mmdetection训练（1）voc格式的数据集（自制）
- 提前准备
- 一、voc数据集
- 二、修改配置代码进行训练（敲黑板！！！！！）
- 1.数据集相关内容修改
- 2.自定义配置文件构建
- 三、训练及其评估测试
- 总结
提前准备
voc数据集，mmdetection代码库
一、voc数据集
需要有以下三个文件夹的格式数据
（这里不需要完全按照vocdevkit/voc2007这种进行构建，下面教大家如何修改）
存放xml标签文件
放的train.txt,test.txt,和val.txt
存放原始的图片数据
二、修改配置代码进行训练（敲黑板！！！！！）
强调：这里包括以下的内容，均在自己创建模板文件下进行修改，原则上不对原始代码进行修改，修改原始代码繁琐且容易搞混破坏代码整体结构，以下均为自己创建配置文件，请自己按需改写（个人喜欢这样的配置方式）。
1.数据集相关内容修改
（1）configs/base/datasets/voc0712.py中
修改相关的路径与voc数据集保持一致(这个文件夹中只要修改路径不要修改别的东西)
# dataset settings
dataset_type = 'VOCDataset'
# data_root = 'data/VOCdevkit/'
data_root = '/home/ubuntu/data/Official-SSDD-OPEN/BBox_SSDD/'# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically Infer from prefix (not support LMDB and Memcache yet)# data_root = 's3://openmmlab/datasets/detection/segmentation/VOCdevkit/'# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/segmentation/',
# 'data/': 's3://openmmlab/datasets/segmentation/'
# }))
backend_args = None###数据增强的方法
train_pipeline = [dict(type='LoadImageFromFile', backend_args=backend_args),dict(type='LoadAnnotations', with_bbox=True),dict(type='Resize', scale=(1000, 600), keep_ratio=True),dict(type='RandomFlip', prob=0.5),dict(type='PackDetInputs')
]
test_pipeline = [dict(type='LoadImageFromFile', backend_args=backend_args),dict(type='Resize', scale=(1000, 600), keep_ratio=True),# avoid bboxes being resizeddict(type='LoadAnnotations', with_bbox=True),dict(type='PackDetInputs',meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape','scale_factor'))
]###数据加载
train_dataloader = dict(batch_size=2,num_workers=2,persistent_workers=True,sampler=dict(type='DefaultSampler', shuffle=True),batch_sampler=dict(type='AspectRatioBatchSampler'),dataset=dict(type='RepeatDataset',times=3,dataset=dict(type='ConcatDataset',# VOCDataset will add different `dataset_type` in dataset.metainfo,# which will get error if using ConcatDataset. Adding# `ignore_keys` can avoid this error.ignore_keys=['dataset_type'],datasets=[dict(type=dataset_type,data_root=data_root,# ann_file='VOC2007/ImageSets/Main/trainval.txt',ann_file='voc_style/ImageSets/Main/train.txt',data_prefix=dict(sub_data_root='voc_style/'),filter_cfg=dict(filter_empty_gt=True, min_size=32, bbox_min_size=32),pipeline=train_pipeline,backend_args=backend_args),# dict(# type=dataset_type,# data_root=data_root,# ann_file='VOC2012/ImageSets/Main/trainval.txt',# data_prefix=dict(sub_data_root='VOC2012/'),# filter_cfg=dict(# filter_empty_gt=True, min_size=32, bbox_min_size=32),# pipeline=train_pipeline,# backend_args=backend_args)])))val_dataloader = dict(batch_size=1,num_workers=2,persistent_workers=True,drop_last=False,sampler=dict(type='DefaultSampler', shuffle=False),dataset=dict(type=dataset_type,data_root=data_root,ann_file='voc_style/ImageSets/Main/test.txt',data_prefix=dict(sub_data_root='voc_style/'),test_mode=True,pipeline=test_pipeline,backend_args=backend_args))
test_dataloader = val_dataloader# Pascal VOC2007 uses `11points` as default evaluate mode, while PASCAL
# VOC2012 defaults to use 'area'.
val_evaluator = dict(type='VOCMetric', metric='mAP', eval_mode='11points')
test_evaluator = val_evaluator
（2）修改 mmdet/datasets/voc.py文件
修改自己数据集的类别信息与框的颜色，并且一定注释取消voc2007和2012版本判断的要求，方便后面使用自己的数据集路径。
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import DATASETS
from .xml_style import XMLDataset@DATASETS.register_module()
class VOCDataset(XMLDataset):"""Dataset for PASCAL VOC."""# 标准的voc格式类别信息# METAINFO = {# 'classes':# ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',# 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person',# 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'),# # palette is a list of color tuples, which is used for visualization.# 'palette': [(106, 0, 228), (119, 11, 32), (165, 42, 42), (0, 0, 192),# (197, 226, 255), (0, 60, 100), (0, 0, 142), (255, 77, 255),# (153, 69, 1), (120, 166, 157), (0, 182, 199),# (0, 226, 252), (182, 182, 255), (0, 0, 230), (220, 20, 60),# (163, 255, 0), (0, 82, 0), (3, 95, 161), (0, 80, 100),# (183, 130, 88)]# }### 修改的数据类别信息METAINFO = {'classes':('ship', ),# palette is a list of color tuples, which is used for visualization.'palette': [(106, 0, 228)]}def __init__(self, **kwargs):super().__init__(**kwargs)# if 'VOC2007' in self.sub_data_root:# self._metainfo['dataset_type'] = 'VOC2007'# elif 'VOC2012' in self.sub_data_root:# self._metainfo['dataset_type'] = 'VOC2012'# else:# self._metainfo['dataset_type'] = None（3）修改网络配置文件中的输出类别（非必须操作）
configs/base/models/faster-rcnn_r50_fpn.py
2.自定义配置文件构建
（1）在代码根目录新建myconfig.py的文件，
（2）复制以下内容到其中：
新的配置文件主要是分为三个部分
1、倒入相应的库文件（base）
2、模型加载文件：一定要家在修改num_classses=‘你的类别’
3、数据集配置：直接复制configs/base/datasets/voc0712.py即可
# 新配置继承了基本配置，并做了必要的修改
# _base_ = './configs/faster_rcnn/mask-rcnn_r50-caffe_fpn_ms-poly-1x_coco.py'
_base_ = './configs/faster_rcnn/faster-rcnn_r50_fpn_1x_voc.py'
# 我们还需要更改 head 中的 num_classes 以匹配数据集中的类别数
########------模型相关配置--------#########
model = dict(roi_head=dict(bbox_head=dict(num_classes=1)))########------修改数据集相关配置--------#########
backend_args = None# dataset settingsdataset_type = 'VOCDataset'
# data_root = 'data/VOCdevkit/'
data_root = '/home/ubuntu/data/Official-SSDD-OPEN/BBox_SSDD/'###数据增强的方法
train_pipeline = [dict(type='LoadImageFromFile', backend_args=backend_args),dict(type='LoadAnnotations', with_bbox=True),dict(type='Resize', scale=(1000, 600), keep_ratio=True),dict(type='RandomFlip', prob=0.5),dict(type='PackDetInputs')
]
test_pipeline = [dict(type='LoadImageFromFile', backend_args=backend_args),dict(type='Resize', scale=(1000, 600), keep_ratio=True),# avoid bboxes being resizeddict(type='LoadAnnotations', with_bbox=True),dict(type='PackDetInputs',meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape','scale_factor'))
]###数据加载
train_dataloader = dict(batch_size=2,num_workers=2,persistent_workers=True,sampler=dict(type='DefaultSampler', shuffle=True),batch_sampler=dict(type='AspectRatioBatchSampler'),dataset=dict(type='RepeatDataset',times=3,dataset=dict(type='ConcatDataset',# VOCDataset will add different `dataset_type` in dataset.metainfo,# which will get error if using ConcatDataset. Adding# `ignore_keys` can avoid this error.ignore_keys=['dataset_type'],datasets=[dict(type=dataset_type,data_root=data_root,# ann_file='VOC2007/ImageSets/Main/trainval.txt',ann_file='voc_style/ImageSets/Main/train.txt',data_prefix=dict(sub_data_root='voc_style/'),filter_cfg=dict(filter_empty_gt=True, min_size=32, bbox_min_size=32),pipeline=train_pipeline,backend_args=backend_args),# dict(# type=dataset_type,# data_root=data_root,# ann_file='VOC2012/ImageSets/Main/trainval.txt',# data_prefix=dict(sub_data_root='VOC2012/'),# filter_cfg=dict(# filter_empty_gt=True, min_size=32, bbox_min_size=32),# pipeline=train_pipeline,# backend_args=backend_args)])))val_dataloader = dict(batch_size=1,num_workers=2,persistent_workers=True,drop_last=False,sampler=dict(type='DefaultSampler', shuffle=False),dataset=dict(type=dataset_type,data_root=data_root,ann_file='voc_style/ImageSets/Main/test.txt',data_prefix=dict(sub_data_root='voc_style/'),test_mode=True,pipeline=test_pipeline,backend_args=backend_args))
test_dataloader = val_dataloader
###数据加载
train_dataloader = dict(batch_size=2,num_workers=2,persistent_workers=True,sampler=dict(type='DefaultSampler', shuffle=True),batch_sampler=dict(type='AspectRatioBatchSampler'),dataset=dict(type='RepeatDataset',times=3,dataset=dict(type='ConcatDataset',# VOCDataset will add different `dataset_type` in dataset.metainfo,# which will get error if using ConcatDataset. Adding# `ignore_keys` can avoid this error.ignore_keys=['dataset_type'],datasets=[dict(type=dataset_type,data_root=data_root,# ann_file='VOC2007/ImageSets/Main/trainval.txt',ann_file='voc_style/ImageSets/Main/train.txt',data_prefix=dict(sub_data_root='voc_style/'),filter_cfg=dict(filter_empty_gt=True, min_size=32, bbox_min_size=32),pipeline=train_pipeline,backend_args=backend_args),# dict(# type=dataset_type,# data_root=data_root,# ann_file='VOC2012/ImageSets/Main/trainval.txt',# data_prefix=dict(sub_data_root='VOC2012/'),# filter_cfg=dict(# filter_empty_gt=True, min_size=32, bbox_min_size=32),# pipeline=train_pipeline,# backend_args=backend_args)])))val_dataloader = dict(batch_size=1,num_workers=2,persistent_workers=True,drop_last=False,sampler=dict(type='DefaultSampler', shuffle=False),dataset=dict(type=dataset_type,data_root=data_root,ann_file='voc_style/ImageSets/Main/test.txt',data_prefix=dict(sub_data_root='voc_style/'),test_mode=True,pipeline=test_pipeline,backend_args=backend_args))
test_dataloader = val_dataloader# 使用预训练的 Mask R-CNN 模型权重来做初始化，可以提高模型性能
# load_from = 'https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'
三、训练及其评估测试
模型训练的命令：
python tools/train.py myconfig_voc.py```
模型测试的命令```bash
在这里插入代码片
总结
训练遇到的问题
记录下遇到的问题。训练SSDD数据集的时候，发现coco格式训练正常，但voc格式训练出现map值很低，一直升不上去。试了下2.x版本的mmdetection训练voc格式没问题，解决方案是在configs/base/datasets/voc0712.py中删掉bbox_min_size=32即可，原文链接：https://blog.csdn.net/Pliter/article/details/134389961
