This tutorial collects answers to any How to xxx with MMYOLO
. Feel free to update this doc if you meet new questions about How to
and find the answers!
Please see Plugins.
If you want to stack multiple Necks, you can directly set the Neck parameters in the config. MMYOLO supports concatenating multiple Necks in the form of List
. You need to ensure that the output channel of the previous Neck matches the input channel of the next Neck. If you need to adjust the number of channels, you can insert the mmdet.ChannelMapper
module to align the number of channels between multiple Necks. The specific configuration is as follows:
_base_ = './yolov5_s-v61_syncbn_8xb16-300e_coco.py'
deepen_factor = _base_.deepen_factor
widen_factor = _base_.widen_factor
model = dict(
type='YOLODetector',
neck=[
dict(
type='YOLOv5PAFPN',
deepen_factor=deepen_factor,
widen_factor=widen_factor,
in_channels=[256, 512, 1024],
out_channels=[256, 512, 1024], # The out_channels is controlled by widen_factor,so the YOLOv5PAFPN's out_channels equls to out_channels * widen_factor
num_csp_blocks=3,
norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
act_cfg=dict(type='SiLU', inplace=True)),
dict(
type='mmdet.ChannelMapper',
in_channels=[128, 256, 512],
out_channels=128,
),
dict(
type='mmdet.DyHead',
in_channels=128,
out_channels=256,
num_blocks=2,
# disable zero_init_offset to follow official implementation
zero_init_offset=False)
]
bbox_head=dict(head_module=dict(in_channels=[512,512,512])) # The out_channels is controlled by widen_factor,so the YOLOv5HeadModuled in_channels * widen_factor equals to the last neck's out_channels
)
1. When using other backbone networks, you need to ensure that the output channels of the backbone network match the input channels of the neck network.
2. The configuration files given below only ensure that the training will work correctly, and their training performance may not be optimal. Because some backbones require specific learning rates, optimizers, and other hyperparameters. Related contents will be added in the "Training Tips" section later.
Suppose you want to use YOLOv6EfficientRep
as the backbone network of YOLOv5
, the example config is as the following:
_base_ = './yolov5_s-v61_syncbn_8xb16-300e_coco.py'
model = dict(
backbone=dict(
type='YOLOv6EfficientRep',
norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
act_cfg=dict(type='ReLU', inplace=True))
)
The model registry in MMYOLO, MMDetection, MMClassification, and MMSegmentation all inherit from the root registry in MMEngine in the OpenMMLab 2.0 system, allowing these repositories to directly use modules already implemented by each other. Therefore, in MMYOLO, users can use backbone networks from MMDetection and MMClassification without reimplementation.
-
Suppose you want to use
ResNet-50
as the backbone network ofYOLOv5
, the example config is as the following:_base_ = './yolov5_s-v61_syncbn_8xb16-300e_coco.py' deepen_factor = _base_.deepen_factor widen_factor = 1.0 channels = [512, 1024, 2048] model = dict( backbone=dict( _delete_=True, # Delete the backbone field in _base_ type='mmdet.ResNet', # Using ResNet from mmdet depth=50, num_stages=4, out_indices=(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='YOLOv5PAFPN', widen_factor=widen_factor, in_channels=channels, # Note: The 3 channels of ResNet-50 output are [512, 1024, 2048], which do not match the original yolov5-s neck and need to be changed. out_channels=channels), bbox_head=dict( type='YOLOv5Head', head_module=dict( type='YOLOv5HeadModule', in_channels=channels, # input channels of head need to be changed accordingly widen_factor=widen_factor)) )
-
Suppose you want to use
SwinTransformer-Tiny
as the backbone network ofYOLOv5
, the example config is as the following:_base_ = './yolov5_s-v61_syncbn_8xb16-300e_coco.py' deepen_factor = _base_.deepen_factor widen_factor = 1.0 channels = [192, 384, 768] checkpoint_file = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa model = dict( backbone=dict( _delete_=True, # Delete the backbone field in _base_ type='mmdet.SwinTransformer', # Using SwinTransformer from mmdet embed_dims=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7, mlp_ratio=4, qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.2, patch_norm=True, out_indices=(1, 2, 3), with_cp=False, convert_weights=True, init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)), neck=dict( type='YOLOv5PAFPN', deepen_factor=deepen_factor, widen_factor=widen_factor, in_channels=channels, # Note: The 3 channels of SwinTransformer-Tiny output are [192, 384, 768], which do not match the original yolov5-s neck and need to be changed. out_channels=channels), bbox_head=dict( type='YOLOv5Head', head_module=dict( type='YOLOv5HeadModule', in_channels=channels, # input channels of head need to be changed accordingly widen_factor=widen_factor)) )
-
Suppose you want to use
ConvNeXt-Tiny
as the backbone network ofYOLOv5
, the example config is as the following:_base_ = './yolov5_s-v61_syncbn_8xb16-300e_coco.py' # please run the command, mim install "mmcls>=1.0.0rc2", to install mmcls # import mmcls.models to trigger register_module in mmcls custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False) checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa deepen_factor = _base_.deepen_factor widen_factor = 1.0 channels = [192, 384, 768] model = dict( backbone=dict( _delete_=True, # Delete the backbone field in _base_ type='mmcls.ConvNeXt', # Using ConvNeXt from mmcls arch='tiny', out_indices=(1, 2, 3), drop_path_rate=0.4, layer_scale_init_value=1.0, gap_before_final_norm=False, init_cfg=dict( type='Pretrained', checkpoint=checkpoint_file, prefix='backbone.')), # The pre-trained weights of backbone network in MMCls have prefix='backbone.'. The prefix in the keys will be removed so that these weights can be normally loaded. neck=dict( type='YOLOv5PAFPN', deepen_factor=deepen_factor, widen_factor=widen_factor, in_channels=channels, # Note: The 3 channels of ConvNeXt-Tiny output are [192, 384, 768], which do not match the original yolov5-s neck and need to be changed. out_channels=channels), bbox_head=dict( type='YOLOv5Head', head_module=dict( type='YOLOv5HeadModule', in_channels=channels, # input channels of head need to be changed accordingly widen_factor=widen_factor)) )
-
Suppose you want to use
MobileNetV3-small
as the backbone network ofYOLOv5
, the example config is as the following:_base_ = './yolov5_s-v61_syncbn_8xb16-300e_coco.py' # please run the command, mim install "mmcls>=1.0.0rc2", to install mmcls # import mmcls.models to trigger register_module in mmcls custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False) checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_small-8427ecf0.pth' # noqa deepen_factor = _base_.deepen_factor widen_factor = 1.0 channels = [24, 48, 96] model = dict( backbone=dict( _delete_=True, # Delete the backbone field in _base_ type='mmcls.MobileNetV3', # Using MobileNetV3 from mmcls arch='small', out_indices=(3, 8, 11), # Modify out_indices init_cfg=dict( type='Pretrained', checkpoint=checkpoint_file, prefix='backbone.')), # The pre-trained weights of backbone network in MMCls have prefix='backbone.'. The prefix in the keys will be removed so that these weights can be normally loaded. neck=dict( type='YOLOv5PAFPN', deepen_factor=deepen_factor, widen_factor=widen_factor, in_channels=channels, # Note: The 3 channels of MobileNetV3 output are [24, 48, 96], which do not match the original yolov5-s neck and need to be changed. out_channels=channels), bbox_head=dict( type='YOLOv5Head', head_module=dict( type='YOLOv5HeadModule', in_channels=channels, # input channels of head need to be changed accordingly widen_factor=widen_factor)) )
MMClassification also provides a wrapper for the PyTorch Image Models (timm
) backbone network, users can directly use the backbone network in timm
through MMClassification. Suppose you want to use EfficientNet-B1
as the backbone network of YOLOv5
, the example config is as the following:
_base_ = './yolov5_s-v61_syncbn_8xb16-300e_coco.py'
# please run the command, mim install "mmcls>=1.0.0rc2", to install mmcls
# and the command, pip install timm, to install timm
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
deepen_factor = _base_.deepen_factor
widen_factor = 1.0
channels = [40, 112, 320]
model = dict(
backbone=dict(
_delete_=True, # Delete the backbone field in _base_
type='mmcls.TIMMBackbone', # Using timm from mmcls
model_name='efficientnet_b1', # Using efficientnet_b1 in timm
features_only=True,
pretrained=True,
out_indices=(2, 3, 4)),
neck=dict(
type='YOLOv5PAFPN',
deepen_factor=deepen_factor,
widen_factor=widen_factor,
in_channels=channels, # Note: The 3 channels of EfficientNet-B1 output are [40, 112, 320], which do not match the original yolov5-s neck and need to be changed.
out_channels=channels),
bbox_head=dict(
type='YOLOv5Head',
head_module=dict(
type='YOLOv5HeadModule',
in_channels=channels, # input channels of head need to be changed accordingly
widen_factor=widen_factor))
)
Suppose you want to use ResNet-50
which is self-supervised trained by MoCo v3
in MMSelfSup as the backbone network of YOLOv5
, the example config is as the following:
_base_ = './yolov5_s-v61_syncbn_8xb16-300e_coco.py'
# please run the command, mim install "mmselfsup>=1.0.0rc3", to install mmselfsup
# import mmselfsup.models to trigger register_module in mmselfsup
custom_imports = dict(imports=['mmselfsup.models'], allow_failed_imports=False)
checkpoint_file = 'https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_resnet50_8xb512-amp-coslr-800e_in1k/mocov3_resnet50_8xb512-amp-coslr-800e_in1k_20220927-e043f51a.pth' # noqa
deepen_factor = _base_.deepen_factor
widen_factor = 1.0
channels = [512, 1024, 2048]
model = dict(
backbone=dict(
_delete_=True, # Delete the backbone field in _base_
type='mmselfsup.ResNet',
depth=50,
num_stages=4,
out_indices=(2, 3, 4), # Note: out_indices of ResNet in MMSelfSup are 1 larger than those in MMdet and MMCls
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)),
neck=dict(
type='YOLOv5PAFPN',
deepen_factor=deepen_factor,
widen_factor=widen_factor,
in_channels=channels, # Note: The 3 channels of ResNet-50 output are [512, 1024, 2048], which do not match the original yolov5-s neck and need to be changed.
out_channels=channels),
bbox_head=dict(
type='YOLOv5Head',
head_module=dict(
type='YOLOv5HeadModule',
in_channels=channels, # input channels of head need to be changed accordingly
widen_factor=widen_factor))
)
If you want to save the prediction results as a specific file for offline evaluation, MMYOLO currently supports both json and pkl formats.
The json file only save `image_id`, `bbox`, `score` and `category_id`. The json file can be read using the json library.
The pkl file holds more content than the json file, and also holds information such as the file name and size of the predicted image; the pkl file can be read using the pickle library. The pkl file can be read using the pickle library.
If you want to output the prediction results as a json file, the command is as follows.
python tools/test.py {path_to_config} {path_to_checkpoint} --json-prefix {json_prefix}
The argument after --json-prefix
should be a filename prefix (no need to enter the .json
suffix) and can also contain a path. For a concrete example:
python tools/test.py configs\yolov5\yolov5_s-v61_syncbn_8xb16-300e_coco.py yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth --json-prefix work_dirs/demo/json_demo
Running the above command will output the json_demo.bbox.json
file in the work_dirs/demo
folder.
If you want to output the prediction results as a pkl file, the command is as follows.
python tools/test.py {path_to_config} {path_to_checkpoint} --out {path_to_output_file}
The argument after --out
should be a full filename (must be with a .pkl
or .pickle
suffix) and can also contain a path. For a concrete example:
python tools/test.py configs\yolov5\yolov5_s-v61_syncbn_8xb16-300e_coco.py yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth --out work_dirs/demo/pkl_demo.pkl
Running the above command will output the pkl_demo.pkl
file in the work_dirs/demo
folder.
1. All script calls across libraries are currently not supported and are being fixed. More examples will be added to this document when the fix is complete. 2.
2. mAP plotting and average training speed calculation are fixed in the MMDetection dev-3.x branch, which currently needs to be installed via the source code to be run successfully.
tools/analysis_tools/analyze_logs.py
plots loss/mAP curves given a training log file. Run pip install seaborn
first to install the dependency.
mim run mmdet analyze_logs plot_curve \
${LOG} \ # path of train log in json format
[--keys ${KEYS}] \ # the metric that you want to plot, default to 'bbox_mAP'
[--start-epoch ${START_EPOCH}] # the epoch that you want to start, default to 1
[--eval-interval ${EVALUATION_INTERVAL}] \ # the evaluation interval when training, default to 1
[--title ${TITLE}] \ # title of figure
[--legend ${LEGEND}] \ # legend of each plot, default to None
[--backend ${BACKEND}] \ # backend of plt, default to None
[--style ${STYLE}] \ # style of plt, default to 'dark'
[--out ${OUT_FILE}] # the path of output file
# [] stands for optional parameters, when actually entering the command line, you do not need to enter []
Examples:
-
Plot the classification loss of some run.
mim run mmdet analyze_logs plot_curve \ yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700.log.json \ --keys loss_cls \ --legend loss_cls
-
Plot the classification and regression loss of some run, and save the figure to a pdf.
mim run mmdet analyze_logs plot_curve \ yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700.log.json \ --keys loss_cls loss_bbox \ --legend loss_cls loss_bbox \ --out losses_yolov5_s.pdf
-
Compare the bbox mAP of two runs in the same figure.
mim run mmdet analyze_logs plot_curve \ yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700.log.json \ yolov5_n-v61_syncbn_fast_8xb16-300e_coco_20220919_090739.log.json \ --keys bbox_mAP \ --legend yolov5_s yolov5_n \ --eval-interval 10 # Note that the evaluation interval must be the same as during training. Otherwise, it will raise an error.
mim run mmdet analyze_logs cal_train_time \
${LOG} \ # path of train log in json format
[--include-outliers] # include the first value of every epoch when computing the average time
Examples:
mim run mmdet analyze_logs cal_train_time \
yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700.log.json
The output is expected to be like the following.
-----Analyze train time of yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700.log.json-----
slowest epoch 278, average time is 0.1705 s/iter
fastest epoch 300, average time is 0.1510 s/iter
time std over epochs is 0.0026
average iter time: 0.1556 s/iter
print_config.py
in MMDetection prints the whole config verbatim, expanding all its imports. The command is as following.
mim run mmdet print_config \
${CONFIG} \ # path of the config file
[--save-path] \ # save path of whole config, suffixed with .py, .json or .yml
[--cfg-options ${OPTIONS [OPTIONS...]}] # override some settings in the used config
Examples:
mim run mmdet print_config \
configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py \
--save-path ./work_dirs/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py
Running the above command will save the yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py
config file with the inheritance relationship expanded to ``yolov5_s-v61_syncbn_fast_1xb4-300e_balloon_whole.pyin the
./work_dirs` folder.