YOLOv5🚀 reproduction by Guo Quanhao using PaddlePaddle

Last update: Nov 14, 2022

Related tags

Deep Learning YOLOv5-Paddle

Overview

YOLOv5-Paddle

YOLOv5 🚀 reproduction by Guo Quanhao using PaddlePaddle

支持AutoBatch
支持AutoAnchor
支持GPU Memory

快速开始

使用AIStudio高性能环境快速构建YOLOv5训练(PaddlePaddle2.2.0-gpu version)

需要安装额外模块

pip install gputil==1.4.0
pip install pycocotools

COCO数据集

数据集已挂载至aistudio项目中，如果需要本地训练可以从这里下载数据集，和标签文件

Data
|-- coco
|   |-- annotions
|   |-- images
|      |-- train2017
|      |-- val2017
|      |-- test2017
|   |-- labels
|      |-- train2017
|      |-- val2017
|      |-- train2017.cache(初始解压可删除，训练时会自动生成)
|      |-- val2017.cache(初始解压可删除，训练时会自动生成)
|   |-- test-dev2017.txt
|   |-- val2017.txt
|   |-- train2017.txt
`   `-- validation

修改data/coco.yaml配置自己的coco路径，你可能需要修改path变量

path: /home/aistudio/Data/coco  # dataset root dir

训练

考虑到AIStudio对于github的访问速度，预先提供了Arial.ttf
AIStudio后端不支持绘图，部分可视乎在AIStudio仓库被注释

training scratch for coco

mkdir /home/aistudio/.config/QuanhaoGuo/
cp /home/aistudio/Arial.ttf /home/aistudio/.config/QuanhaoGuo/
cd YOLOv5-Paddle
python train.py --img 896 --batch 8 --epochs 300 --data ./data/coco.yaml --cfg yolov5s.yaml --weights ''

验证

python val.py --img 640  --data ./data/coco.yaml --weights ./weights/yolov5s.pdparams --cfg yolov5s.yaml

通过--task [val/test]控制验证集和测试集

所有提供的模型验证精度如下，本仓库的所有资源文件包括预训练模型均可在百度云盘下载code:dng9

Model	size ^(pixels)	mAP^val 0.5:0.95	mAP^val 0.5	params ^(M)	FLOPs ^{@640 (B)}	mAP^{test 0.5:0.95}	mAP^test 0.5
YOLOv5n	640	28.4	46.5	1.9	4.5	28.1	46.2
YOLOv5s	640	37.2	56.4	7.2	16.5	37.1	56.1
YOLOv5m	640	45.1	64.2	21.2	49.0	45.4	64.3
YOLOv5l	640	48.6	67.4	46.5	109.1	48.9	67.5
YOLOv5x	640	50.6	69.1	86.7	205.7	0.507	0.690

YOLOv5n6	1280	34.0	51.1	3.2	4.6	34.3	51.7
YOLOv5s6	1280	44.5	63.4	16.8	12.6	44.3	63.0
YOLOv5m6	1280	50.9	69.4	35.7	50.0	51.1	69.5
YOLOv5l6	1280	53.5	71.8	76.8	111.4	53.7	71.8
YOLOv5x6 + [TTA][TTA]	1280 1536	54.6 55.2	72.6 73.0	140.7 -	209.8 -	55.0 55.8	73.0 73.5

使用本地环境快速构建YOLOv5训练(PaddlePaddle2.2.0-gpu version)

git clone https://github.com/GuoQuanhao/YOLOv5-Paddle

然后按照使用AIStudio高性能环境快速构建YOLOv5训练执行

训练Custom Data

这里以一个类别的光栅数据集为例，数据集已上传至AIStudio

其组织结构如下：

Data
|-- guangshan
|   |-- images
|      |-- train
|      |-- val
|   |-- labels
|      |-- train
|      |-- val
|   |-- val.txt
|   |-- train.txt

另外你需要构建data/guangshan.yaml，相关文件已放入相关目录，主要用于指定数据集读取路径和模型配置。

# YOLOv5 reproduction 🚀 by GuoQuanhao

train: /home/aistudio/guangshan/images/train  # 118287 images
val: /home/aistudio/guangshan/images/val  # 5000 images
# number of classes
nc: 1
# class names
names: ['spectrum']

训练

python train.py --img 640 --batch 16 --epochs 100 --data ./data/guangshan.yaml --cfg yolov5s.yaml --weights ./weights/yolov5s.pdparams

Starting training for 100 epochs...

     Epoch   gpu_mem       box       obj       cls    labels  img_size
      0/99     4.19G    0.1039   0.04733         0        29       640: 100%|████████████████████████████████████████████████████████████████████| 9/9 [01:43<00:00, 11.50s/it]
               Class     Images     Labels          P          R     [email protected] [email protected]:.95: 100%|████████████████████████████████████████████████████| 1/1 [00:06<00:00,  6.64s/it]
                 all         16         29      0.266      0.379      0.226     0.0468

     Epoch   gpu_mem       box       obj       cls    labels  img_size
      1/99     4.41G   0.08177    0.0289         0        37       640: 100%|████████████████████████████████████████████████████████████████████| 9/9 [01:40<00:00, 11.20s/it]
               Class     Images     Labels          P          R     [email protected] [email protected]:.95: 100%|████████████████████████████████████████████████████| 1/1 [00:05<00:00,  5.49s/it]
                 all         16         29      0.462      0.445      0.398      0.109
......

完整的训练日志存在data/training.txt

利用VisualDL可视化训练过程

visualdl --logdir ./runs/train/exp

验证

python val.py --img 640  --data ./data/guangshan.yaml --cfg yolov5s.yaml --weights ./runs/train/exp/weights/best.pdparams

推理

python detect.py --weights ./runs/train/exp/weights/best.pdparams --cfg yolov5s.yaml --data ./data/guangshan.yaml --source ./data/images/guangshan.jpg

TODO

Multi-GPU Training ☘️
PaddleLite inference 🌟
Model to ONNX ⭐

关于作者

姓名	郭权浩
学校	电子科技大学研2020级
研究方向	计算机视觉
主页	Deep Hao的主页
github	Deep Hao的github
如有错误，请及时留言纠正，非常蟹蟹！
后续会有更多论文复现系列推出，欢迎大家有问题留言交流学习，共同进步成长！

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Comments

train相关问题

作者您好，我在使用您的数据集训练YOLOv5n模型时，出现一个警告： Epoch gpu_mem box obj cls labels img_size 1/99 1.94G 0.09787 0.5162 0 679 640: 100%|███████████████████████████████████████████████████████████████████████| 57/57 [04:48<00:00, 5.05s/it] Class Images Labels P R [email protected] [email protected]:.95: 25%|██████████████▎ | 1/4 [00:17<00:53, 17.96s/it]/mnt/YOLOv5-Paddle-main/utils/loss.py:191: RuntimeWarning: divide by zero encountered in true_divide j = np.maximum(r, 1 / r).max(2) < self.hyp['anchor_t'] # compare Class Images Labels P R [email protected] [email protected]:.95: 100%|█████████████████████████████████████████████████████████| 4/4 [00:50<00:00, 12.56s/it] all 50 7742 0.571 0.664 0.579 0.188

训练集为450，验证集为50，训练的目标只有一类“钢筋” （label: rebar）想问一下会影响后续的部署使用吗？

opened by qiujianchen 0
resume掉精度问题该怎么解决呢？

您好，我在使用您的代码时发现--resume时精度会下降，并且可能需要训练一些epoch才能恢复到之前的精度，请问该如何做才能做到resume时不掉精度呢（因为AIstudio的GPU每天只有8点算力卡，我所使用的数据集训练一个epoch需要一个小时，只能通过resume来完成整个训练过程）

图中的第一个39-45是直接resume的结果

图中的第二个39-48是我认为之前训练时x['learning_rate']和x['momentum']存在，所以尝试在训练开始前给它们赋上warmup结束时的值，但发现效果并未达到预期 for j, x in enumerate(optimizer._param_groups): x['learning_rate'] = np.interp(nw, [0, nw], [hyp['warmup_bias_lr'] if j == 2 else 0.0, scheduler.base_lr * lf(epoch)]) if 'momentum' in x: x['momentum'] = np.interp(nw, [0, nw], [hyp['warmup_momentum'], hyp['momentum']])

希望能得到您的帮助，谢谢！

opened by diaoa1900 0
ai studio绘图问题

对比了yolov5的源码，绘图部分应该不是ai studio不支持的原因，而是在metrics.py的plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=())函数中，for i, y in enumerate(py.t())编写错误，修改为for i, y in enumerate(py.T)后可以正确绘制PR图

opened by misaka-network10032 0

YOLOv5🚀 reproduction by Guo Quanhao using PaddlePaddle

Related tags

Overview

YOLOv5-Paddle

快速开始

COCO数据集

训练

验证

训练Custom Data

训练

验证

推理

TODO

关于作者

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Comments

train相关问题

resume掉精度问题该怎么解决呢？

ai studio绘图问题

Releases(v1.0)

v1.0(Dec 7, 2021)

Owner

QuanHao Guo

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