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依据这个进行训练#xff1a; https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/faster_rcnn
├── bac… 文章目录 资料环境尝试训练安全帽数据训练测试预测全部数据、代码、训练完的权重等资料见 资料
依据这个进行训练 https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/faster_rcnn
├── backbone: 特征提取网络可以根据自己的要求选择 ├── network_files: Faster R-CNN网络包括Fast R-CNN以及RPN等模块 ├── train_utils: 训练验证相关模块包括cocotools ├── my_dataset.py: 自定义dataset用于读取VOC数据集 ├── train_mobilenet.py: 以MobileNetV2做为backbone进行训练 ├── train_resnet50_fpn.py: 以resnet50FPN做为backbone进行训练 ├── train_multi_GPU.py: 针对使用多GPU的用户使用 ├── predict.py: 简易的预测脚本使用训练好的权重进行预测测试 ├── validation.py: 利用训练好的权重验证/测试数据的COCO指标并生成record_mAP.txt文件 └── pascal_voc_classes.json: pascal_voc标签文件 环境
dockerfile
FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04
ENV DEBIAN_FRONTENDnoninteractive# 安装基本软件包
RUN apt-get update \apt-get upgrade -y \apt-get -y --no-install-recommends install vim wget curl build-essential python3.10-dev python3.10 python3-pip sudo \update-alternatives --install /usr/bin/python python /usr/bin/python3.10 1 \apt-get install -y libgl1 libglib2.0-0 ffmpeg tzdata \ln -sf /usr/share/zoneinfo/Asia/Shanghai /etc/localtime \echo Asia/Shanghai /etc/timezoneRUN apt-get -y --no-install-recommends install vim wget curl git build-essential python3.10 python3-pip python3.10-venv sudoRUN apt-get install -y libgl1 libglib2.0-0 iputils-ping python3.10-dev libgoogle-perftools-dev nginx# 更改默认Shell为bash
SHELL [/bin/bash, -c]
python 环境
git clone https://github.com/WZMIAOMIAO/deep-learning-for-image-processing.git
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
conda create -n py38 python3.8 -y
conda activate py38
# CUDA 11.0
conda install pytorch1.7.1 torchvision0.8.2 torchaudio0.7.2 cudatoolkit11.0 -c pytorch -y
cd /deep-learning-for-image-processing/pytorch_object_detection/faster_rcnn
pip install -r requirements.txt
得到readme.md说的一些权重
cd /deep-learning-for-image-processing/pytorch_object_detection/faster_rcnn/backbonewget https://download.pytorch.org/models/mobilenet_v2-b0353104.pth
--2024-06-05 13:50:21-- https://download.pytorch.org/models/mobilenet_v2-b0353104.pthmv mobilenet_v2-b0353104.pth mobilenet_v2.pthwget https://download.pytorch.org/models/resnet50-0676ba61.pth
--2024-06-05 13:50:46-- https://download.pytorch.org/models/resnet50-0676ba61.pthmv resnet50-0676ba61.pth resnet50.pthwget https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pthmv fasterrcnn_resnet50_fpn_coco-258fb6c6.pth fasterrcnn_resnet50_fpn_coco.pthmkdir /deep-learning-for-image-processing/pytorch_object_detection/faster_rcnn/data
cd /deep-learning-for-image-processing/pytorch_object_detection/faster_rcnn/datawget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
尝试训练
反向commit 镜像
docker commit 74d9893ccb29 kevinchina/deeplearning:fasterrcnn_train_v1docker push kevinchina/deeplearning:fasterrcnn_train_v1重启容器
docker run --gpus all -it -v $PWD:/wkp --shm-size64g kevinchina/deeplearning:fasterrcnn_train_v1 bash训练
conda activate py38
cd /deep-learning-for-image-processing/pytorch_object_detection/faster_rcnn/
python train_mobilenetv2.py启动成功 一轮训练完成后的验证
安全帽数据训练
安全帽佩戴检测 数据集https://github.com/njvisionpower/Safety-Helmet-Wearing-Dataset
加入安全帽数据小小修改一下源代码的一些小的东西 启动训练
python train_mobilenetv2.py训练完一轮 训练结束
Test: Total time: 0:00:52 (0.0858 s / it)
Averaged stats: model_time: 0.0590 (0.0440) evaluator_time: 0.2436 (0.0344)
Accumulating evaluation results...
DONE (t1.42s).
IoU metric: bboxAverage Precision (AP) [ IoU0.50:0.95 | area all | maxDets100 ] 0.412Average Precision (AP) [ IoU0.50 | area all | maxDets100 ] 0.695Average Precision (AP) [ IoU0.75 | area all | maxDets100 ] 0.425Average Precision (AP) [ IoU0.50:0.95 | area small | maxDets100 ] 0.171Average Precision (AP) [ IoU0.50:0.95 | areamedium | maxDets100 ] 0.540Average Precision (AP) [ IoU0.50:0.95 | area large | maxDets100 ] 0.669Average Recall (AR) [ IoU0.50:0.95 | area all | maxDets 1 ] 0.169Average Recall (AR) [ IoU0.50:0.95 | area all | maxDets 10 ] 0.395Average Recall (AR) [ IoU0.50:0.95 | area all | maxDets100 ] 0.466Average Recall (AR) [ IoU0.50:0.95 | area small | maxDets100 ] 0.262Average Recall (AR) [ IoU0.50:0.95 | areamedium | maxDets100 ] 0.602Average Recall (AR) [ IoU0.50:0.95 | area large | maxDets100 ] 0.714
successful save loss curve!
successful save mAP curve!
测试
用test.txt中测试准确率
python validation.py预测
(py38) rootf3661bce90f6:/deep-learning-for-image-processing/pytorch_object_detection/faster_rcnn# python predict.py
using cuda:0 device.
inferenceNMS time: 0.018668174743652344docker push kevinchina/deeplearning:fasterrcnn_train_v2
全部数据、代码、训练完的权重等资料见
https://docs.qq.com/sheet/DUEdqZ2lmbmR6UVdU?tabBB08J2
