今天，学习RTMPose关键点检测实战。教大家如何安装安装MMDetection和MMPose。
实战项目以三角板关键点检测场景为例，结合OpenMMLab开源目标检测算法库MMDetection、开源关键点检测算法库MMPose、开源模型部署算法库MMDeploy，全面讲解项目全流程：

数据集：Labelme标注数据集、整理标注格式至MS COCO

目标检测：分别训练Faster R CNN和RTMDet-Tiny目标检测模型、训练日志可视化、测试集评估、对图像、摄像头画面预测

关键点检测：训练RTMPose-S关键点检测模型、训练日志可视化、测试集上评估、分别对“图像、视频、摄像头画面”预测

模型终端部署：转ONNX格式，终端推理

一、引言

耳朵穴位是中医针灸的重要组成部分，准确定位耳朵穴位对于针灸治疗和健康监测具有重要意义。传统的耳朵穴位检测方法依赖于人工标记，效率低下且容易受到人为因素的影响。随着深度学习和计算机视觉技术的发展，基于图像的自动化穴位检测方法逐渐成为研究热点。

RTMPose是一种基于深度学习的姿态估计模型，具有较高的精度和鲁棒性。本文提出了一种结合RTMPose和检测技术的耳朵穴位关键点检测方法，通过引入先进的图像处理算法，实现对耳朵穴位的自动化检测和定位。

项目最终完成展示图

项目源码网盘自取：链接：https://pan.baidu.com/s/1QFFr4UBwaYePyIOFkgh5mw?pwd=4211 
提取码：4211

二、首先要配置好所需环境

首先要配置好所需环境

首先要配置好所需环境

重要的事情说三遍！！！！

环境配置参考之前教学，传送门：https://blog.csdn.net/m0_74194018/article/details/140844116

打开项目目录结构如下图所示：（checkpoint权重文件我已经帮大家训练好了训练了7749个小时）

三、config配置文件我也已经准备好了不需要自己手动更改。

mmdetection_cfg

# runtime settings
default_scope = 'mmdet'

default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=50),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=1),
    sampler_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=vis_backends, name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)

log_level = 'INFO'
load_from = None
resume = False

# model settings
model = dict(
    type='CascadeRCNN',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        bgr_to_rgb=True,
        pad_mask=True,
        pad_size_divisor=32),
    backbone=dict(
        type='ResNeXt',
        depth=101,
        groups=64,
        base_width=4,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        style='pytorch',
        init_cfg=dict(
            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
    roi_head=dict(
        type='CascadeRoIHead',
        num_stages=3,
        stage_loss_weights=[1, 0.5, 0.25],
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=[
            dict(
                type='Shared2FCBBoxHead',
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=1,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.1, 0.1, 0.2, 0.2]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                               loss_weight=1.0)),
            dict(
                type='Shared2FCBBoxHead',
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=1,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.05, 0.05, 0.1, 0.1]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                               loss_weight=1.0)),
            dict(
                type='Shared2FCBBoxHead',
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=1,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.033, 0.033, 0.067, 0.067]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
        ]),
    # model training and testing settings
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=0,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=2000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=[
            dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.5,
                    neg_iou_thr=0.5,
                    min_pos_iou=0.5,
                    match_low_quality=False,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=512,
                    pos_fraction=0.25,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=True),
                pos_weight=-1,
                debug=False),
            dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.6,
                    neg_iou_thr=0.6,
                    min_pos_iou=0.6,
                    match_low_quality=False,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=512,
                    pos_fraction=0.25,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=True),
                pos_weight=-1,
                debug=False),
            dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.7,
                    neg_iou_thr=0.7,
                    min_pos_iou=0.7,
                    match_low_quality=False,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=512,
                    pos_fraction=0.25,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=True),
                pos_weight=-1,
                debug=False)
        ]),
    test_cfg=dict(
        rpn=dict(
            nms_pre=1000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100)))

# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
    # If you don't have a gt annotation, delete the pipeline
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]
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=dataset_type,
        data_root=data_root,
        ann_file='annotations/instances_train2017.json',
        data_prefix=dict(img='train2017/'),
        filter_cfg=dict(filter_empty_gt=True, min_size=32),
        pipeline=train_pipeline))
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='annotations/instances_val2017.json',
        data_prefix=dict(img='val2017/'),
        test_mode=True,
        pipeline=test_pipeline))
test_dataloader = val_dataloader

val_evaluator = dict(
    type='CocoMetric',
    ann_file=data_root + 'annotations/instances_val2017.json',
    metric='bbox',
    format_only=False)
test_evaluator = val_evaluator

mmtracking_cfg

model = dict(
    detector=dict(
        type='FasterRCNN',
        backbone=dict(
            type='ResNet',
            depth=50,
            num_stages=4,
            out_indices=(0, 1, 2, 3),
            frozen_stages=1,
            norm_cfg=dict(type='BN', requires_grad=True),
            norm_eval=True,
            style='pytorch',
            init_cfg=dict(
                type='Pretrained', checkpoint='torchvision://resnet50')),
        neck=dict(
            type='FPN',
            in_channels=[256, 512, 1024, 2048],
            out_channels=256,
            num_outs=5),
        rpn_head=dict(
            type='RPNHead',
            in_channels=256,
            feat_channels=256,
            anchor_generator=dict(
                type='AnchorGenerator',
                scales=[8],
                ratios=[0.5, 1.0, 2.0],
                strides=[4, 8, 16, 32, 64]),
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0.0, 0.0, 0.0, 0.0],
                target_stds=[1.0, 1.0, 1.0, 1.0],
                clip_border=False),
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
            loss_bbox=dict(
                type='SmoothL1Loss', beta=0.1111111111111111,
                loss_weight=1.0)),
        roi_head=dict(
            type='StandardRoIHead',
            bbox_roi_extractor=dict(
                type='SingleRoIExtractor',
                roi_layer=dict(
                    type='RoIAlign', output_size=7, sampling_ratio=0),
                out_channels=256,
                featmap_strides=[4, 8, 16, 32]),
            bbox_head=dict(
                type='Shared2FCBBoxHead',
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=1,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0.0, 0.0, 0.0, 0.0],
                    target_stds=[0.1, 0.1, 0.2, 0.2],
                    clip_border=False),
                reg_class_agnostic=False,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', loss_weight=1.0))),
        train_cfg=dict(
            rpn=dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.7,
                    neg_iou_thr=0.3,
                    min_pos_iou=0.3,
                    match_low_quality=True,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=256,
                    pos_fraction=0.5,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=False),
                allowed_border=-1,
                pos_weight=-1,
                debug=False),
            rpn_proposal=dict(
                nms_pre=2000,
                max_per_img=1000,
                nms=dict(type='nms', iou_threshold=0.7),
                min_bbox_size=0),
            rcnn=dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.5,
                    neg_iou_thr=0.5,
                    min_pos_iou=0.5,
                    match_low_quality=False,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=512,
                    pos_fraction=0.25,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=True),
                pos_weight=-1,
                debug=False)),
        test_cfg=dict(
            rpn=dict(
                nms_pre=1000,
                max_per_img=1000,
                nms=dict(type='nms', iou_threshold=0.7),
                min_bbox_size=0),
            rcnn=dict(
                score_thr=0.05,
                nms=dict(type='nms', iou_threshold=0.5),
                max_per_img=100)),
        init_cfg=dict(
            type='Pretrained',
            checkpoint='https://download.openmmlab.com/mmtracking/'
            'mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth')),
    type='DeepSORT',
    motion=dict(type='KalmanFilter', center_only=False),
    reid=dict(
        type='BaseReID',
        backbone=dict(
            type='ResNet',
            depth=50,
            num_stages=4,
            out_indices=(3, ),
            style='pytorch'),
        neck=dict(type='GlobalAveragePooling', kernel_size=(8, 4), stride=1),
        head=dict(
            type='LinearReIDHead',
            num_fcs=1,
            in_channels=2048,
            fc_channels=1024,
            out_channels=128,
            num_classes=380,
            loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
            loss_pairwise=dict(
                type='TripletLoss', margin=0.3, loss_weight=1.0),
            norm_cfg=dict(type='BN1d'),
            act_cfg=dict(type='ReLU')),
        init_cfg=dict(
            type='Pretrained',
            checkpoint='https://download.openmmlab.com/mmtracking/'
            'mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth')),
    tracker=dict(
        type='SortTracker',
        obj_score_thr=0.5,
        reid=dict(
            num_samples=10,
            img_scale=(256, 128),
            img_norm_cfg=None,
            match_score_thr=2.0),
        match_iou_thr=0.5,
        momentums=None,
        num_tentatives=2,
        num_frames_retain=100))
dataset_type = 'MOTChallengeDataset'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadMultiImagesFromFile', to_float32=True),
    dict(type='SeqLoadAnnotations', with_bbox=True, with_track=True),
    dict(
        type='SeqResize',
        img_scale=(1088, 1088),
        share_params=True,
        ratio_range=(0.8, 1.2),
        keep_ratio=True,
        bbox_clip_border=False),
    dict(type='SeqPhotoMetricDistortion', share_params=True),
    dict(
        type='SeqRandomCrop',
        share_params=False,
        crop_size=(1088, 1088),
        bbox_clip_border=False),
    dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),
    dict(
        type='SeqNormalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='SeqPad', size_divisor=32),
    dict(type='MatchInstances', skip_nomatch=True),
    dict(
        type='VideoCollect',
        keys=[
            'img', 'gt_bboxes', 'gt_labels', 'gt_match_indices',
            'gt_instance_ids'
        ]),
    dict(type='SeqDefaultFormatBundle', ref_prefix='ref')
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1088, 1088),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='VideoCollect', keys=['img'])
        ])
]
data_root = 'data/MOT17/'
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type='MOTChallengeDataset',
        visibility_thr=-1,
        ann_file='data/MOT17/annotations/half-train_cocoformat.json',
        img_prefix='data/MOT17/train',
        ref_img_sampler=dict(
            num_ref_imgs=1,
            frame_range=10,
            filter_key_img=True,
            method='uniform'),
        pipeline=[
            dict(type='LoadMultiImagesFromFile', to_float32=True),
            dict(type='SeqLoadAnnotations', with_bbox=True, with_track=True),
            dict(
                type='SeqResize',
                img_scale=(1088, 1088),
                share_params=True,
                ratio_range=(0.8, 1.2),
                keep_ratio=True,
                bbox_clip_border=False),
            dict(type='SeqPhotoMetricDistortion', share_params=True),
            dict(
                type='SeqRandomCrop',
                share_params=False,
                crop_size=(1088, 1088),
                bbox_clip_border=False),
            dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),
            dict(
                type='SeqNormalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='SeqPad', size_divisor=32),
            dict(type='MatchInstances', skip_nomatch=True),
            dict(
                type='VideoCollect',
                keys=[
                    'img', 'gt_bboxes', 'gt_labels', 'gt_match_indices',
                    'gt_instance_ids'
                ]),
            dict(type='SeqDefaultFormatBundle', ref_prefix='ref')
        ]),
    val=dict(
        type='MOTChallengeDataset',
        ann_file='data/MOT17/annotations/half-val_cocoformat.json',
        img_prefix='data/MOT17/train',
        ref_img_sampler=None,
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1088, 1088),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='VideoCollect', keys=['img'])
                ])
        ]),
    test=dict(
        type='MOTChallengeDataset',
        ann_file='data/MOT17/annotations/half-val_cocoformat.json',
        img_prefix='data/MOT17/train',
        ref_img_sampler=None,
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1088, 1088),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='VideoCollect', keys=['img'])
                ])
        ]))
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=100,
    warmup_ratio=0.01,
    step=[3])
total_epochs = 4
evaluation = dict(metric=['bbox', 'track'], interval=1)
search_metrics = ['MOTA', 'IDF1', 'FN', 'FP', 'IDs', 'MT', 'ML']

当然这里也可以用yolo来进行detection目标检测Yolo_cfg

Yolo的具体详细介绍可以参考我之前写的博客介绍，传送门：https://blog.csdn.net/m0_74194018/article/details/140747393

train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=300, val_interval=10)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
    dict(
        type='mmdet.QuadraticWarmupLR',
        by_epoch=True,
        begin=0,
        end=5,
        convert_to_iter_based=True),
    dict(
        type='CosineAnnealingLR',
        eta_min=0.0005,
        begin=5,
        T_max=285,
        end=285,
        by_epoch=True,
        convert_to_iter_based=True),
    dict(type='ConstantLR', by_epoch=True, factor=1, begin=285, end=300)
]
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(
        type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005, nesterov=True),
    paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0))
auto_scale_lr = dict(enable=False, base_batch_size=64)
default_scope = 'mmdet'
default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=50),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3),
    sampler_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 = 'https://download.openmmlab.com/mmdetection/' \
            'v2.0/yolox/yolox_s_8x8_300e_coco/' \
            'yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth'
resume = False
img_scale = (640, 640)
model = dict(
    type='YOLOX',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        pad_size_divisor=32,
        batch_augments=[
            dict(
                type='BatchSyncRandomResize',
                random_size_range=(480, 800),
                size_divisor=32,
                interval=10)
        ]),
    backbone=dict(
        type='CSPDarknet',
        deepen_factor=0.33,
        widen_factor=0.5,
        out_indices=(2, 3, 4),
        use_depthwise=False,
        spp_kernal_sizes=(5, 9, 13),
        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
        act_cfg=dict(type='Swish')),
    neck=dict(
        type='YOLOXPAFPN',
        in_channels=[128, 256, 512],
        out_channels=128,
        num_csp_blocks=1,
        use_depthwise=False,
        upsample_cfg=dict(scale_factor=2, mode='nearest'),
        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
        act_cfg=dict(type='Swish')),
    bbox_head=dict(
        type='YOLOXHead',
        num_classes=1,
        in_channels=128,
        feat_channels=128,
        stacked_convs=2,
        strides=(8, 16, 32),
        use_depthwise=False,
        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
        act_cfg=dict(type='Swish'),
        loss_cls=dict(
            type='CrossEntropyLoss',
            use_sigmoid=True,
            reduction='sum',
            loss_weight=1.0),
        loss_bbox=dict(
            type='IoULoss',
            mode='square',
            eps=1e-16,
            reduction='sum',
            loss_weight=5.0),
        loss_obj=dict(
            type='CrossEntropyLoss',
            use_sigmoid=True,
            reduction='sum',
            loss_weight=1.0),
        loss_l1=dict(type='L1Loss', reduction='sum', loss_weight=1.0)),
    train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
    test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
data_root = 'data/coco/'
dataset_type = 'CocoDataset'
backend_args = dict(backend='local')
train_pipeline = [
    dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),
    dict(
        type='RandomAffine', scaling_ratio_range=(0.1, 2),
        border=(-320, -320)),
    dict(
        type='MixUp',
        img_scale=(640, 640),
        ratio_range=(0.8, 1.6),
        pad_val=114.0),
    dict(type='YOLOXHSVRandomAug'),
    dict(type='RandomFlip', prob=0.5),
    dict(type='Resize', scale=(640, 640), keep_ratio=True),
    dict(
        type='Pad',
        pad_to_square=True,
        pad_val=dict(img=(114.0, 114.0, 114.0))),
    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
    dict(type='PackDetInputs')
]
train_dataset = dict(
    type='MultiImageMixDataset',
    dataset=dict(
        type='CocoDataset',
        data_root='data/coco/',
        ann_file='annotations/instances_train2017.json',
        data_prefix=dict(img='train2017/'),
        pipeline=[
            dict(type='LoadImageFromFile', backend_args=dict(backend='local')),
            dict(type='LoadAnnotations', with_bbox=True)
        ],
        filter_cfg=dict(filter_empty_gt=False, min_size=32)),
    pipeline=[
        dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),
        dict(
            type='RandomAffine',
            scaling_ratio_range=(0.1, 2),
            border=(-320, -320)),
        dict(
            type='MixUp',
            img_scale=(640, 640),
            ratio_range=(0.8, 1.6),
            pad_val=114.0),
        dict(type='YOLOXHSVRandomAug'),
        dict(type='RandomFlip', prob=0.5),
        dict(type='Resize', scale=(640, 640), keep_ratio=True),
        dict(
            type='Pad',
            pad_to_square=True,
            pad_val=dict(img=(114.0, 114.0, 114.0))),
        dict(
            type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
        dict(type='PackDetInputs')
    ])
test_pipeline = [
    dict(type='LoadImageFromFile', backend_args=dict(backend='local')),
    dict(type='Resize', scale=(640, 640), keep_ratio=True),
    dict(
        type='Pad',
        pad_to_square=True,
        pad_val=dict(img=(114.0, 114.0, 114.0))),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]
train_dataloader = dict(
    batch_size=8,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type='MultiImageMixDataset',
        dataset=dict(
            type='CocoDataset',
            data_root='data/coco/',
            ann_file='annotations/coco_face_train.json',
            data_prefix=dict(img='train2017/'),
            pipeline=[
                dict(
                    type='LoadImageFromFile',
                    backend_args=dict(backend='local')),
                dict(type='LoadAnnotations', with_bbox=True)
            ],
            filter_cfg=dict(filter_empty_gt=False, min_size=32),
            metainfo=dict(CLASSES=('person', ), PALETTE=(220, 20, 60))),
        pipeline=[
            dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),
            dict(
                type='RandomAffine',
                scaling_ratio_range=(0.1, 2),
                border=(-320, -320)),
            dict(
                type='MixUp',
                img_scale=(640, 640),
                ratio_range=(0.8, 1.6),
                pad_val=114.0),
            dict(type='YOLOXHSVRandomAug'),
            dict(type='RandomFlip', prob=0.5),
            dict(type='Resize', scale=(640, 640), keep_ratio=True),
            dict(
                type='Pad',
                pad_to_square=True,
                pad_val=dict(img=(114.0, 114.0, 114.0))),
            dict(
                type='FilterAnnotations',
                min_gt_bbox_wh=(1, 1),
                keep_empty=False),
            dict(type='PackDetInputs')
        ]))
val_dataloader = dict(
    batch_size=8,
    num_workers=4,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='CocoDataset',
        data_root='data/coco/',
        ann_file='annotations/coco_face_val.json',
        data_prefix=dict(img='val2017/'),
        test_mode=True,
        pipeline=[
            dict(type='LoadImageFromFile', backend_args=dict(backend='local')),
            dict(type='Resize', scale=(640, 640), keep_ratio=True),
            dict(
                type='Pad',
                pad_to_square=True,
                pad_val=dict(img=(114.0, 114.0, 114.0))),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                type='PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ],
        metainfo=dict(CLASSES=('person', ), PALETTE=(220, 20, 60))))
test_dataloader = dict(
    batch_size=8,
    num_workers=4,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='CocoDataset',
        data_root='data/coco/',
        ann_file='annotations/coco_face_val.json',
        data_prefix=dict(img='val2017/'),
        test_mode=True,
        pipeline=[
            dict(type='LoadImageFromFile', backend_args=dict(backend='local')),
            dict(type='Resize', scale=(640, 640), keep_ratio=True),
            dict(
                type='Pad',
                pad_to_square=True,
                pad_val=dict(img=(114.0, 114.0, 114.0))),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                type='PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ],
        metainfo=dict(CLASSES=('person', ), PALETTE=(220, 20, 60))))
val_evaluator = dict(
    type='CocoMetric',
    ann_file='data/coco/annotations/coco_face_val.json',
    metric='bbox')
test_evaluator = dict(
    type='CocoMetric',
    ann_file='data/coco/annotations/instances_val2017.json',
    metric='bbox')
max_epochs = 300
num_last_epochs = 15
interval = 10
base_lr = 0.01
custom_hooks = [
    dict(type='YOLOXModeSwitchHook', num_last_epochs=15, priority=48),
    dict(type='SyncNormHook', priority=48),
    dict(
        type='EMAHook',
        ema_type='ExpMomentumEMA',
        momentum=0.0001,
        strict_load=False,
        update_buffers=True,
        priority=49)
]
metainfo = dict(CLASSES=('person', ), PALETTE=(220, 20, 60))
launcher = 'pytorch'

四、output文件夹

这里主要存放模型推理处理过后的图像

work_dirs文件夹：主要存放我的训练日志

Ear.py

（这个文件主要用python的pyqt框架写一个特比简单的qt界面）

# -*- coding: utf-8 -*-

# Form implementation generated from reading ui file 'Ear.ui'
#
# Created by: PyQt5 UI code generator 5.15.9
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again.  Do not edit this file unless you know what you are doing.


from PyQt5 import QtCore, QtGui, QtWidgets


class Ui_Form(object):
    def setupUi(self, Form):
        Form.setObjectName("Form")
        Form.resize(795, 593)
        self.listWidget = QtWidgets.QListWidget(Form)
        self.listWidget.setGeometry(QtCore.QRect(90, 120, 221, 261))
        self.listWidget.setObjectName("listWidget")
        self.listWidget_2 = QtWidgets.QListWidget(Form)
        self.listWidget_2.setGeometry(QtCore.QRect(480, 120, 221, 261))
        self.listWidget_2.setObjectName("listWidget_2")
        self.pushButton = QtWidgets.QPushButton(Form)
        self.pushButton.setGeometry(QtCore.QRect(340, 310, 111, 31))
        self.pushButton.setObjectName("pushButton")
        self.label = QtWidgets.QLabel(Form)
        self.label.setGeometry(QtCore.QRect(-30, -70, 1256, 707))
        self.label.setText("")
        self.label.setPixmap(QtGui.QPixmap("C:/Users/lenovo/Pictures/0d9289b1001061e53ca12b2063016c0.png"))
        self.label.setScaledContents(True)
        self.label.setWordWrap(False)
        self.label.setOpenExternalLinks(True)
        self.label.setObjectName("label")
        self.pushButton_2 = QtWidgets.QPushButton(Form)
        self.pushButton_2.setGeometry(QtCore.QRect(140, 200, 121, 101))
        self.pushButton_2.setBaseSize(QtCore.QSize(0, 0))
        self.pushButton_2.setIconSize(QtCore.QSize(16, 16))
        self.pushButton_2.setShortcut("")
        self.pushButton_2.setCheckable(False)
        self.pushButton_2.setAutoRepeatInterval(100)
        self.pushButton_2.setObjectName("pushButton_2")
        self.label.raise_()
        self.listWidget.raise_()
        self.listWidget_2.raise_()
        self.pushButton.raise_()
        self.pushButton_2.raise_()

        self.retranslateUi(Form)
        QtCore.QMetaObject.connectSlotsByName(Form)

    def retranslateUi(self, Form):
        _translate = QtCore.QCoreApplication.translate
        Form.setWindowTitle(_translate("Form", "Form"))
        self.pushButton.setText(_translate("Form", "开始转换"))
        self.pushButton_2.setText(_translate("Form", "上传"))

接着可以打开Ear_main.py文件运行一下查看效果:

五、上传我提前为大家准备好的侧视图test1.png，点击开始转换

处理好的图片保存到outputs文件夹里了

有两个warning可能是因为版本冲突的问题不是error可以忽略结果图

致辞这个项目完成啦，别忘了点赞收藏哦！！！继续关注我，教大家更多cv项目！！！