj.com/auth/register?inviter=18452&activityChannel=student_invite
知乎：
github：https://github.com/Whiffe/yolov5-visible-and-full-person-crowdhuman
b站：https://www.bilibili.com/video/BV1KY4y1r7TQ
arxiv：
在对拥挤人群（我应用场景是学生课堂）进行检测时，采用现有的模型代码，有一定问题，比如直接采用yolov5、yolov3、faster rcnn等，在拥挤场景的检测效果不佳，但使用crowded human数据集重训练后的yolov5，效果很好。crowded human数据集标签如下，有Head BBox、Visible BBox、Full BBox。如下图显示:
在拥挤人群中的检测，我发现Full BBox会额外把其他人框进来，那么就会出现一个框里出现多个人，
这里需要些张对比图
full body detection
visible body detection
但是我查的资料中，关于yolov5的检测结果只用了head detection和 full body detection ，并没有visible body detection。于是我使用yolov5对head detection、visible body detection进行重训练。
目录
- 0 前言
- 1 相关资料
- 1.1，CrowdHuman的论文
- 1.2，yolov5-crowdhuman的代码，这里是训练后的head detection和 full body detection 的模型，并没有 visible body detection。
- 1.3，这是githun中对CrowdHuman论文的翻译
- 1.4，这一个博客就非常重要了，是我能完成这篇博客的核心
- 1.5，这个代码链接是上一个博客所使用的代码工具
- 1.6 b站YOLOV5训练自己的目标检测模型的视频
- 2 crowded human数据集下载
- 2.1 官网数据集下载
- 2.2 数据集上传AI平台
- 3 YOLOv5-Tools
- 3.1 YOLOv5-Tools 安装
- 3.2 YOLOv5-Tools中关键文件
- 3.3 crowded human数据转化为coco数据集格式
- 3.4 转化后的结果展示
- 3.5 图像文件和标注文本文件 重构
- 4 yolov5
- 4.1 yolov5 安装
- 4.2 预训练模型
- 4.3 训练
- 4.4 demo测试
- 4.5 训练过程与结果的截图
- 4.6 实时查看GPU，CPU和内存使用情况
- 4.7 分析训练结果
- 5 其他目标检测网络
- 5.1 faster rcnn
- 5.2 yolov3
- 5.3 yolov5
- 5.3 yolov5 (crowded human,Full body)
- 5.3 yolov5 (crowded human,Visible body)
- 6 github 快速实现
- 6.1 yolov5-visible-and-full-person-crowdhuman
- 6.2 demo测试
1 相关资料
1.1，CrowdHuman的论文
CrowdHuman: A Benchmark for Detecting Human in a Crowd：https://arxiv.org/pdf/1805.00123.pdf
1.2，yolov5-crowdhuman的代码，这里是训练后的head detection和 full body detection 的模型，并没有 visible body detection。
yolov5-crowdhuman：https://github.com/deepakcrk/yolov5-crowdhuman
1.3，这是githun中对CrowdHuman论文的翻译
PaperWeekly/CrowdHuman.md：https://github.com/Mycenae/PaperWeekly/blob/master/CrowdHuman.md
1.4，这一个博客就非常重要了，是我能完成这篇博客的核心
目标检测 YOLOv5 CrowdHuman数据集格式转YOLOv5格式：https://blog.csdn.net/flyfish1986/article/details/115485814
1.5，这个代码链接是上一个博客所使用的代码工具
YOLOv5-Tools：https://gitcode.net/mirrors/shaoshengsong/YOLOv5-Tools/-/tree/main/CrowHuman2YOLO/data
1.6 b站YOLOV5训练自己的目标检测模型的视频
手把手教你使用YOLOV5训练自己的目标检测模型：https://www.bilibili.com/video/BV1YL4y1J7xz?p=1
2 crowded human数据集下载
2.1 官网数据集下载
CrowdHuman dataset下载链接：https://www.crowdhuman.org/download.html
下载后有这些文件：
2.2 数据集上传AI平台
我用的AI平台：https://cloud.videojj.com/auth/register?inviter=18452&activityChannel=student_invite
将数据集上传到AI平台中，一般就放在：/user-data 路径下
我是讲crowded human的数据集压缩为：crowdedhuman.zip，然后上传 /user-data/crowdedHuman中，上传方法在：数据传输https://cloud.videojj.com/handbook/guide/data_manage/#%E6%95%B0%E6%8D%AE%E4%BC%A0%E8%BE%93
3 YOLOv5-Tools
3.1 YOLOv5-Tools 安装
YOLOv5-Tools的功能之一就是讲crowded human转化为yolov5可以使用的数据集格式，即coco数据集格式。
YOLOv5-Tools代码链接：https://gitcode.net/mirrors/shaoshengsong/YOLOv5-Tools
我也将这个同步到了自己的github中：https://github.com/Whiffe/YOLOv5-Tools-main
也同步到了码云：https://gitee.com/YFwinston/YOLOv5-Tools-main
在AI平台中搭建项目
pytorch:1.8.0, python:3.8, CUDA:11.1.1
cd /home
git clone https://gitee.com/YFwinston/YOLOv5-Tools-main
3.2 YOLOv5-Tools中关键文件
YOLOv5-Tools中有2个关键文件：
YOLOv5-Tools/CrowHuman2YOLO/data/prepare_vbody_data.sh
YOLOv5-Tools/CrowHuman2YOLO/data/gen_vbody_txts.py
其中我在原作者的基础上的改动如下图（gen_vbody_txts.py中的内容），主要将fbox改为了vbox
3.3 crowded human数据转化为coco数据集格式
crowded human数据转化为coco数据集格式，执行下面的代码
转化前，需要安装zip，然后将/user-data/crowdedHuman/crowdedhuman.zip复制到/home/YOLOv5-Tools-main/CrowHuman2YOLO/data/raw
apt-get update
apt-get install zip
apt-get install unzipcp /user-data/crowdedHuman/crowdedhuman.zip /home/YOLOv5-Tools-main/CrowHuman2YOLO/data/rawcd /home/YOLOv5-Tools-main/CrowHuman2YOLO/data/raw
unzip crowdedhuman.zip
rm crowdedhuman.zipcd /home/YOLOv5-Tools-main/CrowHuman2YOLO/data/
bash ./prepare_vbody_data.sh 608x608
3.4 转化后的结果展示
然后目录/home/YOLOv5-Tools-main/CrowHuman2YOLO/data/raw/Images的结构如下
Images
├── 273271,1017c000ac1360b7.jpg
├── 273271,10355000e3a458a6.jpg
├── 273271,1039400091556057.jpg
├── 273271,104ec00067d5b782.jpg
├── ...
└── 284193,ff25000b6a403e9.jpg
使用文件计数命令可以数出Images文件夹下文件数量
ls -l|grep "^-"| wc -l
结果是：19370
还有个路径也有生成结果：/home/YOLOv5-Tools-main/CrowHuman2YOLO/data/crowdhuman-608x608
结构如下
crowdhuman-608x608
├── 273271,1017c000ac1360b7.jpg
├── 273271,1017c000ac1360b7.txt
├── 273271,10355000e3a458a6.jpg
├── 273271,10355000e3a458a6.txt
├── ...
├── 284193,ff25000b6a403e9.jpg
├── 284193,ff25000b6a403e9.txt
├── test.txt
└── train.txt
我们看看273271,1017c000ac1360b7.txt的内容
再看看test.txt的内容
再看看train.txt的内容
讲crowded human生成coco标准文件夹格式
3.5 图像文件和标注文本文件 重构
3.3节只是将crowded human数据集转化为了coco结构的数据集，但是图像文件和标注文本文件需要重构，
首先按照下面路径创建文件夹：
to_train_img_path = '/user-data/crowdedHuman/images/train/'
to_val_img_path = '/user-data/crowdedHuman/images/val/'
to_train_label_path = '/user-data/crowdedHuman/labels/train/'
to_val_label_path = '/user-data/crowdedHuman/labels/val/'
然后重构命令：
cd /home/YOLOv5-Tools-main/CrowHuman2YOLO/data/
python gen_coco_stru.py
结果如下：
user-data/crowdedHuman/images/
crowdedHuman
├── Images
│ ├── train
│ │ ├── 273271,1017c000ac1360b7.jpg
│ │ ├── 273271,10355000e3a458a6.jpg
│ │ ├── 273271,1039400091556057.jpg
│ │ ├── ...
│ │ └── 284193,ff01000db10348e.jpg
│ ├── val
│ │ ├── 273271,104ec00067d5b782.jpg
│ │ ├── 273271,10f400006b6fb935.jpg
│ │ ├── 273271,118910008d823f61.jpg
│ │ ├── ...
│ │ └── 284193,ff25000b6a403e9.jpg
└── labels ├── train│ ├── 273271,1017c000ac1360b7.txt│ ├── 273271,10355000e3a458a6.txt│ ├── 273271,1039400091556057.txt│ ├── ...│ └── 284193,ff01000db10348e.txt└── val├── 273271,104ec00067d5b782.txt├── 273271,10f400006b6fb935.txt├── 273271,118910008d823f61.txt├── ...└── 284193,ff25000b6a403e9.txt
4 yolov5
4.1 yolov5 安装
cd /home
git clone https://gitee.com/YFwinston/yolov5.git
cd yolov5
pip install -r requirements.txt
pip install opencv-python-headless==4.1.2.30
mkdir -p /root/.config/Ultralytics
wget https://ultralytics.com/assets/Arial.ttf -O /root/.config/Ultralytics/Arial.ttf
4.2 预训练模型
我会使用预训练模型对crowded human中的head、visible body进行训练，我采用yolov5m的网络架构与预训练模型：https://github.com/ultralytics/yolov5/releases
执行下面的代码
cd /home/yolov5
mkdir pretrained
cd pretrained
wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m.pt4.3 训练
训练需要在 /home/yolov5/data 下创建：crowdhuman.yaml，其内容如下：
执行下面代码：
cd /home/yolov5/data
touch crowdhuman.yaml
train: /user-data/crowdedHuman/images/train
val: /user-data/crowdedHuman/images/val
#test: test.txt#number of classes
nc: 2# class names
names: ['head', 'Vperson']执行下面的训练代码：
cd /home/yolov5/
python train.py --data ./data/crowdhuman.yaml --cfg ./models/yolov5m.yaml --weights ./pretrained/yolov5m.pt --batch-size 16 --epochs 2004.4 demo测试
cd /home/yolov5
python ./detect.py --weights ./crowdhuman_yolov5m_visible_body.pt --source ./1.jpeg --save-txt --save-conf --hide-labels --line-thickness 4 --classes 14.5 训练过程与结果的截图
从结果上来看，我使用的是默认epoch，300次，但是只训练了286次，原因是第186次的训练达到最优，之后100次再也没有超过186的结果，所以训练停止。
训练时间：31.2小时
4.6 实时查看GPU，CPU和内存使用情况
如果在训练过程中，实时查看GPU，CPU和内存使用情况执行下面的代码：
pip install gpustat
gpustat -cp -i 1
4.7 分析训练结果
confusion_matrix.png
F1_curve.png
hyp.yaml
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 0.05
cls: 0.5
cls_pw: 1.0
obj: 1.0
obj_pw: 1.0
iou_t: 0.2
anchor_t: 4.0
fl_gamma: 0.0
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
mosaic: 1.0
mixup: 0.0
copy_paste: 0.0labels_correlogram.jpg
labels.jpg
opt.yaml
weights: ./pretrained/yolov5m.pt
cfg: ./models/yolov5m.yaml
data: ./data/crowdhuman.yaml
hyp: data/hyps/hyp.scratch-low.yaml
epochs: 300
batch_size: 16
imgsz: 640
rect: false
resume: false
nosave: false
noval: false
noautoanchor: false
noplots: false
evolve: null
bucket: ''
cache: null
image_weights: false
device: ''
multi_scale: false
single_cls: false
optimizer: SGD
sync_bn: false
workers: 8
project: runs/train
name: exp
exist_ok: false
quad: false
cos_lr: false
label_smoothing: 0.0
patience: 100
freeze:
- 0
save_period: -1
local_rank: -1
entity: null
upload_dataset: false
bbox_interval: -1
artifact_alias: latest
save_dir: runs/train/exp5P_curve.png
PR_curve.png
R_curve.png
results.csv
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train_batch0.jpg
train_batch1.jpg
train_batch2.jpg
val_batch0_labels.jpg
val_batch0_pred.jpg
val_batch1_labels.jpg
val_batch1_pred.jpg
val_batch2_labels.jpg
val_batch2_pred.jpg
5 其他目标检测网络
cd /home
git clone https://gitee.com/YFwinston/mmdetection.git
cd mmdetection
pip install mmcv-full==1.3.17 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html
pip install opencv-python-headless==4.1.2.30
pip install -r requirements/build.txt
pip install -v -e .
5.1 faster rcnn
mkdir ./models
wget https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth -O ./models/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth
python ./demo/image_demo.py ./demo/1.jpeg ./configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py ./models/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth --out-file ./demo/out.jpg
可以发现，mmdetection默认的检测框大小不合适，看不清楚，所以要做修改
修改方式，我参考的：MMDetection V2.0 可视化参数修改https://blog.csdn.net/i013140225/article/details/109819366
找到mmdet/models/detectors/base.py文件，修改class BaseDetector()中的show_result()函数的输入参数
cd cd /home/mmdetection
python ./demo/image_demo.py ./demo/1.jpeg ./configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py ./models/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth --out-file ./demo/out.jpg
5.2 yolov3
cd cd /home/mmdetection
wget https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-416_273e_coco/yolov3_d53_mstrain-416_273e_coco-2b60fcd9.pth -O ./models/yolov3_d53_mstrain-416_273e_coco-2b60fcd9.pthpython ./demo/image_demo.py ./demo/1.jpeg ./configs/yolo/yolov3_d53_mstrain-416_273e_coco.py ./models/yolov3_d53_mstrain-416_273e_coco-2b60fcd9.pth --out-file ./demo/out.jpg
5.3 yolov5
cd /home/yolov5/
wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m.pt
python ./detect.py --weights ./yolov5m.pt --source ./1.jpeg --save-txt --save-conf --hide-labels --line-thickness 4 --classes 0
5.3 yolov5 (crowded human,Full body)
需要提前下载好权重：https://github.com/deepakcrk/yolov5-crowdhuman
cd /home/yolov5
python ./detect.py --weights ./crowdhuman_yolov5m_full_body.pt --source ./1.jpeg --save-txt --save-conf --hide-labels --line-thickness 4 --classes 0
5.3 yolov5 (crowded human,Visible body)
权重就是本文所训练的权重
python ./detect.py --weights ./crowdhuman_yolov5m_visible_body.pt --source ./1.jpeg --save-txt --save-conf --hide-labels --line-thickness 4 --classes 1
6 github 快速实现
6.1 yolov5-visible-and-full-person-crowdhuman
我已经把训练好的权重放在了google云盘中，需要通过github对应链接下载，然后上传到AI平台中。
github：https://github.com/Whiffe/yolov5-visible-and-full-person-crowdhuman
权重下载地址：https://drive.google.com/file/d/1VJtrdE85Wc4xSZXqAPUkWABLResUYG8V/view?usp=sharing
cd /home
git clone https://gitee.com/YFwinston/yolov5-visible-and-full-person-crowdhuman.git
cd yolov5-visible-and-full-person-crowdhuman
pip install -r requirements.txt
pip install opencv-python-headless==4.1.2.30
mkdir -p /root/.config/Ultralytics
wget https://ultralytics.com/assets/Arial.ttf -O /root/.config/Ultralytics/Arial.ttf6.2 demo测试
检测头与身体
python ./detect.py --weights ./crowdhuman_vbody_yolov5m.pt --source ./1.jpeg --save-txt --save-conf检测visible body
Test (Only Visible Person Class)
python ./detect.py --weights ./crowdhuman_vbody_yolov5m.pt --source ./1.jpeg --save-txt --save-conf --classes 1检测头
Test (Only Heads)
python ./detect.py --weights ./crowdhuman_vbody_yolov5m.pt --source ./1.jpeg --save-txt --save-conf --classes 0yolov5 训练crowded human 【visible body detection】相关推荐
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