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从零开始的目标检测和关键点检测（一）：用labelme标注数据集

从零开始的目标检测和关键点检测（三）：训练一个Glue的RTMPose模型

在[1]用labelme标注自己的数据集中已经标注好数据集(关键点和检测框)，通过labelme2coco脚本将所有的labelme json文件集成为两个coco格式的json文件，即train_coco.json和val_coco.json。训练一个RTMDet模型，需要重写config文件。

一、config文件解读

1、数据集类型即coco格式的数据集，metainfo是指框的类别，因为这里只有一个glue的类，因此NUM_CLASSES为1，注意metainfo类别名后的逗号，

# 数据集类型及路径
dataset_type = 'CocoDataset'
data_root = 'data/glue_134_Keypoint/'
metainfo = {'classes': ('glue',)}
NUM_CLASSES = len(metainfo['classes'])

2、加载backnbone预训练权重和RTMDet-tiny预训练权重

# RTMDet-tiny
load_from = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_tiny_8xb32-300e_coco/rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth'
backbone_pretrain = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth'
deepen_factor = 0.167
widen_factor = 0.375
in_channels = [96, 192, 384]
neck_out_channels = 96
num_csp_blocks = 1
exp_on_reg = False

3、训练参数设置，如epoch、batchsize…

MAX_EPOCHS = 200
TRAIN_BATCH_SIZE = 8
VAL_BATCH_SIZE = 4
stage2_num_epochs = 20
base_lr = 0.004
VAL_INTERVAL = 5  # 每隔多少轮评估保存一次模型权重

4、default_runtime，即默认设置，在config文件夹的default_runtime.py可看到。不同的MM-框架的默认设置不一样(如default_scope = 'mmdet')，可以包含这个.py也可以直接复制过来。

default_scope = 'mmdet'
default_hooks = dict(timer=dict(type='IterTimerHook'),logger=dict(type='LoggerHook', interval=1),param_scheduler=dict(type='ParamSchedulerHook'),checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=2, save_best='coco/bbox_mAP'),# auto coco/bbox_mAP_50 coco/bbox_mAP_75 coco/bbox_mAP_ssampler_seed=dict(type='DistSamplerSeedHook'),visualization=dict(type='DetVisualizationHook'))
env_cfg = dict(cudnn_benchmark=False,mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(type='DetLocalVisualizer',vis_backends=[dict(type='LocalVisBackend')],name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = None
resume = False

5、训练超参数配置

train_cfg = dict(type='EpochBasedTrainLoop',max_epochs=MAX_EPOCHS,val_interval=VAL_INTERVAL,dynamic_intervals=[(MAX_EPOCHS - stage2_num_epochs, 1)])val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')# 学习率
param_scheduler = [dict(type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0,end=1000),dict(type='CosineAnnealingLR',eta_min=0.0002,begin=150,end=300,T_max=150,by_epoch=True,convert_to_iter_based=True)
]# 优化器
optim_wrapper = dict(type='OptimWrapper',optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),paramwise_cfg=dict(norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
auto_scale_lr = dict(enable=False, base_batch_size=16)

6、数据处理pipeline，做数据预处理(数据增强)

# DataLoader
backend_args = None
train_pipeline = [dict(type='LoadImageFromFile', backend_args=None),dict(type='LoadAnnotations', with_bbox=True),dict(type='CachedMosaic',img_scale=(640, 640),pad_val=114.0,max_cached_images=20,random_pop=False),dict(type='RandomResize',scale=(1280, 1280),ratio_range=(0.5, 2.0),keep_ratio=True),dict(type='RandomCrop', crop_size=(640, 640)),dict(type='YOLOXHSVRandomAug'),dict(type='RandomFlip', prob=0.5),dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),dict(type='CachedMixUp',img_scale=(640, 640),ratio_range=(1.0, 1.0),max_cached_images=10,random_pop=False,pad_val=(114, 114, 114),prob=0.5),dict(type='PackDetInputs')
]
test_pipeline = [dict(type='LoadImageFromFile', backend_args=None),dict(type='Resize', scale=(640, 640), keep_ratio=True),dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),dict(type='PackDetInputs',meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape','scale_factor'))
]

7、加载数据和标注并用对应pipeliane做预处理

train_dataloader = dict(batch_size=TRAIN_BATCH_SIZE,num_workers=4,persistent_workers=True,sampler=dict(type='DefaultSampler', shuffle=True),batch_sampler=None,dataset=dict(type='CocoDataset',data_root=data_root,metainfo=metainfo,ann_file='train_coco.json',data_prefix=dict(img='images/'),filter_cfg=dict(filter_empty_gt=True, min_size=32),pipeline=train_pipeline,backend_args=None),pin_memory=True)
val_dataloader = dict(batch_size=VAL_BATCH_SIZE,num_workers=2,persistent_workers=True,drop_last=False,sampler=dict(type='DefaultSampler', shuffle=False),dataset=dict(type='CocoDataset',data_root=data_root,metainfo=metainfo,ann_file='val_coco.json',data_prefix=dict(img='images/'),test_mode=True,pipeline=test_pipeline,backend_args=None))
test_dataloader = val_dataloader

8、定义模型结构backbone + neck + head

# 模型结构
model = dict(type='RTMDet',data_preprocessor=dict(type='DetDataPreprocessor',mean=[103.53, 116.28, 123.675],std=[57.375, 57.12, 58.395],bgr_to_rgb=False,batch_augments=None),backbone=dict(type='CSPNeXt',arch='P5',expand_ratio=0.5,deepen_factor=deepen_factor,widen_factor=widen_factor,channel_attention=True,norm_cfg=dict(type='SyncBN'),act_cfg=dict(type='SiLU', inplace=True),init_cfg=dict(type='Pretrained',prefix='backbone.',checkpoint=backbone_pretrain)),neck=dict(type='CSPNeXtPAFPN',in_channels=in_channels,out_channels=neck_out_channels,num_csp_blocks=num_csp_blocks,expand_ratio=0.5,norm_cfg=dict(type='SyncBN'),act_cfg=dict(type='SiLU', inplace=True)),bbox_head=dict(type='RTMDetSepBNHead',num_classes=NUM_CLASSES,in_channels=neck_out_channels,stacked_convs=2,feat_channels=neck_out_channels,anchor_generator=dict(type='MlvlPointGenerator', offset=0, strides=[8, 16, 32]),bbox_coder=dict(type='DistancePointBBoxCoder'),loss_cls=dict(type='QualityFocalLoss',use_sigmoid=True,beta=2.0,loss_weight=1.0),loss_bbox=dict(type='GIoULoss', loss_weight=2.0),with_objectness=False,exp_on_reg=exp_on_reg,share_conv=True,pred_kernel_size=1,norm_cfg=dict(type='SyncBN'),act_cfg=dict(type='SiLU', inplace=True)),train_cfg=dict(assigner=dict(type='DynamicSoftLabelAssigner', topk=13),allowed_border=-1,pos_weight=-1,debug=False),test_cfg=dict(nms_pre=30000,min_bbox_size=0,score_thr=0.001,nms=dict(type='nms', iou_threshold=0.65),max_per_img=300))

二、开始训练

1、开始训练

python tools/train.py data/glue_134_Keypoint/rtmdet_tiny_glue.py

训练结果

 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.719Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.483Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.766Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.766Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.766

2、测试一下训练结果

python demo/image_demo.py data/glue_134_Keypoint/test_image/test.png data/glue_134_Keypoint/rtmdet_tiny_glue.py --weights work_dirs/rtmdet_tiny_glue/best_coco_bbox_mAP_epoch_180.pth --device cpu

3、可视化训练过程

4、由于标注数据集的glue都是小目标的，因此大目标无法识别，如下：

三、数据集分析

1、可视化部分图像

框标注-框中心点位置分布

框标注-框宽高分布

显然都是小目标的检测

四、ncnn部署

在线模型转换：Deploee

上传文件完成在线转换