MMDetection新手安装使用教程（无限踩坑）
MMDetection是商汤和港中文大学针对目标检测任务推出的一个开源项目，它基于Pytorch实现了大量的目标检测算法，例如fast_rcnn,frater_rcnn,detr等算法。提示：以下是本篇文章正文内容，下面案例可供参考以上就是今天要讲的内容，本文主要介绍了mmdetection的安装以及采用标准COCO数据集的使用过程，后续如果读者需要，也会介绍自己数据集的制作以及利用mmdetec
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文章目录
前言
MMDetection是商汤和港中文大学针对目标检测任务推出的一个开源项目，它基于Pytorch实现了大量的目标检测算法，例如fast_rcnn,frater_rcnn,detr等算法。
提示：以下是本篇文章正文内容，下面案例可供参考
一、MMDetection安装过程
1.torch+torchvision的安装
首先我们要安装mmdetection利用GPU推理训练的CUDA以及Pytorch框架。以我自身的RTX3060 12G为例，我安装的是CUDA11.3+torch1.11.0+torchvision0.12.0.
CUDA11.3的安装请查看其他教程链接。链接: link
这里推荐创建一个新的虚拟环境进行安装，打开anaconda prompt
// 创建虚拟环境
conda create -n 环境名 python=X.X
以python3.9为例(以自己的python版本为准)
conda create -n pythorch2 python=3.9
激活环境
activate pythorch2
之后下载torch1.11.0和torchvision0.12.0文件，文件下载链接如下：
链接: link
注意自己的python版本以及win平台
往下滑下载找到对应的torchvision版本，torch1.11对应torchvision0.12.0版本
打开在下载好的两个文件的文件夹中，复制文件的地址
回到我们的虚拟环境中进行安装torch
同样的方法安装torchvision
安装完成后进行pip list查看
测试torch安装是否成功，显示true表明torch安装成功
2.mmdetection的安装
官网安装简介链接如下：
https://mmdetection.readthedocs.io/zh_CN/latest/get_started.html
显卡的注意事项：对于30系列的显卡建议安装CUDA11.X的版本
官网给出了基本的安装步骤，如下图：
在步骤1中，建议采用方案a进行安装，因为在之后使用的过程中，如果采用方案b作为第三方库进行安装的话，在后续的使用过程中可能会出现部分项目无法编辑的问题。
当然，官网文档中也给出了使用pip 进行安装的教程,这两种安装方法都可以,还是在我们的虚拟环境中进行pip install 安装。
// 安装mmengine
pip install mmengine;
// 安装 MMCV(以cuda113+torch1.11为例)
pip install "mmcv>=2.0.0" -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html;
// 安装mmdet
git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
pip install -v -e .
# "-v" 指详细说明，或更多的输出
# "-e" 表示在可编辑模式下安装项目，因此对代码所做的任何本地修改都会生效，从而无需重新安装。;
安装完成后在pip list查看会有如下三个库
之后在运行项目的时候如果出现缺少一些基本库的现象，采用pip install进行下载安装即可。例如安装CV2
pip install opencv-python
二、MMDetection的使用步骤
1.下载项目文件
我下载的是mmdetection3.0.0项目文件，如下图:
在Tags中选择v3.0.0版本项目，链接如下：
https://github.com/open-mmlab/mmdetection/tree/v3.0.0
之后Download Zip下载到本地即可
2.使用教程
2.1验证安装是否成功
用pycharm打开项目文件（注意将解释器配置成自己创建的虚拟环境），在image_demo.py中进行验证
官网给出了验证过程，当然我们也可以在pycharm中进行验证。首先在官网下载需要的模型文件和权重文件
mim download mmdet --config rtmdet_tiny_8xb32-300e_coco --dest .
在cmd运行上面指令，一般会下载到用户文件夹（C:\Users\Ogl），将下载好的两个文件放到mmdetection3.0.0项目文件夹中
之后在pycharm的image_demo.py运行前传入这两个文件的地址以及demo图片的地址作为参数传入。
复制三个文件的绝对路径，示例如下：(注意是双斜杠\\)
E:\\mmdetection-3.0.0\\mmdetection-3.0.0\\demo\\demo.jpg（图片地址）
E:\\mmdetection-3.0.0\\mmdetection-3.0.0\\rtmdet_tiny_8xb32-300e_coco.py（模型文件地址）
--weights
E:\\mmdetection-3.0.0\\mmdetection-3.0.0\\rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth（权重文件地址）
--show
将文件地址复制到参数项(parameters)中
之后运行image_demo.py即可
能显示demo.jpg图片的物体被成功识别及预测后，自此我们的mmdetection安装运行成功
2.2选择模型对COCO数据集进行推理训练
本文以faster_rcnn_r50_fpn_1x_coco.py为例，我们在configs中找到模型文件
之后在./tools/train.py中传入该文件地址，在work_dirs中会生成一个faster_rcnn_r50_fpn_1x_coco.py文件，该文件包含之前py文件的各种配置文件，方便我们后续对模型进行修改
之后下载coco数据集，我这里用的是coco2017数据集
如果要使用自己的数据集需按照官网的数据集格式准备数据集，mmdetection官网数据集准备链接如下：
链接: link
在detection3.0.0项目新建data文件夹，将COCO数据集放到data文件夹中，就可以运行tools/train.py 进行训练了
打开terminal窗口，在窗口中输入以下指令
(pytorch2) PS E:\mmdetection-3.0.0\mmdetection-3.0.0> python .\tools\train.py E:\\mmdetection-3.0.0\\mmdetection-3.0.0\\work_dirs\\faster-rcnn_r50_fpn_1x_coco\\faster-rcnn_r50_fpn_1x_coco.py
成功运行后会在终端提示如下信息，并开始训练。
(pytorch2) PS E:\mmdetection-3.0.0\mmdetection-3.0.0> python .\tools\train.py E:\\mmdetection-3.0.0\\mmdetection-3.0.0\\work_dirs\\faster-rcnn_r50_fpn_1x_coco\\faster-rcnn_r50_fpn_1x_coco.py
06/15 14:05:09 - mmengine - INFO -
------------------------------------------------------------
System environment:
sys.platform: win32
Python: 3.9.16 (main, Mar 8 2023, 10:39:24) [MSC v.1916 64 bit (AMD64)]
CUDA available: True
numpy_random_seed: 201700358
GPU 0: NVIDIA GeForce RTX 3060
CUDA_HOME: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.3
NVCC: Cuda compilation tools, release 11.3, V11.3.58
MSVC: 用于 x64 的 Microsoft (R) C/C++ 优化编译器 19.34.31942 版
GCC: n/a
PyTorch: 1.11.0+cu113
PyTorch compiling details: PyTorch built with:
- C++ Version: 199711
- MSVC 192829337
- Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)
- OpenMP 2019
- LAPACK is enabled (usually provided by MKL)
- CPU capability usage: AVX2
- CUDA Runtime 11.3
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
- CuDNN 8.2
- Magma 2.5.4
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=C:/actions-runner/_work/pytorch/pytorch/builder/windows/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /GR /EHsc /w /b
igobj -DUSE_PTHREADPOOL -openmp:experimental -IC:/actions-runner/_work/pytorch/pytorch/builder/windows/mkl/include -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILE
R_USE_KINETO, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=OFF, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.12.0+cu113
OpenCV: 4.7.0
MMEngine: 0.7.4
Runtime environment:
cudnn_benchmark: False
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
dist_cfg: {'backend': 'nccl'}
seed: 201700358
Distributed launcher: none
Distributed training: False
GPU number: 1
------------------------------------------------------------
06/15 14:05:09 - mmengine - INFO - Config:
model = dict(
type='FasterRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
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]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', 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=2,
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]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', 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)))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
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', backend_args=None),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
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='CocoDataset',
data_root='data/labelme-data/coco-formatcoco/',
ann_file='annotations/instances_train2017.json',
data_prefix=dict(img='images/train2017/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=[
dict(type='LoadImageFromFile', backend_args=None),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
],
backend_args=None))
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='CocoDataset',
data_root='data/labelme-data/coco-formatcoco/',
ann_file='annotations/instances_val2017.json',
data_prefix=dict(img='images/val2017/'),
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile', backend_args=None),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
],
backend_args=None))
test_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='CocoDataset',
data_root='data/labelme-data/coco-formatcoco/',
ann_file='annotations/instances_val2017.json',
data_prefix=dict(img='images/val2017/'),
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile', backend_args=None),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
],
backend_args=None))
val_evaluator = dict(
type='CocoMetric',
ann_file=
'data/labelme-data/coco-formatcoco/annotations/instances_val2017.json',
metric='bbox',
format_only=False,
backend_args=None)
test_evaluator = dict(
type='CocoMetric',
ann_file=
'data/labelme-data/coco-formatcoco/annotations/instances_val2017.json',
metric='bbox',
format_only=False,
backend_args=None)
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=50, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=50,
by_epoch=True,
milestones=[8, 11],
gamma=0.1)
]
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001))
auto_scale_lr = dict(enable=False, base_batch_size=16)
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', draw=True, test_out_dir='out'))
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 = '.\\work_dirs\\faster-rcnn_r50_fpn_1x_coco\\epoch_50.pth'
resume = False
launcher = 'none'
work_dir = './work_dirs\\faster-rcnn_r50_fpn_1x_coco'
06/15 14:05:10 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.
06/15 14:05:10 - mmengine - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) RuntimeInfoHook
(BELOW_NORMAL) LoggerHook
--------------------
before_train:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(VERY_LOW ) CheckpointHook
--------------------
before_train_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(NORMAL ) DistSamplerSeedHook
--------------------
before_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
--------------------
after_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
after_train_epoch:
(NORMAL ) IterTimerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
before_val_epoch:
(NORMAL ) IterTimerHook
--------------------
before_val_iter:
(NORMAL ) IterTimerHook
--------------------
after_val_iter:
(NORMAL ) IterTimerHook
(NORMAL ) DetVisualizationHook
(BELOW_NORMAL) LoggerHook
--------------------
after_val_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
after_train:
(VERY_LOW ) CheckpointHook
--------------------
before_test_epoch:
(NORMAL ) IterTimerHook
--------------------
before_test_iter:
(NORMAL ) IterTimerHook
--------------------
after_test_iter:
(NORMAL ) IterTimerHook
(NORMAL ) DetVisualizationHook
(BELOW_NORMAL) LoggerHook
--------------------
after_test_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
--------------------
after_run:
(BELOW_NORMAL) LoggerHook
--------------------
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
loading annotations into memory...
Done (t=0.02s)
creating index...
index created!
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
06/15 14:05:12 - mmengine - INFO - load model from: torchvision://resnet50
06/15 14:05:12 - mmengine - INFO - Loads checkpoint by torchvision backend from path: torchvision://resnet50
06/15 14:05:12 - mmengine - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: fc.weight, fc.bias
Loads checkpoint by local backend from path: .\work_dirs\faster-rcnn_r50_fpn_1x_coco\epoch_50.pth
06/15 14:05:12 - mmengine - INFO - Load checkpoint from .\work_dirs\faster-rcnn_r50_fpn_1x_coco\epoch_50.pth
06/15 14:05:12 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
06/15 14:05:12 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
06/15 14:05:12 - mmengine - INFO - Checkpoints will be saved to E:\mmdetection-3.0.0\mmdetection-3.0.0\work_dirs\faster-rcnn_r50_fpn_1x_coco.
06/15 14:05:20 - mmengine - INFO - Exp name: faster-rcnn_r50_fpn_1x_coco_20230615_140507
06/15 14:05:20 - mmengine - INFO - Epoch(train) [1][9/9] lr: 3.4032e-04 eta: 0:06:53 time: 0.9368 data_time: 0.2160 memory: 4485 loss: 0.8013 loss_rpn_cls: 0.0591 loss_rpn_bbox: 0.0445 loss_cls: 0.2363 acc: 92.2852 loss_bbox: 0.4615
06/15 14:05:20 - mmengine - INFO - Saving checkpoint at 1 epochs
06/15 14:05:23 - mmengine - INFO - Evaluating bbox...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.02s).
Accumulating evaluation results...
DONE (t=0.02s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.153
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.435
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.073
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.318
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.317
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.317
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.317
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.317
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
06/15 14:05:23 - mmengine - INFO - bbox_mAP_copypaste: 0.153 0.435 0.073 0.318 -1.000 -1.000
06/15 14:05:23 - mmengine - INFO - Epoch(val) [1][2/2] coco/bbox_mAP: 0.1530 coco/bbox_mAP_50: 0.4350 coco/bbox_mAP_75: 0.0730 coco/bbox_mAP_s: 0.3180 coco/bbox_mAP_m: -1.0000 coco/bbox_mAP_l: -1.0000 data_time: 0.6178 time: 0.8028
06/15 14:05:27 - mmengine - INFO - Exp name: faster-rcnn_r50_fpn_1x_coco_20230615_140507
06/15 14:05:27 - mmengine - INFO - Epoch(train) [2][9/9] lr: 7.0068e-04 eta: 0:05:02 time: 0.6997 data_time: 0.1117 memory: 5159 loss: 0.8078 loss_rpn_cls: 0.0608 loss_rpn_bbox: 0.0441 loss_cls: 0.2357 acc: 92.7734 loss_bbox: 0.4672
06/15 14:05:27 - mmengine - INFO - Saving checkpoint at 2 epochs
06/15 14:05:29 - mmengine - INFO - Evaluating bbox...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.02s).
Accumulating evaluation results...
DONE (t=0.01s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.140
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.401
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.037
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.314
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.312
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.312
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.312
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.312
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
06/15 14:05:29 - mmengine - INFO - bbox_mAP_copypaste: 0.140 0.401 0.037 0.314 -1.000 -1.000
06/15 14:05:29 - mmengine - INFO - Epoch(val) [2][2/2] coco/bbox_mAP: 0.1400 coco/bbox_mAP_50: 0.4010 coco/bbox_mAP_75: 0.0370 coco/bbox_mAP_s: 0.3140 coco/bbox_mAP_m: -1.0000 coco/bbox_mAP_l: -1.0000 data_time: 0.0082 time: 0.0971
06/15 14:05:33 - mmengine - INFO - Exp name: faster-rcnn_r50_fpn_1x_coco_20230615_140507
06/15 14:05:33 - mmengine - INFO - Epoch(train) [3][9/9] lr: 1.0610e-03 eta: 0:04:20 time: 0.6156 data_time: 0.0756 memory: 5388 loss: 0.8012 loss_rpn_cls: 0.0611 loss_rpn_bbox: 0.0444 loss_cls: 0.2323 acc: 94.4336 loss_bbox: 0.4634
06/15 14:05:33 - mmengine - INFO - Saving checkpoint at 3 epochs
06/15 14:05:34 - mmengine - INFO - Evaluating bbox...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.00s).
Accumulating evaluation results...
DONE (t=0.02s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.148
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.445
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.052
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.319
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.317
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.317
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.317
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.317
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
06/15 14:05:34 - mmengine - INFO - bbox_mAP_copypaste: 0.148 0.445 0.052 0.319 -1.000 -1.000
06/15 14:05:34 - mmengine - INFO - Epoch(val) [3][2/2] coco/bbox_mAP: 0.1480 coco/bbox_mAP_50: 0.4450 coco/bbox_mAP_75: 0.0520 coco/bbox_mAP_s: 0.3190 coco/bbox_mAP_m: -1.0000 coco/bbox_mAP_l: -1.0000 data_time: 0.0111 time: 0.0956
06/15 14:05:38 - mmengine - INFO - Exp name: faster-rcnn_r50_fpn_1x_coco_20230615_140507
06/15 14:05:38 - mmengine - INFO - Epoch(train) [4][9/9] lr: 1.4214e-03 eta: 0:03:58 time: 0.5761 data_time: 0.0581 memory: 5388 loss: 0.8093 loss_rpn_cls: 0.0605 loss_rpn_bbox: 0.0454 loss_cls: 0.2331 acc: 95.0195 loss_bbox: 0.4703
06/15 14:05:38 - mmengine - INFO - Saving checkpoint at 4 epochs
06/15 14:05:40 - mmengine - INFO - Evaluating bbox...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.02s).
Accumulating evaluation results...
DONE (t=0.00s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.153
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.421
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.153
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.268
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.267
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.267
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.267
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.267
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
06/15 14:05:40 - mmengine - INFO - bbox_mAP_copypaste: 0.153 0.421 0.153 0.268 -1.000 -1.000
06/15 14:05:40 - mmengine - INFO - Epoch(val) [4][2/2] coco/bbox_mAP: 0.1530 coco/bbox_mAP_50: 0.4210 coco/bbox_mAP_75: 0.1530 coco/bbox_mAP_s: 0.2680 coco/bbox_mAP_m: -1.0000 coco/bbox_mAP_l: -1.0000 data_time: 0.0104 time: 0.0912
06/15 14:05:44 - mmengine - INFO - Exp name: faster-rcnn_r50_fpn_1x_coco_20230615_140507
06/15 14:05:44 - mmengine - INFO - Epoch(train) [5][9/9] lr: 1.7818e-03 eta: 0:03:42 time: 0.5506 data_time: 0.0474 memory: 5159 loss: 0.8008 loss_rpn_cls: 0.0594 loss_rpn_bbox: 0.0455 loss_cls: 0.2366 acc: 92.1875 loss_bbox: 0.4593
06/15 14:05:44 - mmengine - INFO - Saving checkpoint at 5 epochs
06/15 14:05:47 - mmengine - INFO - Evaluating bbox...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.04s).
Accumulating evaluation results...
DONE (t=0.01s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.170
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.525
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.087
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.290
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.288
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.288
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.288
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.288
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
06/15 14:05:47 - mmengine - INFO - bbox_mAP_copypaste: 0.170 0.525 0.087 0.290 -1.000 -1.000
06/15 14:05:47 - mmengine - INFO - Epoch(val) [5][2/2] coco/bbox_mAP: 0.1700 coco/bbox_mAP_50: 0.5250 coco/bbox_mAP_75: 0.0870 coco/bbox_mAP_s: 0.2900 coco/bbox_mAP_m: -1.0000 coco/bbox_mAP_l: -1.0000 data_time: 0.0156 time: 0.1525
;
当然我们也可以用到text.py 来对模型进行测试， 需要出入模型文件地址和训练的权重文件地址，训练的权重文件一般保存在work_dirs 文件夹下，我这里是E:\mmdetection-3.0.0\mmdetection-3.0.0\work_dirs\faster-rcnn_r50_fpn_1x_coco文件夹下
找到最后的epoch训练得到的权重参数，如下：
我这里总共训练了50次，就选择epoch_50.pth作为权重文件进行测试，测试指令如下：
(pytorch2) PS E:\mmdetection-3.0.0\mmdetection-3.0.0> python .\tools\test.py .\work_dirs/faster-rcnn_r50_fpn_1x_coco/faster-rcnn_r50_fpn_1x_coco.py .\work_dirs\faster-rcnn_r50_fpn_1x_coco\epoch_50.pth --show-dir out
在该文件夹下会生成一个out文件夹，就是对测试图片的识别和预测。
总结
以上就是今天要讲的内容，本文主要介绍了mmdetection的安装以及采用标准COCO数据集的使用过程，后续如果读者需要，也会介绍自己数据集的制作以及利用mmdetection中的模型进行训练和测试。
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