Copy disabled (too large)
Download .txt
Showing preview only (19,816K chars total). Download the full file to get everything.
Repository: WongKinYiu/yolov7
Branch: main
Commit: a207844b1ce8
Files: 87
Total size: 42.5 MB
Directory structure:
gitextract_uttfz7r5/
├── .gitignore
├── LICENSE.md
├── README.md
├── cfg/
│ ├── baseline/
│ │ ├── r50-csp.yaml
│ │ ├── x50-csp.yaml
│ │ ├── yolor-csp-x.yaml
│ │ ├── yolor-csp.yaml
│ │ ├── yolor-d6.yaml
│ │ ├── yolor-e6.yaml
│ │ ├── yolor-p6.yaml
│ │ ├── yolor-w6.yaml
│ │ ├── yolov3-spp.yaml
│ │ ├── yolov3.yaml
│ │ └── yolov4-csp.yaml
│ ├── deploy/
│ │ ├── yolov7-d6.yaml
│ │ ├── yolov7-e6.yaml
│ │ ├── yolov7-e6e.yaml
│ │ ├── yolov7-tiny-silu.yaml
│ │ ├── yolov7-tiny.yaml
│ │ ├── yolov7-w6.yaml
│ │ ├── yolov7.yaml
│ │ └── yolov7x.yaml
│ └── training/
│ ├── yolov7-d6.yaml
│ ├── yolov7-e6.yaml
│ ├── yolov7-e6e.yaml
│ ├── yolov7-tiny.yaml
│ ├── yolov7-w6.yaml
│ ├── yolov7.yaml
│ └── yolov7x.yaml
├── data/
│ ├── coco.yaml
│ ├── hyp.scratch.custom.yaml
│ ├── hyp.scratch.p5.yaml
│ ├── hyp.scratch.p6.yaml
│ └── hyp.scratch.tiny.yaml
├── deploy/
│ └── triton-inference-server/
│ ├── README.md
│ ├── boundingbox.py
│ ├── client.py
│ ├── labels.py
│ ├── processing.py
│ └── render.py
├── detect.py
├── export.py
├── hubconf.py
├── models/
│ ├── __init__.py
│ ├── common.py
│ ├── experimental.py
│ └── yolo.py
├── requirements.txt
├── scripts/
│ └── get_coco.sh
├── test.py
├── tools/
│ ├── YOLOv7-Dynamic-Batch-ONNXRUNTIME.ipynb
│ ├── YOLOv7-Dynamic-Batch-TENSORRT.ipynb
│ ├── YOLOv7CoreML.ipynb
│ ├── YOLOv7onnx.ipynb
│ ├── YOLOv7trt.ipynb
│ ├── compare_YOLOv7_vs_YOLOv5m6.ipynb
│ ├── compare_YOLOv7_vs_YOLOv5m6_half.ipynb
│ ├── compare_YOLOv7_vs_YOLOv5s6.ipynb
│ ├── compare_YOLOv7e6_vs_YOLOv5x6.ipynb
│ ├── compare_YOLOv7e6_vs_YOLOv5x6_half.ipynb
│ ├── instance.ipynb
│ ├── keypoint.ipynb
│ ├── reparameterization.ipynb
│ └── visualization.ipynb
├── train.py
├── train_aux.py
└── utils/
├── __init__.py
├── activations.py
├── add_nms.py
├── autoanchor.py
├── aws/
│ ├── __init__.py
│ ├── mime.sh
│ ├── resume.py
│ └── userdata.sh
├── datasets.py
├── general.py
├── google_app_engine/
│ ├── Dockerfile
│ ├── additional_requirements.txt
│ └── app.yaml
├── google_utils.py
├── loss.py
├── metrics.py
├── plots.py
├── torch_utils.py
└── wandb_logging/
├── __init__.py
├── log_dataset.py
└── wandb_utils.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
# Repo-specific GitIgnore ----------------------------------------------------------------------------------------------
*.jpg
*.jpeg
*.png
*.bmp
*.tif
*.tiff
*.heic
*.JPG
*.JPEG
*.PNG
*.BMP
*.TIF
*.TIFF
*.HEIC
*.mp4
*.mov
*.MOV
*.avi
*.data
*.json
*.cfg
!setup.cfg
!cfg/yolov3*.cfg
storage.googleapis.com
runs/*
data/*
data/images/*
!data/*.yaml
!data/hyps
!data/scripts
!data/images
!data/images/zidane.jpg
!data/images/bus.jpg
!data/*.sh
results*.csv
# Datasets -------------------------------------------------------------------------------------------------------------
coco/
coco128/
VOC/
coco2017labels-segments.zip
test2017.zip
train2017.zip
val2017.zip
# MATLAB GitIgnore -----------------------------------------------------------------------------------------------------
*.m~
*.mat
!targets*.mat
# Neural Network weights -----------------------------------------------------------------------------------------------
*.weights
*.pt
*.pb
*.onnx
*.engine
*.mlmodel
*.torchscript
*.tflite
*.h5
*_saved_model/
*_web_model/
*_openvino_model/
darknet53.conv.74
yolov3-tiny.conv.15
*.ptl
*.trt
# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
env/
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
/wandb/
.installed.cfg
*.egg
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# pyenv
.python-version
# celery beat schedule file
celerybeat-schedule
# SageMath parsed files
*.sage.py
# dotenv
.env
# virtualenv
.venv*
venv*/
ENV*/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------
# General
.DS_Store
.AppleDouble
.LSOverride
# Icon must end with two \r
Icon
Icon?
# Thumbnails
._*
# Files that might appear in the root of a volume
.DocumentRevisions-V100
.fseventsd
.Spotlight-V100
.TemporaryItems
.Trashes
.VolumeIcon.icns
.com.apple.timemachine.donotpresent
# Directories potentially created on remote AFP share
.AppleDB
.AppleDesktop
Network Trash Folder
Temporary Items
.apdisk
# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore
# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
# User-specific stuff:
.idea/*
.idea/**/workspace.xml
.idea/**/tasks.xml
.idea/dictionaries
.html # Bokeh Plots
.pg # TensorFlow Frozen Graphs
.avi # videos
# Sensitive or high-churn files:
.idea/**/dataSources/
.idea/**/dataSources.ids
.idea/**/dataSources.local.xml
.idea/**/sqlDataSources.xml
.idea/**/dynamic.xml
.idea/**/uiDesigner.xml
# Gradle:
.idea/**/gradle.xml
.idea/**/libraries
# CMake
cmake-build-debug/
cmake-build-release/
# Mongo Explorer plugin:
.idea/**/mongoSettings.xml
## File-based project format:
*.iws
## Plugin-specific files:
# IntelliJ
out/
# mpeltonen/sbt-idea plugin
.idea_modules/
# JIRA plugin
atlassian-ide-plugin.xml
# Cursive Clojure plugin
.idea/replstate.xml
# Crashlytics plugin (for Android Studio and IntelliJ)
com_crashlytics_export_strings.xml
crashlytics.properties
crashlytics-build.properties
fabric.properties
================================================
FILE: LICENSE.md
================================================
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The GNU General Public License is a free, copyleft license for
software and other kinds of works.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
the GNU General Public License is intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users. We, the Free Software Foundation, use the
GNU General Public License for most of our software; it applies also to
any other work released this way by its authors. You can apply it to
your programs, too.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
them if you wish), that you receive source code or can get it if you
want it, that you can change the software or use pieces of it in new
free programs, and that you know you can do these things.
To protect your rights, we need to prevent others from denying you
these rights or asking you to surrender the rights. Therefore, you have
certain responsibilities if you distribute copies of the software, or if
you modify it: responsibilities to respect the freedom of others.
For example, if you distribute copies of such a program, whether
gratis or for a fee, you must pass on to the recipients the same
freedoms that you received. You must make sure that they, too, receive
or can get the source code. And you must show them these terms so they
know their rights.
Developers that use the GNU GPL protect your rights with two steps:
(1) assert copyright on the software, and (2) offer you this License
giving you legal permission to copy, distribute and/or modify it.
For the developers' and authors' protection, the GPL clearly explains
that there is no warranty for this free software. For both users' and
authors' sake, the GPL requires that modified versions be marked as
changed, so that their problems will not be attributed erroneously to
authors of previous versions.
Some devices are designed to deny users access to install or run
modified versions of the software inside them, although the manufacturer
can do so. This is fundamentally incompatible with the aim of
protecting users' freedom to change the software. The systematic
pattern of such abuse occurs in the area of products for individuals to
use, which is precisely where it is most unacceptable. Therefore, we
have designed this version of the GPL to prohibit the practice for those
products. If such problems arise substantially in other domains, we
stand ready to extend this provision to those domains in future versions
of the GPL, as needed to protect the freedom of users.
Finally, every program is threatened constantly by software patents.
States should not allow patents to restrict development and use of
software on general-purpose computers, but in those that do, we wish to
avoid the special danger that patents applied to a free program could
make it effectively proprietary. To prevent this, the GPL assures that
patents cannot be used to render the program non-free.
The precise terms and conditions for copying, distribution and
modification follow.
TERMS AND CONDITIONS
0. Definitions.
"This License" refers to version 3 of the GNU General Public License.
"Copyright" also means copyright-like laws that apply to other kinds of
works, such as semiconductor masks.
"The Program" refers to any copyrightable work licensed under this
License. Each licensee is addressed as "you". "Licensees" and
"recipients" may be individuals or organizations.
To "modify" a work means to copy from or adapt all or part of the work
in a fashion requiring copyright permission, other than the making of an
exact copy. The resulting work is called a "modified version" of the
earlier work or a work "based on" the earlier work.
A "covered work" means either the unmodified Program or a work based
on the Program.
To "propagate" a work means to do anything with it that, without
permission, would make you directly or secondarily liable for
infringement under applicable copyright law, except executing it on a
computer or modifying a private copy. Propagation includes copying,
distribution (with or without modification), making available to the
public, and in some countries other activities as well.
To "convey" a work means any kind of propagation that enables other
parties to make or receive copies. Mere interaction with a user through
a computer network, with no transfer of a copy, is not conveying.
An interactive user interface displays "Appropriate Legal Notices"
to the extent that it includes a convenient and prominently visible
feature that (1) displays an appropriate copyright notice, and (2)
tells the user that there is no warranty for the work (except to the
extent that warranties are provided), that licensees may convey the
work under this License, and how to view a copy of this License. If
the interface presents a list of user commands or options, such as a
menu, a prominent item in the list meets this criterion.
1. Source Code.
The "source code" for a work means the preferred form of the work
for making modifications to it. "Object code" means any non-source
form of a work.
A "Standard Interface" means an interface that either is an official
standard defined by a recognized standards body, or, in the case of
interfaces specified for a particular programming language, one that
is widely used among developers working in that language.
The "System Libraries" of an executable work include anything, other
than the work as a whole, that (a) is included in the normal form of
packaging a Major Component, but which is not part of that Major
Component, and (b) serves only to enable use of the work with that
Major Component, or to implement a Standard Interface for which an
implementation is available to the public in source code form. A
"Major Component", in this context, means a major essential component
(kernel, window system, and so on) of the specific operating system
(if any) on which the executable work runs, or a compiler used to
produce the work, or an object code interpreter used to run it.
The "Corresponding Source" for a work in object code form means all
the source code needed to generate, install, and (for an executable
work) run the object code and to modify the work, including scripts to
control those activities. However, it does not include the work's
System Libraries, or general-purpose tools or generally available free
programs which are used unmodified in performing those activities but
which are not part of the work. For example, Corresponding Source
includes interface definition files associated with source files for
the work, and the source code for shared libraries and dynamically
linked subprograms that the work is specifically designed to require,
such as by intimate data communication or control flow between those
subprograms and other parts of the work.
The Corresponding Source need not include anything that users
can regenerate automatically from other parts of the Corresponding
Source.
The Corresponding Source for a work in source code form is that
same work.
2. Basic Permissions.
All rights granted under this License are granted for the term of
copyright on the Program, and are irrevocable provided the stated
conditions are met. This License explicitly affirms your unlimited
permission to run the unmodified Program. The output from running a
covered work is covered by this License only if the output, given its
content, constitutes a covered work. This License acknowledges your
rights of fair use or other equivalent, as provided by copyright law.
You may make, run and propagate covered works that you do not
convey, without conditions so long as your license otherwise remains
in force. You may convey covered works to others for the sole purpose
of having them make modifications exclusively for you, or provide you
with facilities for running those works, provided that you comply with
the terms of this License in conveying all material for which you do
not control copyright. Those thus making or running the covered works
for you must do so exclusively on your behalf, under your direction
and control, on terms that prohibit them from making any copies of
your copyrighted material outside their relationship with you.
Conveying under any other circumstances is permitted solely under
the conditions stated below. Sublicensing is not allowed; section 10
makes it unnecessary.
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
No covered work shall be deemed part of an effective technological
measure under any applicable law fulfilling obligations under article
11 of the WIPO copyright treaty adopted on 20 December 1996, or
similar laws prohibiting or restricting circumvention of such
measures.
When you convey a covered work, you waive any legal power to forbid
circumvention of technological measures to the extent such circumvention
is effected by exercising rights under this License with respect to
the covered work, and you disclaim any intention to limit operation or
modification of the work as a means of enforcing, against the work's
users, your or third parties' legal rights to forbid circumvention of
technological measures.
4. Conveying Verbatim Copies.
You may convey verbatim copies of the Program's source code as you
receive it, in any medium, provided that you conspicuously and
appropriately publish on each copy an appropriate copyright notice;
keep intact all notices stating that this License and any
non-permissive terms added in accord with section 7 apply to the code;
keep intact all notices of the absence of any warranty; and give all
recipients a copy of this License along with the Program.
You may charge any price or no price for each copy that you convey,
and you may offer support or warranty protection for a fee.
5. Conveying Modified Source Versions.
You may convey a work based on the Program, or the modifications to
produce it from the Program, in the form of source code under the
terms of section 4, provided that you also meet all of these conditions:
a) The work must carry prominent notices stating that you modified
it, and giving a relevant date.
b) The work must carry prominent notices stating that it is
released under this License and any conditions added under section
7. This requirement modifies the requirement in section 4 to
"keep intact all notices".
c) You must license the entire work, as a whole, under this
License to anyone who comes into possession of a copy. This
License will therefore apply, along with any applicable section 7
additional terms, to the whole of the work, and all its parts,
regardless of how they are packaged. This License gives no
permission to license the work in any other way, but it does not
invalidate such permission if you have separately received it.
d) If the work has interactive user interfaces, each must display
Appropriate Legal Notices; however, if the Program has interactive
interfaces that do not display Appropriate Legal Notices, your
work need not make them do so.
A compilation of a covered work with other separate and independent
works, which are not by their nature extensions of the covered work,
and which are not combined with it such as to form a larger program,
in or on a volume of a storage or distribution medium, is called an
"aggregate" if the compilation and its resulting copyright are not
used to limit the access or legal rights of the compilation's users
beyond what the individual works permit. Inclusion of a covered work
in an aggregate does not cause this License to apply to the other
parts of the aggregate.
6. Conveying Non-Source Forms.
You may convey a covered work in object code form under the terms
of sections 4 and 5, provided that you also convey the
machine-readable Corresponding Source under the terms of this License,
in one of these ways:
a) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by the
Corresponding Source fixed on a durable physical medium
customarily used for software interchange.
b) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by a
written offer, valid for at least three years and valid for as
long as you offer spare parts or customer support for that product
model, to give anyone who possesses the object code either (1) a
copy of the Corresponding Source for all the software in the
product that is covered by this License, on a durable physical
medium customarily used for software interchange, for a price no
more than your reasonable cost of physically performing this
conveying of source, or (2) access to copy the
Corresponding Source from a network server at no charge.
c) Convey individual copies of the object code with a copy of the
written offer to provide the Corresponding Source. This
alternative is allowed only occasionally and noncommercially, and
only if you received the object code with such an offer, in accord
with subsection 6b.
d) Convey the object code by offering access from a designated
place (gratis or for a charge), and offer equivalent access to the
Corresponding Source in the same way through the same place at no
further charge. You need not require recipients to copy the
Corresponding Source along with the object code. If the place to
copy the object code is a network server, the Corresponding Source
may be on a different server (operated by you or a third party)
that supports equivalent copying facilities, provided you maintain
clear directions next to the object code saying where to find the
Corresponding Source. Regardless of what server hosts the
Corresponding Source, you remain obligated to ensure that it is
available for as long as needed to satisfy these requirements.
e) Convey the object code using peer-to-peer transmission, provided
you inform other peers where the object code and Corresponding
Source of the work are being offered to the general public at no
charge under subsection 6d.
A separable portion of the object code, whose source code is excluded
from the Corresponding Source as a System Library, need not be
included in conveying the object code work.
A "User Product" is either (1) a "consumer product", which means any
tangible personal property which is normally used for personal, family,
or household purposes, or (2) anything designed or sold for incorporation
into a dwelling. In determining whether a product is a consumer product,
doubtful cases shall be resolved in favor of coverage. For a particular
product received by a particular user, "normally used" refers to a
typical or common use of that class of product, regardless of the status
of the particular user or of the way in which the particular user
actually uses, or expects or is expected to use, the product. A product
is a consumer product regardless of whether the product has substantial
commercial, industrial or non-consumer uses, unless such uses represent
the only significant mode of use of the product.
"Installation Information" for a User Product means any methods,
procedures, authorization keys, or other information required to install
and execute modified versions of a covered work in that User Product from
a modified version of its Corresponding Source. The information must
suffice to ensure that the continued functioning of the modified object
code is in no case prevented or interfered with solely because
modification has been made.
If you convey an object code work under this section in, or with, or
specifically for use in, a User Product, and the conveying occurs as
part of a transaction in which the right of possession and use of the
User Product is transferred to the recipient in perpetuity or for a
fixed term (regardless of how the transaction is characterized), the
Corresponding Source conveyed under this section must be accompanied
by the Installation Information. But this requirement does not apply
if neither you nor any third party retains the ability to install
modified object code on the User Product (for example, the work has
been installed in ROM).
The requirement to provide Installation Information does not include a
requirement to continue to provide support service, warranty, or updates
for a work that has been modified or installed by the recipient, or for
the User Product in which it has been modified or installed. Access to a
network may be denied when the modification itself materially and
adversely affects the operation of the network or violates the rules and
protocols for communication across the network.
Corresponding Source conveyed, and Installation Information provided,
in accord with this section must be in a format that is publicly
documented (and with an implementation available to the public in
source code form), and must require no special password or key for
unpacking, reading or copying.
7. Additional Terms.
"Additional permissions" are terms that supplement the terms of this
License by making exceptions from one or more of its conditions.
Additional permissions that are applicable to the entire Program shall
be treated as though they were included in this License, to the extent
that they are valid under applicable law. If additional permissions
apply only to part of the Program, that part may be used separately
under those permissions, but the entire Program remains governed by
this License without regard to the additional permissions.
When you convey a copy of a covered work, you may at your option
remove any additional permissions from that copy, or from any part of
it. (Additional permissions may be written to require their own
removal in certain cases when you modify the work.) You may place
additional permissions on material, added by you to a covered work,
for which you have or can give appropriate copyright permission.
Notwithstanding any other provision of this License, for material you
add to a covered work, you may (if authorized by the copyright holders of
that material) supplement the terms of this License with terms:
a) Disclaiming warranty or limiting liability differently from the
terms of sections 15 and 16 of this License; or
b) Requiring preservation of specified reasonable legal notices or
author attributions in that material or in the Appropriate Legal
Notices displayed by works containing it; or
c) Prohibiting misrepresentation of the origin of that material, or
requiring that modified versions of such material be marked in
reasonable ways as different from the original version; or
d) Limiting the use for publicity purposes of names of licensors or
authors of the material; or
e) Declining to grant rights under trademark law for use of some
trade names, trademarks, or service marks; or
f) Requiring indemnification of licensors and authors of that
material by anyone who conveys the material (or modified versions of
it) with contractual assumptions of liability to the recipient, for
any liability that these contractual assumptions directly impose on
those licensors and authors.
All other non-permissive additional terms are considered "further
restrictions" within the meaning of section 10. If the Program as you
received it, or any part of it, contains a notice stating that it is
governed by this License along with a term that is a further
restriction, you may remove that term. If a license document contains
a further restriction but permits relicensing or conveying under this
License, you may add to a covered work material governed by the terms
of that license document, provided that the further restriction does
not survive such relicensing or conveying.
If you add terms to a covered work in accord with this section, you
must place, in the relevant source files, a statement of the
additional terms that apply to those files, or a notice indicating
where to find the applicable terms.
Additional terms, permissive or non-permissive, may be stated in the
form of a separately written license, or stated as exceptions;
the above requirements apply either way.
8. Termination.
You may not propagate or modify a covered work except as expressly
provided under this License. Any attempt otherwise to propagate or
modify it is void, and will automatically terminate your rights under
this License (including any patent licenses granted under the third
paragraph of section 11).
However, if you cease all violation of this License, then your
license from a particular copyright holder is reinstated (a)
provisionally, unless and until the copyright holder explicitly and
finally terminates your license, and (b) permanently, if the copyright
holder fails to notify you of the violation by some reasonable means
prior to 60 days after the cessation.
Moreover, your license from a particular copyright holder is
reinstated permanently if the copyright holder notifies you of the
violation by some reasonable means, this is the first time you have
received notice of violation of this License (for any work) from that
copyright holder, and you cure the violation prior to 30 days after
your receipt of the notice.
Termination of your rights under this section does not terminate the
licenses of parties who have received copies or rights from you under
this License. If your rights have been terminated and not permanently
reinstated, you do not qualify to receive new licenses for the same
material under section 10.
9. Acceptance Not Required for Having Copies.
You are not required to accept this License in order to receive or
run a copy of the Program. Ancillary propagation of a covered work
occurring solely as a consequence of using peer-to-peer transmission
to receive a copy likewise does not require acceptance. However,
nothing other than this License grants you permission to propagate or
modify any covered work. These actions infringe copyright if you do
not accept this License. Therefore, by modifying or propagating a
covered work, you indicate your acceptance of this License to do so.
10. Automatic Licensing of Downstream Recipients.
Each time you convey a covered work, the recipient automatically
receives a license from the original licensors, to run, modify and
propagate that work, subject to this License. You are not responsible
for enforcing compliance by third parties with this License.
An "entity transaction" is a transaction transferring control of an
organization, or substantially all assets of one, or subdividing an
organization, or merging organizations. If propagation of a covered
work results from an entity transaction, each party to that
transaction who receives a copy of the work also receives whatever
licenses to the work the party's predecessor in interest had or could
give under the previous paragraph, plus a right to possession of the
Corresponding Source of the work from the predecessor in interest, if
the predecessor has it or can get it with reasonable efforts.
You may not impose any further restrictions on the exercise of the
rights granted or affirmed under this License. For example, you may
not impose a license fee, royalty, or other charge for exercise of
rights granted under this License, and you may not initiate litigation
(including a cross-claim or counterclaim in a lawsuit) alleging that
any patent claim is infringed by making, using, selling, offering for
sale, or importing the Program or any portion of it.
11. Patents.
A "contributor" is a copyright holder who authorizes use under this
License of the Program or a work on which the Program is based. The
work thus licensed is called the contributor's "contributor version".
A contributor's "essential patent claims" are all patent claims
owned or controlled by the contributor, whether already acquired or
hereafter acquired, that would be infringed by some manner, permitted
by this License, of making, using, or selling its contributor version,
but do not include claims that would be infringed only as a
consequence of further modification of the contributor version. For
purposes of this definition, "control" includes the right to grant
patent sublicenses in a manner consistent with the requirements of
this License.
Each contributor grants you a non-exclusive, worldwide, royalty-free
patent license under the contributor's essential patent claims, to
make, use, sell, offer for sale, import and otherwise run, modify and
propagate the contents of its contributor version.
In the following three paragraphs, a "patent license" is any express
agreement or commitment, however denominated, not to enforce a patent
(such as an express permission to practice a patent or covenant not to
sue for patent infringement). To "grant" such a patent license to a
party means to make such an agreement or commitment not to enforce a
patent against the party.
If you convey a covered work, knowingly relying on a patent license,
and the Corresponding Source of the work is not available for anyone
to copy, free of charge and under the terms of this License, through a
publicly available network server or other readily accessible means,
then you must either (1) cause the Corresponding Source to be so
available, or (2) arrange to deprive yourself of the benefit of the
patent license for this particular work, or (3) arrange, in a manner
consistent with the requirements of this License, to extend the patent
license to downstream recipients. "Knowingly relying" means you have
actual knowledge that, but for the patent license, your conveying the
covered work in a country, or your recipient's use of the covered work
in a country, would infringe one or more identifiable patents in that
country that you have reason to believe are valid.
If, pursuant to or in connection with a single transaction or
arrangement, you convey, or propagate by procuring conveyance of, a
covered work, and grant a patent license to some of the parties
receiving the covered work authorizing them to use, propagate, modify
or convey a specific copy of the covered work, then the patent license
you grant is automatically extended to all recipients of the covered
work and works based on it.
A patent license is "discriminatory" if it does not include within
the scope of its coverage, prohibits the exercise of, or is
conditioned on the non-exercise of one or more of the rights that are
specifically granted under this License. You may not convey a covered
work if you are a party to an arrangement with a third party that is
in the business of distributing software, under which you make payment
to the third party based on the extent of your activity of conveying
the work, and under which the third party grants, to any of the
parties who would receive the covered work from you, a discriminatory
patent license (a) in connection with copies of the covered work
conveyed by you (or copies made from those copies), or (b) primarily
for and in connection with specific products or compilations that
contain the covered work, unless you entered into that arrangement,
or that patent license was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting
any implied license or other defenses to infringement that may
otherwise be available to you under applicable patent law.
12. No Surrender of Others' Freedom.
If conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
excuse you from the conditions of this License. If you cannot convey a
covered work so as to satisfy simultaneously your obligations under this
License and any other pertinent obligations, then as a consequence you may
not convey it at all. For example, if you agree to terms that obligate you
to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU Affero General Public License into a single
combined work, and to convey the resulting work. The terms of this
License will continue to apply to the part which is the covered work,
but the special requirements of the GNU Affero General Public License,
section 13, concerning interaction through a network will apply to the
combination as such.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
the GNU General Public License from time to time. Such new versions will
be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
Later license versions may give you additional or different
permissions. However, no additional obligations are imposed on any
author or copyright holder as a result of your choosing to follow a
later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.
================================================
FILE: README.md
================================================
# Official YOLOv7
Implementation of paper - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696)
[](https://paperswithcode.com/sota/real-time-object-detection-on-coco?p=yolov7-trainable-bag-of-freebies-sets-new)
[](https://huggingface.co/spaces/akhaliq/yolov7)
<a href="https://colab.research.google.com/gist/AlexeyAB/b769f5795e65fdab80086f6cb7940dae/yolov7detection.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
[](https://arxiv.org/abs/2207.02696)
<div align="center">
<a href="./">
<img src="./figure/performance.png" width="79%"/>
</a>
</div>
## Web Demo
- Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces/akhaliq/yolov7) using Gradio. Try out the Web Demo [](https://huggingface.co/spaces/akhaliq/yolov7)
## Performance
MS COCO
| Model | Test Size | AP<sup>test</sup> | AP<sub>50</sub><sup>test</sup> | AP<sub>75</sub><sup>test</sup> | batch 1 fps | batch 32 average time |
| :-- | :-: | :-: | :-: | :-: | :-: | :-: |
| [**YOLOv7**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) | 640 | **51.4%** | **69.7%** | **55.9%** | 161 *fps* | 2.8 *ms* |
| [**YOLOv7-X**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) | 640 | **53.1%** | **71.2%** | **57.8%** | 114 *fps* | 4.3 *ms* |
| | | | | | | |
| [**YOLOv7-W6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) | 1280 | **54.9%** | **72.6%** | **60.1%** | 84 *fps* | 7.6 *ms* |
| [**YOLOv7-E6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) | 1280 | **56.0%** | **73.5%** | **61.2%** | 56 *fps* | 12.3 *ms* |
| [**YOLOv7-D6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) | 1280 | **56.6%** | **74.0%** | **61.8%** | 44 *fps* | 15.0 *ms* |
| [**YOLOv7-E6E**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt) | 1280 | **56.8%** | **74.4%** | **62.1%** | 36 *fps* | 18.7 *ms* |
## Installation
Docker environment (recommended)
<details><summary> <b>Expand</b> </summary>
``` shell
# create the docker container, you can change the share memory size if you have more.
nvidia-docker run --name yolov7 -it -v your_coco_path/:/coco/ -v your_code_path/:/yolov7 --shm-size=64g nvcr.io/nvidia/pytorch:21.08-py3
# apt install required packages
apt update
apt install -y zip htop screen libgl1-mesa-glx
# pip install required packages
pip install seaborn thop
# go to code folder
cd /yolov7
```
</details>
## Testing
[`yolov7.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) [`yolov7x.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) [`yolov7-w6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) [`yolov7-e6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) [`yolov7-d6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) [`yolov7-e6e.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt)
``` shell
python test.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.65 --device 0 --weights yolov7.pt --name yolov7_640_val
```
You will get the results:
```
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.51206
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.69730
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.55521
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.35247
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.55937
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66693
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.38453
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.63765
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.68772
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.53766
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.73549
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83868
```
To measure accuracy, download [COCO-annotations for Pycocotools](http://images.cocodataset.org/annotations/annotations_trainval2017.zip) to the `./coco/annotations/instances_val2017.json`
## Training
Data preparation
``` shell
bash scripts/get_coco.sh
```
* Download MS COCO dataset images ([train](http://images.cocodataset.org/zips/train2017.zip), [val](http://images.cocodataset.org/zips/val2017.zip), [test](http://images.cocodataset.org/zips/test2017.zip)) and [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip). If you have previously used a different version of YOLO, we strongly recommend that you delete `train2017.cache` and `val2017.cache` files, and redownload [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip)
Single GPU training
``` shell
# train p5 models
python train.py --workers 8 --device 0 --batch-size 32 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml
# train p6 models
python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights '' --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml
```
Multiple GPU training
``` shell
# train p5 models
python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch-size 128 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml
# train p6 models
python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_aux.py --workers 8 --device 0,1,2,3,4,5,6,7 --sync-bn --batch-size 128 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights '' --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml
```
## Transfer learning
[`yolov7_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7_training.pt) [`yolov7x_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x_training.pt) [`yolov7-w6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6_training.pt) [`yolov7-e6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6_training.pt) [`yolov7-d6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6_training.pt) [`yolov7-e6e_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e_training.pt)
Single GPU finetuning for custom dataset
``` shell
# finetune p5 models
python train.py --workers 8 --device 0 --batch-size 32 --data data/custom.yaml --img 640 640 --cfg cfg/training/yolov7-custom.yaml --weights 'yolov7_training.pt' --name yolov7-custom --hyp data/hyp.scratch.custom.yaml
# finetune p6 models
python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/custom.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6-custom.yaml --weights 'yolov7-w6_training.pt' --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml
```
## Re-parameterization
See [reparameterization.ipynb](tools/reparameterization.ipynb)
## Inference
On video:
``` shell
python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source yourvideo.mp4
```
On image:
``` shell
python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg
```
<div align="center">
<a href="./">
<img src="./figure/horses_prediction.jpg" width="59%"/>
</a>
</div>
## Export
**Pytorch to CoreML (and inference on MacOS/iOS)** <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7CoreML.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
**Pytorch to ONNX with NMS (and inference)** <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7onnx.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
```shell
python export.py --weights yolov7-tiny.pt --grid --end2end --simplify \
--topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640
```
**Pytorch to TensorRT with NMS (and inference)** <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7trt.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
```shell
wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt
python export.py --weights ./yolov7-tiny.pt --grid --end2end --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640
git clone https://github.com/Linaom1214/tensorrt-python.git
python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16
```
**Pytorch to TensorRT another way** <a href="https://colab.research.google.com/gist/AlexeyAB/fcb47ae544cf284eb24d8ad8e880d45c/yolov7trtlinaom.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <details><summary> <b>Expand</b> </summary>
```shell
wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt
python export.py --weights yolov7-tiny.pt --grid --include-nms
git clone https://github.com/Linaom1214/tensorrt-python.git
python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16
# Or use trtexec to convert ONNX to TensorRT engine
/usr/src/tensorrt/bin/trtexec --onnx=yolov7-tiny.onnx --saveEngine=yolov7-tiny-nms.trt --fp16
```
</details>
Tested with: Python 3.7.13, Pytorch 1.12.0+cu113
## Pose estimation
[`code`](https://github.com/WongKinYiu/yolov7/tree/pose) [`yolov7-w6-pose.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6-pose.pt)
See [keypoint.ipynb](https://github.com/WongKinYiu/yolov7/blob/main/tools/keypoint.ipynb).
<div align="center">
<a href="./">
<img src="./figure/pose.png" width="39%"/>
</a>
</div>
## Instance segmentation (with NTU)
[`code`](https://github.com/WongKinYiu/yolov7/tree/mask) [`yolov7-mask.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-mask.pt)
See [instance.ipynb](https://github.com/WongKinYiu/yolov7/blob/main/tools/instance.ipynb).
<div align="center">
<a href="./">
<img src="./figure/mask.png" width="59%"/>
</a>
</div>
## Instance segmentation
[`code`](https://github.com/WongKinYiu/yolov7/tree/u7/seg) [`yolov7-seg.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-seg.pt)
YOLOv7 for instance segmentation (YOLOR + YOLOv5 + YOLACT)
| Model | Test Size | AP<sup>box</sup> | AP<sub>50</sub><sup>box</sup> | AP<sub>75</sub><sup>box</sup> | AP<sup>mask</sup> | AP<sub>50</sub><sup>mask</sup> | AP<sub>75</sub><sup>mask</sup> |
| :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
| **YOLOv7-seg** | 640 | **51.4%** | **69.4%** | **55.8%** | **41.5%** | **65.5%** | **43.7%** |
## Anchor free detection head
[`code`](https://github.com/WongKinYiu/yolov7/tree/u6) [`yolov7-u6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-u6.pt)
YOLOv7 with decoupled TAL head (YOLOR + YOLOv5 + YOLOv6)
| Model | Test Size | AP<sup>val</sup> | AP<sub>50</sub><sup>val</sup> | AP<sub>75</sub><sup>val</sup> |
| :-- | :-: | :-: | :-: | :-: |
| **YOLOv7-u6** | 640 | **52.6%** | **69.7%** | **57.3%** |
## Citation
```
@inproceedings{wang2023yolov7,
title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2023}
}
```
```
@article{wang2023designing,
title={Designing Network Design Strategies Through Gradient Path Analysis},
author={Wang, Chien-Yao and Liao, Hong-Yuan Mark and Yeh, I-Hau},
journal={Journal of Information Science and Engineering},
year={2023}
}
```
## Teaser
YOLOv7-semantic & YOLOv7-panoptic & YOLOv7-caption
<div align="center">
<a href="./">
<img src="./figure/tennis.jpg" width="24%"/>
</a>
<a href="./">
<img src="./figure/tennis_semantic.jpg" width="24%"/>
</a>
<a href="./">
<img src="./figure/tennis_panoptic.png" width="24%"/>
</a>
<a href="./">
<img src="./figure/tennis_caption.png" width="24%"/>
</a>
</div>
YOLOv7-semantic & YOLOv7-detection & YOLOv7-depth (with NTUT)
<div align="center">
<a href="./">
<img src="./figure/yolov7_city.jpg" width="80%"/>
</a>
</div>
YOLOv7-3d-detection & YOLOv7-lidar & YOLOv7-road (with NTUT)
<div align="center">
<a href="./">
<img src="./figure/yolov7_3d.jpg" width="30%"/>
</a>
<a href="./">
<img src="./figure/yolov7_lidar.jpg" width="30%"/>
</a>
<a href="./">
<img src="./figure/yolov7_road.jpg" width="30%"/>
</a>
</div>
## Acknowledgements
<details><summary> <b>Expand</b> </summary>
* [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet)
* [https://github.com/WongKinYiu/yolor](https://github.com/WongKinYiu/yolor)
* [https://github.com/WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4)
* [https://github.com/WongKinYiu/ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)
* [https://github.com/Megvii-BaseDetection/YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)
* [https://github.com/ultralytics/yolov3](https://github.com/ultralytics/yolov3)
* [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)
* [https://github.com/DingXiaoH/RepVGG](https://github.com/DingXiaoH/RepVGG)
* [https://github.com/JUGGHM/OREPA_CVPR2022](https://github.com/JUGGHM/OREPA_CVPR2022)
* [https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose](https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose)
</details>
================================================
FILE: cfg/baseline/r50-csp.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [12,16, 19,36, 40,28] # P3/8
- [36,75, 76,55, 72,146] # P4/16
- [142,110, 192,243, 459,401] # P5/32
# CSP-ResNet backbone
backbone:
# [from, number, module, args]
[[-1, 1, Stem, [128]], # 0-P1/2
[-1, 3, ResCSPC, [128]],
[-1, 1, Conv, [256, 3, 2]], # 2-P3/8
[-1, 4, ResCSPC, [256]],
[-1, 1, Conv, [512, 3, 2]], # 4-P3/8
[-1, 6, ResCSPC, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 6-P3/8
[-1, 3, ResCSPC, [1024]], # 7
]
# CSP-Res-PAN head
head:
[[-1, 1, SPPCSPC, [512]], # 8
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[5, 1, Conv, [256, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 2, ResCSPB, [256]], # 13
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[3, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 2, ResCSPB, [128]], # 18
[-1, 1, Conv, [256, 3, 1]],
[-2, 1, Conv, [256, 3, 2]],
[[-1, 13], 1, Concat, [1]], # cat
[-1, 2, ResCSPB, [256]], # 22
[-1, 1, Conv, [512, 3, 1]],
[-2, 1, Conv, [512, 3, 2]],
[[-1, 8], 1, Concat, [1]], # cat
[-1, 2, ResCSPB, [512]], # 26
[-1, 1, Conv, [1024, 3, 1]],
[[19,23,27], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5)
]
================================================
FILE: cfg/baseline/x50-csp.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [12,16, 19,36, 40,28] # P3/8
- [36,75, 76,55, 72,146] # P4/16
- [142,110, 192,243, 459,401] # P5/32
# CSP-ResNeXt backbone
backbone:
# [from, number, module, args]
[[-1, 1, Stem, [128]], # 0-P1/2
[-1, 3, ResXCSPC, [128]],
[-1, 1, Conv, [256, 3, 2]], # 2-P3/8
[-1, 4, ResXCSPC, [256]],
[-1, 1, Conv, [512, 3, 2]], # 4-P3/8
[-1, 6, ResXCSPC, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 6-P3/8
[-1, 3, ResXCSPC, [1024]], # 7
]
# CSP-ResX-PAN head
head:
[[-1, 1, SPPCSPC, [512]], # 8
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[5, 1, Conv, [256, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 2, ResXCSPB, [256]], # 13
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[3, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 2, ResXCSPB, [128]], # 18
[-1, 1, Conv, [256, 3, 1]],
[-2, 1, Conv, [256, 3, 2]],
[[-1, 13], 1, Concat, [1]], # cat
[-1, 2, ResXCSPB, [256]], # 22
[-1, 1, Conv, [512, 3, 1]],
[-2, 1, Conv, [512, 3, 2]],
[[-1, 8], 1, Concat, [1]], # cat
[-1, 2, ResXCSPB, [512]], # 26
[-1, 1, Conv, [1024, 3, 1]],
[[19,23,27], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5)
]
================================================
FILE: cfg/baseline/yolor-csp-x.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.33 # model depth multiple
width_multiple: 1.25 # layer channel multiple
# anchors
anchors:
- [12,16, 19,36, 40,28] # P3/8
- [36,75, 76,55, 72,146] # P4/16
- [142,110, 192,243, 459,401] # P5/32
# CSP-Darknet backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
[-1, 1, Bottleneck, [64]],
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
[-1, 2, BottleneckCSPC, [128]],
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
[-1, 8, BottleneckCSPC, [256]],
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
[-1, 8, BottleneckCSPC, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
[-1, 4, BottleneckCSPC, [1024]], # 10
]
# CSP-Dark-PAN head
head:
[[-1, 1, SPPCSPC, [512]], # 11
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[8, 1, Conv, [256, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 2, BottleneckCSPB, [256]], # 16
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[6, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 2, BottleneckCSPB, [128]], # 21
[-1, 1, Conv, [256, 3, 1]],
[-2, 1, Conv, [256, 3, 2]],
[[-1, 16], 1, Concat, [1]], # cat
[-1, 2, BottleneckCSPB, [256]], # 25
[-1, 1, Conv, [512, 3, 1]],
[-2, 1, Conv, [512, 3, 2]],
[[-1, 11], 1, Concat, [1]], # cat
[-1, 2, BottleneckCSPB, [512]], # 29
[-1, 1, Conv, [1024, 3, 1]],
[[22,26,30], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5)
]
================================================
FILE: cfg/baseline/yolor-csp.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [12,16, 19,36, 40,28] # P3/8
- [36,75, 76,55, 72,146] # P4/16
- [142,110, 192,243, 459,401] # P5/32
# CSP-Darknet backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
[-1, 1, Bottleneck, [64]],
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
[-1, 2, BottleneckCSPC, [128]],
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
[-1, 8, BottleneckCSPC, [256]],
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
[-1, 8, BottleneckCSPC, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
[-1, 4, BottleneckCSPC, [1024]], # 10
]
# CSP-Dark-PAN head
head:
[[-1, 1, SPPCSPC, [512]], # 11
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[8, 1, Conv, [256, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 2, BottleneckCSPB, [256]], # 16
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[6, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 2, BottleneckCSPB, [128]], # 21
[-1, 1, Conv, [256, 3, 1]],
[-2, 1, Conv, [256, 3, 2]],
[[-1, 16], 1, Concat, [1]], # cat
[-1, 2, BottleneckCSPB, [256]], # 25
[-1, 1, Conv, [512, 3, 1]],
[-2, 1, Conv, [512, 3, 2]],
[[-1, 11], 1, Concat, [1]], # cat
[-1, 2, BottleneckCSPB, [512]], # 29
[-1, 1, Conv, [1024, 3, 1]],
[[22,26,30], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5)
]
================================================
FILE: cfg/baseline/yolor-d6.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # expand model depth
width_multiple: 1.25 # expand layer channels
# anchors
anchors:
- [ 19,27, 44,40, 38,94 ] # P3/8
- [ 96,68, 86,152, 180,137 ] # P4/16
- [ 140,301, 303,264, 238,542 ] # P5/32
- [ 436,615, 739,380, 925,792 ] # P6/64
# CSP-Darknet backbone
backbone:
# [from, number, module, args]
[[-1, 1, ReOrg, []], # 0
[-1, 1, Conv, [64, 3, 1]], # 1-P1/2
[-1, 1, DownC, [128]], # 2-P2/4
[-1, 3, BottleneckCSPA, [128]],
[-1, 1, DownC, [256]], # 4-P3/8
[-1, 15, BottleneckCSPA, [256]],
[-1, 1, DownC, [512]], # 6-P4/16
[-1, 15, BottleneckCSPA, [512]],
[-1, 1, DownC, [768]], # 8-P5/32
[-1, 7, BottleneckCSPA, [768]],
[-1, 1, DownC, [1024]], # 10-P6/64
[-1, 7, BottleneckCSPA, [1024]], # 11
]
# CSP-Dark-PAN head
head:
[[-1, 1, SPPCSPC, [512]], # 12
[-1, 1, Conv, [384, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[-6, 1, Conv, [384, 1, 1]], # route backbone P5
[[-1, -2], 1, Concat, [1]],
[-1, 3, BottleneckCSPB, [384]], # 17
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[-13, 1, Conv, [256, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 3, BottleneckCSPB, [256]], # 22
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[-20, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 3, BottleneckCSPB, [128]], # 27
[-1, 1, Conv, [256, 3, 1]],
[-2, 1, DownC, [256]],
[[-1, 22], 1, Concat, [1]], # cat
[-1, 3, BottleneckCSPB, [256]], # 31
[-1, 1, Conv, [512, 3, 1]],
[-2, 1, DownC, [384]],
[[-1, 17], 1, Concat, [1]], # cat
[-1, 3, BottleneckCSPB, [384]], # 35
[-1, 1, Conv, [768, 3, 1]],
[-2, 1, DownC, [512]],
[[-1, 12], 1, Concat, [1]], # cat
[-1, 3, BottleneckCSPB, [512]], # 39
[-1, 1, Conv, [1024, 3, 1]],
[[28,32,36,40], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5, P6)
]
================================================
FILE: cfg/baseline/yolor-e6.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # expand model depth
width_multiple: 1.25 # expand layer channels
# anchors
anchors:
- [ 19,27, 44,40, 38,94 ] # P3/8
- [ 96,68, 86,152, 180,137 ] # P4/16
- [ 140,301, 303,264, 238,542 ] # P5/32
- [ 436,615, 739,380, 925,792 ] # P6/64
# CSP-Darknet backbone
backbone:
# [from, number, module, args]
[[-1, 1, ReOrg, []], # 0
[-1, 1, Conv, [64, 3, 1]], # 1-P1/2
[-1, 1, DownC, [128]], # 2-P2/4
[-1, 3, BottleneckCSPA, [128]],
[-1, 1, DownC, [256]], # 4-P3/8
[-1, 7, BottleneckCSPA, [256]],
[-1, 1, DownC, [512]], # 6-P4/16
[-1, 7, BottleneckCSPA, [512]],
[-1, 1, DownC, [768]], # 8-P5/32
[-1, 3, BottleneckCSPA, [768]],
[-1, 1, DownC, [1024]], # 10-P6/64
[-1, 3, BottleneckCSPA, [1024]], # 11
]
# CSP-Dark-PAN head
head:
[[-1, 1, SPPCSPC, [512]], # 12
[-1, 1, Conv, [384, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[-6, 1, Conv, [384, 1, 1]], # route backbone P5
[[-1, -2], 1, Concat, [1]],
[-1, 3, BottleneckCSPB, [384]], # 17
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[-13, 1, Conv, [256, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 3, BottleneckCSPB, [256]], # 22
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[-20, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 3, BottleneckCSPB, [128]], # 27
[-1, 1, Conv, [256, 3, 1]],
[-2, 1, DownC, [256]],
[[-1, 22], 1, Concat, [1]], # cat
[-1, 3, BottleneckCSPB, [256]], # 31
[-1, 1, Conv, [512, 3, 1]],
[-2, 1, DownC, [384]],
[[-1, 17], 1, Concat, [1]], # cat
[-1, 3, BottleneckCSPB, [384]], # 35
[-1, 1, Conv, [768, 3, 1]],
[-2, 1, DownC, [512]],
[[-1, 12], 1, Concat, [1]], # cat
[-1, 3, BottleneckCSPB, [512]], # 39
[-1, 1, Conv, [1024, 3, 1]],
[[28,32,36,40], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5, P6)
]
================================================
FILE: cfg/baseline/yolor-p6.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # expand model depth
width_multiple: 1.0 # expand layer channels
# anchors
anchors:
- [ 19,27, 44,40, 38,94 ] # P3/8
- [ 96,68, 86,152, 180,137 ] # P4/16
- [ 140,301, 303,264, 238,542 ] # P5/32
- [ 436,615, 739,380, 925,792 ] # P6/64
# CSP-Darknet backbone
backbone:
# [from, number, module, args]
[[-1, 1, ReOrg, []], # 0
[-1, 1, Conv, [64, 3, 1]], # 1-P1/2
[-1, 1, Conv, [128, 3, 2]], # 2-P2/4
[-1, 3, BottleneckCSPA, [128]],
[-1, 1, Conv, [256, 3, 2]], # 4-P3/8
[-1, 7, BottleneckCSPA, [256]],
[-1, 1, Conv, [384, 3, 2]], # 6-P4/16
[-1, 7, BottleneckCSPA, [384]],
[-1, 1, Conv, [512, 3, 2]], # 8-P5/32
[-1, 3, BottleneckCSPA, [512]],
[-1, 1, Conv, [640, 3, 2]], # 10-P6/64
[-1, 3, BottleneckCSPA, [640]], # 11
]
# CSP-Dark-PAN head
head:
[[-1, 1, SPPCSPC, [320]], # 12
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[-6, 1, Conv, [256, 1, 1]], # route backbone P5
[[-1, -2], 1, Concat, [1]],
[-1, 3, BottleneckCSPB, [256]], # 17
[-1, 1, Conv, [192, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[-13, 1, Conv, [192, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 3, BottleneckCSPB, [192]], # 22
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[-20, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 3, BottleneckCSPB, [128]], # 27
[-1, 1, Conv, [256, 3, 1]],
[-2, 1, Conv, [192, 3, 2]],
[[-1, 22], 1, Concat, [1]], # cat
[-1, 3, BottleneckCSPB, [192]], # 31
[-1, 1, Conv, [384, 3, 1]],
[-2, 1, Conv, [256, 3, 2]],
[[-1, 17], 1, Concat, [1]], # cat
[-1, 3, BottleneckCSPB, [256]], # 35
[-1, 1, Conv, [512, 3, 1]],
[-2, 1, Conv, [320, 3, 2]],
[[-1, 12], 1, Concat, [1]], # cat
[-1, 3, BottleneckCSPB, [320]], # 39
[-1, 1, Conv, [640, 3, 1]],
[[28,32,36,40], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5, P6)
]
================================================
FILE: cfg/baseline/yolor-w6.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # expand model depth
width_multiple: 1.0 # expand layer channels
# anchors
anchors:
- [ 19,27, 44,40, 38,94 ] # P3/8
- [ 96,68, 86,152, 180,137 ] # P4/16
- [ 140,301, 303,264, 238,542 ] # P5/32
- [ 436,615, 739,380, 925,792 ] # P6/64
# CSP-Darknet backbone
backbone:
# [from, number, module, args]
[[-1, 1, ReOrg, []], # 0
[-1, 1, Conv, [64, 3, 1]], # 1-P1/2
[-1, 1, Conv, [128, 3, 2]], # 2-P2/4
[-1, 3, BottleneckCSPA, [128]],
[-1, 1, Conv, [256, 3, 2]], # 4-P3/8
[-1, 7, BottleneckCSPA, [256]],
[-1, 1, Conv, [512, 3, 2]], # 6-P4/16
[-1, 7, BottleneckCSPA, [512]],
[-1, 1, Conv, [768, 3, 2]], # 8-P5/32
[-1, 3, BottleneckCSPA, [768]],
[-1, 1, Conv, [1024, 3, 2]], # 10-P6/64
[-1, 3, BottleneckCSPA, [1024]], # 11
]
# CSP-Dark-PAN head
head:
[[-1, 1, SPPCSPC, [512]], # 12
[-1, 1, Conv, [384, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[-6, 1, Conv, [384, 1, 1]], # route backbone P5
[[-1, -2], 1, Concat, [1]],
[-1, 3, BottleneckCSPB, [384]], # 17
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[-13, 1, Conv, [256, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 3, BottleneckCSPB, [256]], # 22
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[-20, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 3, BottleneckCSPB, [128]], # 27
[-1, 1, Conv, [256, 3, 1]],
[-2, 1, Conv, [256, 3, 2]],
[[-1, 22], 1, Concat, [1]], # cat
[-1, 3, BottleneckCSPB, [256]], # 31
[-1, 1, Conv, [512, 3, 1]],
[-2, 1, Conv, [384, 3, 2]],
[[-1, 17], 1, Concat, [1]], # cat
[-1, 3, BottleneckCSPB, [384]], # 35
[-1, 1, Conv, [768, 3, 1]],
[-2, 1, Conv, [512, 3, 2]],
[[-1, 12], 1, Concat, [1]], # cat
[-1, 3, BottleneckCSPB, [512]], # 39
[-1, 1, Conv, [1024, 3, 1]],
[[28,32,36,40], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5, P6)
]
================================================
FILE: cfg/baseline/yolov3-spp.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
# darknet53 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
[-1, 1, Bottleneck, [64]],
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
[-1, 2, Bottleneck, [128]],
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
[-1, 8, Bottleneck, [256]],
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
[-1, 8, Bottleneck, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
[-1, 4, Bottleneck, [1024]], # 10
]
# YOLOv3-SPP head
head:
[[-1, 1, Bottleneck, [1024, False]],
[-1, 1, SPP, [512, [5, 9, 13]]],
[-1, 1, Conv, [1024, 3, 1]],
[-1, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 8], 1, Concat, [1]], # cat backbone P4
[-1, 1, Bottleneck, [512, False]],
[-1, 1, Bottleneck, [512, False]],
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P3
[-1, 1, Bottleneck, [256, False]],
[-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
[[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
================================================
FILE: cfg/baseline/yolov3.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
# darknet53 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
[-1, 1, Bottleneck, [64]],
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
[-1, 2, Bottleneck, [128]],
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
[-1, 8, Bottleneck, [256]],
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
[-1, 8, Bottleneck, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
[-1, 4, Bottleneck, [1024]], # 10
]
# YOLOv3 head
head:
[[-1, 1, Bottleneck, [1024, False]],
[-1, 1, Conv, [512, [1, 1]]],
[-1, 1, Conv, [1024, 3, 1]],
[-1, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 8], 1, Concat, [1]], # cat backbone P4
[-1, 1, Bottleneck, [512, False]],
[-1, 1, Bottleneck, [512, False]],
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P3
[-1, 1, Bottleneck, [256, False]],
[-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
[[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
================================================
FILE: cfg/baseline/yolov4-csp.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [12,16, 19,36, 40,28] # P3/8
- [36,75, 76,55, 72,146] # P4/16
- [142,110, 192,243, 459,401] # P5/32
# CSP-Darknet backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
[-1, 1, Bottleneck, [64]],
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
[-1, 2, BottleneckCSPC, [128]],
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
[-1, 8, BottleneckCSPC, [256]],
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
[-1, 8, BottleneckCSPC, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
[-1, 4, BottleneckCSPC, [1024]], # 10
]
# CSP-Dark-PAN head
head:
[[-1, 1, SPPCSPC, [512]], # 11
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[8, 1, Conv, [256, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 2, BottleneckCSPB, [256]], # 16
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[6, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 2, BottleneckCSPB, [128]], # 21
[-1, 1, Conv, [256, 3, 1]],
[-2, 1, Conv, [256, 3, 2]],
[[-1, 16], 1, Concat, [1]], # cat
[-1, 2, BottleneckCSPB, [256]], # 25
[-1, 1, Conv, [512, 3, 1]],
[-2, 1, Conv, [512, 3, 2]],
[[-1, 11], 1, Concat, [1]], # cat
[-1, 2, BottleneckCSPB, [512]], # 29
[-1, 1, Conv, [1024, 3, 1]],
[[22,26,30], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
================================================
FILE: cfg/deploy/yolov7-d6.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [ 19,27, 44,40, 38,94 ] # P3/8
- [ 96,68, 86,152, 180,137 ] # P4/16
- [ 140,301, 303,264, 238,542 ] # P5/32
- [ 436,615, 739,380, 925,792 ] # P6/64
# yolov7-d6 backbone
backbone:
# [from, number, module, args],
[[-1, 1, ReOrg, []], # 0
[-1, 1, Conv, [96, 3, 1]], # 1-P1/2
[-1, 1, DownC, [192]], # 2-P2/4
[-1, 1, Conv, [64, 1, 1]],
[-2, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -3, -5, -7, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [192, 1, 1]], # 14
[-1, 1, DownC, [384]], # 15-P3/8
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -7, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]], # 27
[-1, 1, DownC, [768]], # 28-P4/16
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -7, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [768, 1, 1]], # 40
[-1, 1, DownC, [1152]], # 41-P5/32
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[[-1, -3, -5, -7, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [1152, 1, 1]], # 53
[-1, 1, DownC, [1536]], # 54-P6/64
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[[-1, -3, -5, -7, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [1536, 1, 1]], # 66
]
# yolov7-d6 head
head:
[[-1, 1, SPPCSPC, [768]], # 67
[-1, 1, Conv, [576, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[53, 1, Conv, [576, 1, 1]], # route backbone P5
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [576, 1, 1]], # 83
[-1, 1, Conv, [384, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[40, 1, Conv, [384, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]], # 99
[-1, 1, Conv, [192, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[27, 1, Conv, [192, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [192, 1, 1]], # 115
[-1, 1, DownC, [384]],
[[-1, 99], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]], # 129
[-1, 1, DownC, [576]],
[[-1, 83], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [576, 1, 1]], # 143
[-1, 1, DownC, [768]],
[[-1, 67], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [768, 1, 1]], # 157
[115, 1, Conv, [384, 3, 1]],
[129, 1, Conv, [768, 3, 1]],
[143, 1, Conv, [1152, 3, 1]],
[157, 1, Conv, [1536, 3, 1]],
[[158,159,160,161], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
]
================================================
FILE: cfg/deploy/yolov7-e6.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [ 19,27, 44,40, 38,94 ] # P3/8
- [ 96,68, 86,152, 180,137 ] # P4/16
- [ 140,301, 303,264, 238,542 ] # P5/32
- [ 436,615, 739,380, 925,792 ] # P6/64
# yolov7-e6 backbone
backbone:
# [from, number, module, args],
[[-1, 1, ReOrg, []], # 0
[-1, 1, Conv, [80, 3, 1]], # 1-P1/2
[-1, 1, DownC, [160]], # 2-P2/4
[-1, 1, Conv, [64, 1, 1]],
[-2, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [160, 1, 1]], # 12
[-1, 1, DownC, [320]], # 13-P3/8
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 23
[-1, 1, DownC, [640]], # 24-P4/16
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [640, 1, 1]], # 34
[-1, 1, DownC, [960]], # 35-P5/32
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [960, 1, 1]], # 45
[-1, 1, DownC, [1280]], # 46-P6/64
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [1280, 1, 1]], # 56
]
# yolov7-e6 head
head:
[[-1, 1, SPPCSPC, [640]], # 57
[-1, 1, Conv, [480, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[45, 1, Conv, [480, 1, 1]], # route backbone P5
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [480, 1, 1]], # 71
[-1, 1, Conv, [320, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[34, 1, Conv, [320, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 85
[-1, 1, Conv, [160, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[23, 1, Conv, [160, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [160, 1, 1]], # 99
[-1, 1, DownC, [320]],
[[-1, 85], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 111
[-1, 1, DownC, [480]],
[[-1, 71], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [480, 1, 1]], # 123
[-1, 1, DownC, [640]],
[[-1, 57], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [640, 1, 1]], # 135
[99, 1, Conv, [320, 3, 1]],
[111, 1, Conv, [640, 3, 1]],
[123, 1, Conv, [960, 3, 1]],
[135, 1, Conv, [1280, 3, 1]],
[[136,137,138,139], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
]
================================================
FILE: cfg/deploy/yolov7-e6e.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [ 19,27, 44,40, 38,94 ] # P3/8
- [ 96,68, 86,152, 180,137 ] # P4/16
- [ 140,301, 303,264, 238,542 ] # P5/32
- [ 436,615, 739,380, 925,792 ] # P6/64
# yolov7-e6e backbone
backbone:
# [from, number, module, args],
[[-1, 1, ReOrg, []], # 0
[-1, 1, Conv, [80, 3, 1]], # 1-P1/2
[-1, 1, DownC, [160]], # 2-P2/4
[-1, 1, Conv, [64, 1, 1]],
[-2, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [160, 1, 1]], # 12
[-11, 1, Conv, [64, 1, 1]],
[-12, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [160, 1, 1]], # 22
[[-1, -11], 1, Shortcut, [1]], # 23
[-1, 1, DownC, [320]], # 24-P3/8
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 34
[-11, 1, Conv, [128, 1, 1]],
[-12, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 44
[[-1, -11], 1, Shortcut, [1]], # 45
[-1, 1, DownC, [640]], # 46-P4/16
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [640, 1, 1]], # 56
[-11, 1, Conv, [256, 1, 1]],
[-12, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [640, 1, 1]], # 66
[[-1, -11], 1, Shortcut, [1]], # 67
[-1, 1, DownC, [960]], # 68-P5/32
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [960, 1, 1]], # 78
[-11, 1, Conv, [384, 1, 1]],
[-12, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [960, 1, 1]], # 88
[[-1, -11], 1, Shortcut, [1]], # 89
[-1, 1, DownC, [1280]], # 90-P6/64
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [1280, 1, 1]], # 100
[-11, 1, Conv, [512, 1, 1]],
[-12, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [1280, 1, 1]], # 110
[[-1, -11], 1, Shortcut, [1]], # 111
]
# yolov7-e6e head
head:
[[-1, 1, SPPCSPC, [640]], # 112
[-1, 1, Conv, [480, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[89, 1, Conv, [480, 1, 1]], # route backbone P5
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [480, 1, 1]], # 126
[-11, 1, Conv, [384, 1, 1]],
[-12, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [480, 1, 1]], # 136
[[-1, -11], 1, Shortcut, [1]], # 137
[-1, 1, Conv, [320, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[67, 1, Conv, [320, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 151
[-11, 1, Conv, [256, 1, 1]],
[-12, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 161
[[-1, -11], 1, Shortcut, [1]], # 162
[-1, 1, Conv, [160, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[45, 1, Conv, [160, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [160, 1, 1]], # 176
[-11, 1, Conv, [128, 1, 1]],
[-12, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [160, 1, 1]], # 186
[[-1, -11], 1, Shortcut, [1]], # 187
[-1, 1, DownC, [320]],
[[-1, 162], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 199
[-11, 1, Conv, [256, 1, 1]],
[-12, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 209
[[-1, -11], 1, Shortcut, [1]], # 210
[-1, 1, DownC, [480]],
[[-1, 137], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [480, 1, 1]], # 222
[-11, 1, Conv, [384, 1, 1]],
[-12, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [480, 1, 1]], # 232
[[-1, -11], 1, Shortcut, [1]], # 233
[-1, 1, DownC, [640]],
[[-1, 112], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [640, 1, 1]], # 245
[-11, 1, Conv, [512, 1, 1]],
[-12, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [640, 1, 1]], # 255
[[-1, -11], 1, Shortcut, [1]], # 256
[187, 1, Conv, [320, 3, 1]],
[210, 1, Conv, [640, 3, 1]],
[233, 1, Conv, [960, 3, 1]],
[256, 1, Conv, [1280, 3, 1]],
[[257,258,259,260], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
]
================================================
FILE: cfg/deploy/yolov7-tiny-silu.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
# YOLOv7-tiny backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [32, 3, 2]], # 0-P1/2
[-1, 1, Conv, [64, 3, 2]], # 1-P2/4
[-1, 1, Conv, [32, 1, 1]],
[-2, 1, Conv, [32, 1, 1]],
[-1, 1, Conv, [32, 3, 1]],
[-1, 1, Conv, [32, 3, 1]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [64, 1, 1]], # 7
[-1, 1, MP, []], # 8-P3/8
[-1, 1, Conv, [64, 1, 1]],
[-2, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]], # 14
[-1, 1, MP, []], # 15-P4/16
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 21
[-1, 1, MP, []], # 22-P5/32
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]], # 28
]
# YOLOv7-tiny head
head:
[[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, SP, [5]],
[-2, 1, SP, [9]],
[-3, 1, SP, [13]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[[-1, -7], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 37
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[21, 1, Conv, [128, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [64, 1, 1]],
[-2, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]], # 47
[-1, 1, Conv, [64, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[14, 1, Conv, [64, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [32, 1, 1]],
[-2, 1, Conv, [32, 1, 1]],
[-1, 1, Conv, [32, 3, 1]],
[-1, 1, Conv, [32, 3, 1]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [64, 1, 1]], # 57
[-1, 1, Conv, [128, 3, 2]],
[[-1, 47], 1, Concat, [1]],
[-1, 1, Conv, [64, 1, 1]],
[-2, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]], # 65
[-1, 1, Conv, [256, 3, 2]],
[[-1, 37], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 73
[57, 1, Conv, [128, 3, 1]],
[65, 1, Conv, [256, 3, 1]],
[73, 1, Conv, [512, 3, 1]],
[[74,75,76], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
================================================
FILE: cfg/deploy/yolov7-tiny.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
# yolov7-tiny backbone
backbone:
# [from, number, module, args] c2, k=1, s=1, p=None, g=1, act=True
[[-1, 1, Conv, [32, 3, 2, None, 1, nn.LeakyReLU(0.1)]], # 0-P1/2
[-1, 1, Conv, [64, 3, 2, None, 1, nn.LeakyReLU(0.1)]], # 1-P2/4
[-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 7
[-1, 1, MP, []], # 8-P3/8
[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 14
[-1, 1, MP, []], # 15-P4/16
[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 21
[-1, 1, MP, []], # 22-P5/32
[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 28
]
# yolov7-tiny head
head:
[[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, SP, [5]],
[-2, 1, SP, [9]],
[-3, 1, SP, [13]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -7], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 37
[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[21, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 47
[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[14, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 57
[-1, 1, Conv, [128, 3, 2, None, 1, nn.LeakyReLU(0.1)]],
[[-1, 47], 1, Concat, [1]],
[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 65
[-1, 1, Conv, [256, 3, 2, None, 1, nn.LeakyReLU(0.1)]],
[[-1, 37], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 73
[57, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[65, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[73, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[74,75,76], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
================================================
FILE: cfg/deploy/yolov7-w6.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [ 19,27, 44,40, 38,94 ] # P3/8
- [ 96,68, 86,152, 180,137 ] # P4/16
- [ 140,301, 303,264, 238,542 ] # P5/32
- [ 436,615, 739,380, 925,792 ] # P6/64
# yolov7-w6 backbone
backbone:
# [from, number, module, args]
[[-1, 1, ReOrg, []], # 0
[-1, 1, Conv, [64, 3, 1]], # 1-P1/2
[-1, 1, Conv, [128, 3, 2]], # 2-P2/4
[-1, 1, Conv, [64, 1, 1]],
[-2, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]], # 10
[-1, 1, Conv, [256, 3, 2]], # 11-P3/8
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 19
[-1, 1, Conv, [512, 3, 2]], # 20-P4/16
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]], # 28
[-1, 1, Conv, [768, 3, 2]], # 29-P5/32
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [768, 1, 1]], # 37
[-1, 1, Conv, [1024, 3, 2]], # 38-P6/64
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [1024, 1, 1]], # 46
]
# yolov7-w6 head
head:
[[-1, 1, SPPCSPC, [512]], # 47
[-1, 1, Conv, [384, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[37, 1, Conv, [384, 1, 1]], # route backbone P5
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]], # 59
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[28, 1, Conv, [256, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 71
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[19, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]], # 83
[-1, 1, Conv, [256, 3, 2]],
[[-1, 71], 1, Concat, [1]], # cat
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 93
[-1, 1, Conv, [384, 3, 2]],
[[-1, 59], 1, Concat, [1]], # cat
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]], # 103
[-1, 1, Conv, [512, 3, 2]],
[[-1, 47], 1, Concat, [1]], # cat
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]], # 113
[83, 1, Conv, [256, 3, 1]],
[93, 1, Conv, [512, 3, 1]],
[103, 1, Conv, [768, 3, 1]],
[113, 1, Conv, [1024, 3, 1]],
[[114,115,116,117], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
]
================================================
FILE: cfg/deploy/yolov7.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [12,16, 19,36, 40,28] # P3/8
- [36,75, 76,55, 72,146] # P4/16
- [142,110, 192,243, 459,401] # P5/32
# yolov7 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
[-1, 1, Conv, [64, 1, 1]],
[-2, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 11
[-1, 1, MP, []],
[-1, 1, Conv, [128, 1, 1]],
[-3, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 2]],
[[-1, -3], 1, Concat, [1]], # 16-P3/8
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]], # 24
[-1, 1, MP, []],
[-1, 1, Conv, [256, 1, 1]],
[-3, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 2]],
[[-1, -3], 1, Concat, [1]], # 29-P4/16
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [1024, 1, 1]], # 37
[-1, 1, MP, []],
[-1, 1, Conv, [512, 1, 1]],
[-3, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [512, 3, 2]],
[[-1, -3], 1, Concat, [1]], # 42-P5/32
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [1024, 1, 1]], # 50
]
# yolov7 head
head:
[[-1, 1, SPPCSPC, [512]], # 51
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[37, 1, Conv, [256, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 63
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[24, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]], # 75
[-1, 1, MP, []],
[-1, 1, Conv, [128, 1, 1]],
[-3, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 2]],
[[-1, -3, 63], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 88
[-1, 1, MP, []],
[-1, 1, Conv, [256, 1, 1]],
[-3, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 2]],
[[-1, -3, 51], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]], # 101
[75, 1, RepConv, [256, 3, 1]],
[88, 1, RepConv, [512, 3, 1]],
[101, 1, RepConv, [1024, 3, 1]],
[[102,103,104], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
================================================
FILE: cfg/deploy/yolov7x.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [12,16, 19,36, 40,28] # P3/8
- [36,75, 76,55, 72,146] # P4/16
- [142,110, 192,243, 459,401] # P5/32
# yolov7x backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [40, 3, 1]], # 0
[-1, 1, Conv, [80, 3, 2]], # 1-P1/2
[-1, 1, Conv, [80, 3, 1]],
[-1, 1, Conv, [160, 3, 2]], # 3-P2/4
[-1, 1, Conv, [64, 1, 1]],
[-2, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 13
[-1, 1, MP, []],
[-1, 1, Conv, [160, 1, 1]],
[-3, 1, Conv, [160, 1, 1]],
[-1, 1, Conv, [160, 3, 2]],
[[-1, -3], 1, Concat, [1]], # 18-P3/8
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [640, 1, 1]], # 28
[-1, 1, MP, []],
[-1, 1, Conv, [320, 1, 1]],
[-3, 1, Conv, [320, 1, 1]],
[-1, 1, Conv, [320, 3, 2]],
[[-1, -3], 1, Concat, [1]], # 33-P4/16
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [1280, 1, 1]], # 43
[-1, 1, MP, []],
[-1, 1, Conv, [640, 1, 1]],
[-3, 1, Conv, [640, 1, 1]],
[-1, 1, Conv, [640, 3, 2]],
[[-1, -3], 1, Concat, [1]], # 48-P5/32
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [1280, 1, 1]], # 58
]
# yolov7x head
head:
[[-1, 1, SPPCSPC, [640]], # 59
[-1, 1, Conv, [320, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[43, 1, Conv, [320, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 73
[-1, 1, Conv, [160, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[28, 1, Conv, [160, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [160, 1, 1]], # 87
[-1, 1, MP, []],
[-1, 1, Conv, [160, 1, 1]],
[-3, 1, Conv, [160, 1, 1]],
[-1, 1, Conv, [160, 3, 2]],
[[-1, -3, 73], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 102
[-1, 1, MP, []],
[-1, 1, Conv, [320, 1, 1]],
[-3, 1, Conv, [320, 1, 1]],
[-1, 1, Conv, [320, 3, 2]],
[[-1, -3, 59], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [640, 1, 1]], # 117
[87, 1, Conv, [320, 3, 1]],
[102, 1, Conv, [640, 3, 1]],
[117, 1, Conv, [1280, 3, 1]],
[[118,119,120], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
================================================
FILE: cfg/training/yolov7-d6.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [ 19,27, 44,40, 38,94 ] # P3/8
- [ 96,68, 86,152, 180,137 ] # P4/16
- [ 140,301, 303,264, 238,542 ] # P5/32
- [ 436,615, 739,380, 925,792 ] # P6/64
# yolov7 backbone
backbone:
# [from, number, module, args],
[[-1, 1, ReOrg, []], # 0
[-1, 1, Conv, [96, 3, 1]], # 1-P1/2
[-1, 1, DownC, [192]], # 2-P2/4
[-1, 1, Conv, [64, 1, 1]],
[-2, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -3, -5, -7, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [192, 1, 1]], # 14
[-1, 1, DownC, [384]], # 15-P3/8
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -7, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]], # 27
[-1, 1, DownC, [768]], # 28-P4/16
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -7, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [768, 1, 1]], # 40
[-1, 1, DownC, [1152]], # 41-P5/32
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[[-1, -3, -5, -7, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [1152, 1, 1]], # 53
[-1, 1, DownC, [1536]], # 54-P6/64
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[[-1, -3, -5, -7, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [1536, 1, 1]], # 66
]
# yolov7 head
head:
[[-1, 1, SPPCSPC, [768]], # 67
[-1, 1, Conv, [576, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[53, 1, Conv, [576, 1, 1]], # route backbone P5
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [576, 1, 1]], # 83
[-1, 1, Conv, [384, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[40, 1, Conv, [384, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]], # 99
[-1, 1, Conv, [192, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[27, 1, Conv, [192, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [192, 1, 1]], # 115
[-1, 1, DownC, [384]],
[[-1, 99], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]], # 129
[-1, 1, DownC, [576]],
[[-1, 83], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [576, 1, 1]], # 143
[-1, 1, DownC, [768]],
[[-1, 67], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]],
[-1, 1, Conv, [768, 1, 1]], # 157
[115, 1, Conv, [384, 3, 1]],
[129, 1, Conv, [768, 3, 1]],
[143, 1, Conv, [1152, 3, 1]],
[157, 1, Conv, [1536, 3, 1]],
[115, 1, Conv, [384, 3, 1]],
[99, 1, Conv, [768, 3, 1]],
[83, 1, Conv, [1152, 3, 1]],
[67, 1, Conv, [1536, 3, 1]],
[[158,159,160,161,162,163,164,165], 1, IAuxDetect, [nc, anchors]], # Detect(P3, P4, P5, P6)
]
================================================
FILE: cfg/training/yolov7-e6.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [ 19,27, 44,40, 38,94 ] # P3/8
- [ 96,68, 86,152, 180,137 ] # P4/16
- [ 140,301, 303,264, 238,542 ] # P5/32
- [ 436,615, 739,380, 925,792 ] # P6/64
# yolov7 backbone
backbone:
# [from, number, module, args],
[[-1, 1, ReOrg, []], # 0
[-1, 1, Conv, [80, 3, 1]], # 1-P1/2
[-1, 1, DownC, [160]], # 2-P2/4
[-1, 1, Conv, [64, 1, 1]],
[-2, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [160, 1, 1]], # 12
[-1, 1, DownC, [320]], # 13-P3/8
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 23
[-1, 1, DownC, [640]], # 24-P4/16
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [640, 1, 1]], # 34
[-1, 1, DownC, [960]], # 35-P5/32
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [960, 1, 1]], # 45
[-1, 1, DownC, [1280]], # 46-P6/64
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [1280, 1, 1]], # 56
]
# yolov7 head
head:
[[-1, 1, SPPCSPC, [640]], # 57
[-1, 1, Conv, [480, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[45, 1, Conv, [480, 1, 1]], # route backbone P5
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [480, 1, 1]], # 71
[-1, 1, Conv, [320, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[34, 1, Conv, [320, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 85
[-1, 1, Conv, [160, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[23, 1, Conv, [160, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [160, 1, 1]], # 99
[-1, 1, DownC, [320]],
[[-1, 85], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 111
[-1, 1, DownC, [480]],
[[-1, 71], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [480, 1, 1]], # 123
[-1, 1, DownC, [640]],
[[-1, 57], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [640, 1, 1]], # 135
[99, 1, Conv, [320, 3, 1]],
[111, 1, Conv, [640, 3, 1]],
[123, 1, Conv, [960, 3, 1]],
[135, 1, Conv, [1280, 3, 1]],
[99, 1, Conv, [320, 3, 1]],
[85, 1, Conv, [640, 3, 1]],
[71, 1, Conv, [960, 3, 1]],
[57, 1, Conv, [1280, 3, 1]],
[[136,137,138,139,140,141,142,143], 1, IAuxDetect, [nc, anchors]], # Detect(P3, P4, P5, P6)
]
================================================
FILE: cfg/training/yolov7-e6e.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [ 19,27, 44,40, 38,94 ] # P3/8
- [ 96,68, 86,152, 180,137 ] # P4/16
- [ 140,301, 303,264, 238,542 ] # P5/32
- [ 436,615, 739,380, 925,792 ] # P6/64
# yolov7 backbone
backbone:
# [from, number, module, args],
[[-1, 1, ReOrg, []], # 0
[-1, 1, Conv, [80, 3, 1]], # 1-P1/2
[-1, 1, DownC, [160]], # 2-P2/4
[-1, 1, Conv, [64, 1, 1]],
[-2, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [160, 1, 1]], # 12
[-11, 1, Conv, [64, 1, 1]],
[-12, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [160, 1, 1]], # 22
[[-1, -11], 1, Shortcut, [1]], # 23
[-1, 1, DownC, [320]], # 24-P3/8
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 34
[-11, 1, Conv, [128, 1, 1]],
[-12, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 44
[[-1, -11], 1, Shortcut, [1]], # 45
[-1, 1, DownC, [640]], # 46-P4/16
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [640, 1, 1]], # 56
[-11, 1, Conv, [256, 1, 1]],
[-12, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [640, 1, 1]], # 66
[[-1, -11], 1, Shortcut, [1]], # 67
[-1, 1, DownC, [960]], # 68-P5/32
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [960, 1, 1]], # 78
[-11, 1, Conv, [384, 1, 1]],
[-12, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [960, 1, 1]], # 88
[[-1, -11], 1, Shortcut, [1]], # 89
[-1, 1, DownC, [1280]], # 90-P6/64
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [1280, 1, 1]], # 100
[-11, 1, Conv, [512, 1, 1]],
[-12, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [1280, 1, 1]], # 110
[[-1, -11], 1, Shortcut, [1]], # 111
]
# yolov7 head
head:
[[-1, 1, SPPCSPC, [640]], # 112
[-1, 1, Conv, [480, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[89, 1, Conv, [480, 1, 1]], # route backbone P5
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [480, 1, 1]], # 126
[-11, 1, Conv, [384, 1, 1]],
[-12, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [480, 1, 1]], # 136
[[-1, -11], 1, Shortcut, [1]], # 137
[-1, 1, Conv, [320, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[67, 1, Conv, [320, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 151
[-11, 1, Conv, [256, 1, 1]],
[-12, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 161
[[-1, -11], 1, Shortcut, [1]], # 162
[-1, 1, Conv, [160, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[45, 1, Conv, [160, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [160, 1, 1]], # 176
[-11, 1, Conv, [128, 1, 1]],
[-12, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [160, 1, 1]], # 186
[[-1, -11], 1, Shortcut, [1]], # 187
[-1, 1, DownC, [320]],
[[-1, 162], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 199
[-11, 1, Conv, [256, 1, 1]],
[-12, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 209
[[-1, -11], 1, Shortcut, [1]], # 210
[-1, 1, DownC, [480]],
[[-1, 137], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [480, 1, 1]], # 222
[-11, 1, Conv, [384, 1, 1]],
[-12, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [480, 1, 1]], # 232
[[-1, -11], 1, Shortcut, [1]], # 233
[-1, 1, DownC, [640]],
[[-1, 112], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [640, 1, 1]], # 245
[-11, 1, Conv, [512, 1, 1]],
[-12, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [640, 1, 1]], # 255
[[-1, -11], 1, Shortcut, [1]], # 256
[187, 1, Conv, [320, 3, 1]],
[210, 1, Conv, [640, 3, 1]],
[233, 1, Conv, [960, 3, 1]],
[256, 1, Conv, [1280, 3, 1]],
[186, 1, Conv, [320, 3, 1]],
[161, 1, Conv, [640, 3, 1]],
[136, 1, Conv, [960, 3, 1]],
[112, 1, Conv, [1280, 3, 1]],
[[257,258,259,260,261,262,263,264], 1, IAuxDetect, [nc, anchors]], # Detect(P3, P4, P5, P6)
]
================================================
FILE: cfg/training/yolov7-tiny.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
# yolov7-tiny backbone
backbone:
# [from, number, module, args] c2, k=1, s=1, p=None, g=1, act=True
[[-1, 1, Conv, [32, 3, 2, None, 1, nn.LeakyReLU(0.1)]], # 0-P1/2
[-1, 1, Conv, [64, 3, 2, None, 1, nn.LeakyReLU(0.1)]], # 1-P2/4
[-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 7
[-1, 1, MP, []], # 8-P3/8
[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 14
[-1, 1, MP, []], # 15-P4/16
[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 21
[-1, 1, MP, []], # 22-P5/32
[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 28
]
# yolov7-tiny head
head:
[[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, SP, [5]],
[-2, 1, SP, [9]],
[-3, 1, SP, [13]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -7], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 37
[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[21, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 47
[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[14, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 57
[-1, 1, Conv, [128, 3, 2, None, 1, nn.LeakyReLU(0.1)]],
[[-1, 47], 1, Concat, [1]],
[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 65
[-1, 1, Conv, [256, 3, 2, None, 1, nn.LeakyReLU(0.1)]],
[[-1, 37], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 73
[57, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[65, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[73, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
[[74,75,76], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5)
]
================================================
FILE: cfg/training/yolov7-w6.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [ 19,27, 44,40, 38,94 ] # P3/8
- [ 96,68, 86,152, 180,137 ] # P4/16
- [ 140,301, 303,264, 238,542 ] # P5/32
- [ 436,615, 739,380, 925,792 ] # P6/64
# yolov7 backbone
backbone:
# [from, number, module, args]
[[-1, 1, ReOrg, []], # 0
[-1, 1, Conv, [64, 3, 1]], # 1-P1/2
[-1, 1, Conv, [128, 3, 2]], # 2-P2/4
[-1, 1, Conv, [64, 1, 1]],
[-2, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]], # 10
[-1, 1, Conv, [256, 3, 2]], # 11-P3/8
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 19
[-1, 1, Conv, [512, 3, 2]], # 20-P4/16
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]], # 28
[-1, 1, Conv, [768, 3, 2]], # 29-P5/32
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[-1, 1, Conv, [384, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [768, 1, 1]], # 37
[-1, 1, Conv, [1024, 3, 2]], # 38-P6/64
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [1024, 1, 1]], # 46
]
# yolov7 head
head:
[[-1, 1, SPPCSPC, [512]], # 47
[-1, 1, Conv, [384, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[37, 1, Conv, [384, 1, 1]], # route backbone P5
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]], # 59
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[28, 1, Conv, [256, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 71
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[19, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]], # 83
[-1, 1, Conv, [256, 3, 2]],
[[-1, 71], 1, Concat, [1]], # cat
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 93
[-1, 1, Conv, [384, 3, 2]],
[[-1, 59], 1, Concat, [1]], # cat
[-1, 1, Conv, [384, 1, 1]],
[-2, 1, Conv, [384, 1, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[-1, 1, Conv, [192, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [384, 1, 1]], # 103
[-1, 1, Conv, [512, 3, 2]],
[[-1, 47], 1, Concat, [1]], # cat
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]], # 113
[83, 1, Conv, [256, 3, 1]],
[93, 1, Conv, [512, 3, 1]],
[103, 1, Conv, [768, 3, 1]],
[113, 1, Conv, [1024, 3, 1]],
[83, 1, Conv, [320, 3, 1]],
[71, 1, Conv, [640, 3, 1]],
[59, 1, Conv, [960, 3, 1]],
[47, 1, Conv, [1280, 3, 1]],
[[114,115,116,117,118,119,120,121], 1, IAuxDetect, [nc, anchors]], # Detect(P3, P4, P5, P6)
]
================================================
FILE: cfg/training/yolov7.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [12,16, 19,36, 40,28] # P3/8
- [36,75, 76,55, 72,146] # P4/16
- [142,110, 192,243, 459,401] # P5/32
# yolov7 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
[-1, 1, Conv, [64, 1, 1]],
[-2, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 11
[-1, 1, MP, []],
[-1, 1, Conv, [128, 1, 1]],
[-3, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 2]],
[[-1, -3], 1, Concat, [1]], # 16-P3/8
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]], # 24
[-1, 1, MP, []],
[-1, 1, Conv, [256, 1, 1]],
[-3, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 2]],
[[-1, -3], 1, Concat, [1]], # 29-P4/16
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [1024, 1, 1]], # 37
[-1, 1, MP, []],
[-1, 1, Conv, [512, 1, 1]],
[-3, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [512, 3, 2]],
[[-1, -3], 1, Concat, [1]], # 42-P5/32
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [1024, 1, 1]], # 50
]
# yolov7 head
head:
[[-1, 1, SPPCSPC, [512]], # 51
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[37, 1, Conv, [256, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 63
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[24, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]], # 75
[-1, 1, MP, []],
[-1, 1, Conv, [128, 1, 1]],
[-3, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 2]],
[[-1, -3, 63], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 88
[-1, 1, MP, []],
[-1, 1, Conv, [256, 1, 1]],
[-3, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 2]],
[[-1, -3, 51], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]], # 101
[75, 1, RepConv, [256, 3, 1]],
[88, 1, RepConv, [512, 3, 1]],
[101, 1, RepConv, [1024, 3, 1]],
[[102,103,104], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5)
]
================================================
FILE: cfg/training/yolov7x.yaml
================================================
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [12,16, 19,36, 40,28] # P3/8
- [36,75, 76,55, 72,146] # P4/16
- [142,110, 192,243, 459,401] # P5/32
# yolov7 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [40, 3, 1]], # 0
[-1, 1, Conv, [80, 3, 2]], # 1-P1/2
[-1, 1, Conv, [80, 3, 1]],
[-1, 1, Conv, [160, 3, 2]], # 3-P2/4
[-1, 1, Conv, [64, 1, 1]],
[-2, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 13
[-1, 1, MP, []],
[-1, 1, Conv, [160, 1, 1]],
[-3, 1, Conv, [160, 1, 1]],
[-1, 1, Conv, [160, 3, 2]],
[[-1, -3], 1, Concat, [1]], # 18-P3/8
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [640, 1, 1]], # 28
[-1, 1, MP, []],
[-1, 1, Conv, [320, 1, 1]],
[-3, 1, Conv, [320, 1, 1]],
[-1, 1, Conv, [320, 3, 2]],
[[-1, -3], 1, Concat, [1]], # 33-P4/16
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [1280, 1, 1]], # 43
[-1, 1, MP, []],
[-1, 1, Conv, [640, 1, 1]],
[-3, 1, Conv, [640, 1, 1]],
[-1, 1, Conv, [640, 3, 2]],
[[-1, -3], 1, Concat, [1]], # 48-P5/32
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [1280, 1, 1]], # 58
]
# yolov7 head
head:
[[-1, 1, SPPCSPC, [640]], # 59
[-1, 1, Conv, [320, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[43, 1, Conv, [320, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 73
[-1, 1, Conv, [160, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[28, 1, Conv, [160, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [160, 1, 1]], # 87
[-1, 1, MP, []],
[-1, 1, Conv, [160, 1, 1]],
[-3, 1, Conv, [160, 1, 1]],
[-1, 1, Conv, [160, 3, 2]],
[[-1, -3, 73], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [320, 1, 1]], # 102
[-1, 1, MP, []],
[-1, 1, Conv, [320, 1, 1]],
[-3, 1, Conv, [320, 1, 1]],
[-1, 1, Conv, [320, 3, 2]],
[[-1, -3, 59], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[[-1, -3, -5, -7, -8], 1, Concat, [1]],
[-1, 1, Conv, [640, 1, 1]], # 117
[87, 1, Conv, [320, 3, 1]],
[102, 1, Conv, [640, 3, 1]],
[117, 1, Conv, [1280, 3, 1]],
[[118,119,120], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5)
]
================================================
FILE: data/coco.yaml
================================================
# COCO 2017 dataset http://cocodataset.org
# download command/URL (optional)
download: bash ./scripts/get_coco.sh
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: ./coco/train2017.txt # 118287 images
val: ./coco/val2017.txt # 5000 images
test: ./coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
# number of classes
nc: 80
# class names
names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush' ]
================================================
FILE: data/hyp.scratch.custom.yaml
================================================
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.0005 # optimizer weight decay 5e-4
warmup_epochs: 3.0 # warmup epochs (fractions ok)
warmup_momentum: 0.8 # warmup initial momentum
warmup_bias_lr: 0.1 # warmup initial bias lr
box: 0.05 # box loss gain
cls: 0.3 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 0.7 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.20 # IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
# anchors: 3 # anchors per output layer (0 to ignore)
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
degrees: 0.0 # image rotation (+/- deg)
translate: 0.2 # image translation (+/- fraction)
scale: 0.5 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability)
mixup: 0.0 # image mixup (probability)
copy_paste: 0.0 # image copy paste (probability)
paste_in: 0.0 # image copy paste (probability), use 0 for faster training
loss_ota: 1 # use ComputeLossOTA, use 0 for faster training
================================================
FILE: data/hyp.scratch.p5.yaml
================================================
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.0005 # optimizer weight decay 5e-4
warmup_epochs: 3.0 # warmup epochs (fractions ok)
warmup_momentum: 0.8 # warmup initial momentum
warmup_bias_lr: 0.1 # warmup initial bias lr
box: 0.05 # box loss gain
cls: 0.3 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 0.7 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.20 # IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
# anchors: 3 # anchors per output layer (0 to ignore)
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
degrees: 0.0 # image rotation (+/- deg)
translate: 0.2 # image translation (+/- fraction)
scale: 0.9 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability)
mixup: 0.15 # image mixup (probability)
copy_paste: 0.0 # image copy paste (probability)
paste_in: 0.15 # image copy paste (probability), use 0 for faster training
loss_ota: 1 # use ComputeLossOTA, use 0 for faster training
================================================
FILE: data/hyp.scratch.p6.yaml
================================================
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.0005 # optimizer weight decay 5e-4
warmup_epochs: 3.0 # warmup epochs (fractions ok)
warmup_momentum: 0.8 # warmup initial momentum
warmup_bias_lr: 0.1 # warmup initial bias lr
box: 0.05 # box loss gain
cls: 0.3 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 0.7 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.20 # IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
# anchors: 3 # anchors per output layer (0 to ignore)
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
degrees: 0.0 # image rotation (+/- deg)
translate: 0.2 # image translation (+/- fraction)
scale: 0.9 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability)
mixup: 0.15 # image mixup (probability)
copy_paste: 0.0 # image copy paste (probability)
paste_in: 0.15 # image copy paste (probability), use 0 for faster training
loss_ota: 1 # use ComputeLossOTA, use 0 for faster training
================================================
FILE: data/hyp.scratch.tiny.yaml
================================================
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.0005 # optimizer weight decay 5e-4
warmup_epochs: 3.0 # warmup epochs (fractions ok)
warmup_momentum: 0.8 # warmup initial momentum
warmup_bias_lr: 0.1 # warmup initial bias lr
box: 0.05 # box loss gain
cls: 0.5 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 1.0 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.20 # IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
# anchors: 3 # anchors per output layer (0 to ignore)
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
degrees: 0.0 # image rotation (+/- deg)
translate: 0.1 # image translation (+/- fraction)
scale: 0.5 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability)
mixup: 0.05 # image mixup (probability)
copy_paste: 0.0 # image copy paste (probability)
paste_in: 0.05 # image copy paste (probability), use 0 for faster training
loss_ota: 1 # use ComputeLossOTA, use 0 for faster training
================================================
FILE: deploy/triton-inference-server/README.md
================================================
# YOLOv7 on Triton Inference Server
Instructions to deploy YOLOv7 as TensorRT engine to [Triton Inference Server](https://github.com/NVIDIA/triton-inference-server).
Triton Inference Server takes care of model deployment with many out-of-the-box benefits, like a GRPC and HTTP interface, automatic scheduling on multiple GPUs, shared memory (even on GPU), dynamic server-side batching, health metrics and memory resource management.
There are no additional dependencies needed to run this deployment, except a working docker daemon with GPU support.
## Export TensorRT
See https://github.com/WongKinYiu/yolov7#export for more info.
```bash
#install onnx-simplifier not listed in general yolov7 requirements.txt
pip3 install onnx-simplifier
# Pytorch Yolov7 -> ONNX with grid, EfficientNMS plugin and dynamic batch size
python export.py --weights ./yolov7.pt --grid --end2end --dynamic-batch --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640
# ONNX -> TensorRT with trtexec and docker
docker run -it --rm --gpus=all nvcr.io/nvidia/tensorrt:22.06-py3
# Copy onnx -> container: docker cp yolov7.onnx <container-id>:/workspace/
# Export with FP16 precision, min batch 1, opt batch 8 and max batch 8
./tensorrt/bin/trtexec --onnx=yolov7.onnx --minShapes=images:1x3x640x640 --optShapes=images:8x3x640x640 --maxShapes=images:8x3x640x640 --fp16 --workspace=4096 --saveEngine=yolov7-fp16-1x8x8.engine --timingCacheFile=timing.cache
# Test engine
./tensorrt/bin/trtexec --loadEngine=yolov7-fp16-1x8x8.engine
# Copy engine -> host: docker cp <container-id>:/workspace/yolov7-fp16-1x8x8.engine .
```
Example output of test with RTX 3090.
```
[I] === Performance summary ===
[I] Throughput: 73.4985 qps
[I] Latency: min = 14.8578 ms, max = 15.8344 ms, mean = 15.07 ms, median = 15.0422 ms, percentile(99%) = 15.7443 ms
[I] End-to-End Host Latency: min = 25.8715 ms, max = 28.4102 ms, mean = 26.672 ms, median = 26.6082 ms, percentile(99%) = 27.8314 ms
[I] Enqueue Time: min = 0.793701 ms, max = 1.47144 ms, mean = 1.2008 ms, median = 1.28644 ms, percentile(99%) = 1.38965 ms
[I] H2D Latency: min = 1.50073 ms, max = 1.52454 ms, mean = 1.51225 ms, median = 1.51404 ms, percentile(99%) = 1.51941 ms
[I] GPU Compute Time: min = 13.3386 ms, max = 14.3186 ms, mean = 13.5448 ms, median = 13.5178 ms, percentile(99%) = 14.2151 ms
[I] D2H Latency: min = 0.00878906 ms, max = 0.0172729 ms, mean = 0.0128844 ms, median = 0.0125732 ms, percentile(99%) = 0.0166016 ms
[I] Total Host Walltime: 3.04768 s
[I] Total GPU Compute Time: 3.03404 s
[I] Explanations of the performance metrics are printed in the verbose logs.
```
Note: 73.5 qps x batch 8 = 588 fps @ ~15ms latency.
## Model Repository
See [Triton Model Repository Documentation](https://github.com/triton-inference-server/server/blob/main/docs/model_repository.md#model-repository) for more info.
```bash
# Create folder structure
mkdir -p triton-deploy/models/yolov7/1/
touch triton-deploy/models/yolov7/config.pbtxt
# Place model
mv yolov7-fp16-1x8x8.engine triton-deploy/models/yolov7/1/model.plan
```
## Model Configuration
See [Triton Model Configuration Documentation](https://github.com/triton-inference-server/server/blob/main/docs/model_configuration.md#model-configuration) for more info.
Minimal configuration for `triton-deploy/models/yolov7/config.pbtxt`:
```
name: "yolov7"
platform: "tensorrt_plan"
max_batch_size: 8
dynamic_batching { }
```
Example repository:
```bash
$ tree triton-deploy/
triton-deploy/
└── models
└── yolov7
├── 1
│ └── model.plan
└── config.pbtxt
3 directories, 2 files
```
## Start Triton Inference Server
```
docker run --gpus all --rm --ipc=host --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -p8000:8000 -p8001:8001 -p8002:8002 -v$(pwd)/triton-deploy/models:/models nvcr.io/nvidia/tritonserver:22.06-py3 tritonserver --model-repository=/models --strict-model-config=false --log-verbose 1
```
In the log you should see:
```
+--------+---------+--------+
| Model | Version | Status |
+--------+---------+--------+
| yolov7 | 1 | READY |
+--------+---------+--------+
```
## Performance with Model Analyzer
See [Triton Model Analyzer Documentation](https://github.com/triton-inference-server/server/blob/main/docs/model_analyzer.md#model-analyzer) for more info.
Performance numbers @ RTX 3090 + AMD Ryzen 9 5950X
Example test for 16 concurrent clients using shared memory, each with batch size 1 requests:
```bash
docker run -it --ipc=host --net=host nvcr.io/nvidia/tritonserver:22.06-py3-sdk /bin/bash
./install/bin/perf_analyzer -m yolov7 -u 127.0.0.1:8001 -i grpc --shared-memory system --concurrency-range 16
# Result (truncated)
Concurrency: 16, throughput: 590.119 infer/sec, latency 27080 usec
```
Throughput for 16 clients with batch size 1 is the same as for a single thread running the engine at 16 batch size locally thanks to Triton [Dynamic Batching Strategy](https://github.com/triton-inference-server/server/blob/main/docs/model_configuration.md#dynamic-batcher). Result without dynamic batching (disable in model configuration) considerably worse:
```bash
# Result (truncated)
Concurrency: 16, throughput: 335.587 infer/sec, latency 47616 usec
```
## How to run model in your code
Example client can be found in client.py. It can run dummy input, images and videos.
```bash
pip3 install tritonclient[all] opencv-python
python3 client.py image data/dog.jpg
```
```
$ python3 client.py --help
usage: client.py [-h] [-m MODEL] [--width WIDTH] [--height HEIGHT] [-u URL] [-o OUT] [-f FPS] [-i] [-v] [-t CLIENT_TIMEOUT] [-s] [-r ROOT_CERTIFICATES] [-p PRIVATE_KEY] [-x CERTIFICATE_CHAIN] {dummy,image,video} [input]
positional arguments:
{dummy,image,video} Run mode. 'dummy' will send an emtpy buffer to the server to test if inference works. 'image' will process an image. 'video' will process a video.
input Input file to load from in image or video mode
optional arguments:
-h, --help show this help message and exit
-m MODEL, --model MODEL
Inference model name, default yolov7
--width WIDTH Inference model input width, default 640
--height HEIGHT Inference model input height, default 640
-u URL, --url URL Inference server URL, default localhost:8001
-o OUT, --out OUT Write output into file instead of displaying it
-f FPS, --fps FPS Video output fps, default 24.0 FPS
-i, --model-info Print model status, configuration and statistics
-v, --verbose Enable verbose client output
-t CLIENT_TIMEOUT, --client-timeout CLIENT_TIMEOUT
Client timeout in seconds, default no timeout
-s, --ssl Enable SSL encrypted channel to the server
-r ROOT_CERTIFICATES, --root-certificates ROOT_CERTIFICATES
File holding PEM-encoded root certificates, default none
-p PRIVATE_KEY, --private-key PRIVATE_KEY
File holding PEM-encoded private key, default is none
-x CERTIFICATE_CHAIN, --certificate-chain CERTIFICATE_CHAIN
File holding PEM-encoded certicate chain default is none
```
================================================
FILE: deploy/triton-inference-server/boundingbox.py
================================================
class BoundingBox:
def __init__(self, classID, confidence, x1, x2, y1, y2, image_width, image_height):
self.classID = classID
self.confidence = confidence
self.x1 = x1
self.x2 = x2
self.y1 = y1
self.y2 = y2
self.u1 = x1 / image_width
self.u2 = x2 / image_width
self.v1 = y1 / image_height
self.v2 = y2 / image_height
def box(self):
return (self.x1, self.y1, self.x2, self.y2)
def width(self):
return self.x2 - self.x1
def height(self):
return self.y2 - self.y1
def center_absolute(self):
return (0.5 * (self.x1 + self.x2), 0.5 * (self.y1 + self.y2))
def center_normalized(self):
return (0.5 * (self.u1 + self.u2), 0.5 * (self.v1 + self.v2))
def size_absolute(self):
return (self.x2 - self.x1, self.y2 - self.y1)
def size_normalized(self):
return (self.u2 - self.u1, self.v2 - self.v1)
================================================
FILE: deploy/triton-inference-server/client.py
================================================
#!/usr/bin/env python
import argparse
import numpy as np
import sys
import cv2
import tritonclient.grpc as grpcclient
from tritonclient.utils import InferenceServerException
from processing import preprocess, postprocess
from render import render_box, render_filled_box, get_text_size, render_text, RAND_COLORS
from labels import COCOLabels
INPUT_NAMES = ["images"]
OUTPUT_NAMES = ["num_dets", "det_boxes", "det_scores", "det_classes"]
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('mode',
choices=['dummy', 'image', 'video'],
default='dummy',
help='Run mode. \'dummy\' will send an emtpy buffer to the server to test if inference works. \'image\' will process an image. \'video\' will process a video.')
parser.add_argument('input',
type=str,
nargs='?',
help='Input file to load from in image or video mode')
parser.add_argument('-m',
'--model',
type=str,
required=False,
default='yolov7',
help='Inference model name, default yolov7')
parser.add_argument('--width',
type=int,
required=False,
default=640,
help='Inference model input width, default 640')
parser.add_argument('--height',
type=int,
required=False,
default=640,
help='Inference model input height, default 640')
parser.add_argument('-u',
'--url',
type=str,
required=False,
default='localhost:8001',
help='Inference server URL, default localhost:8001')
parser.add_argument('-o',
'--out',
type=str,
required=False,
default='',
help='Write output into file instead of displaying it')
parser.add_argument('-f',
'--fps',
type=float,
required=False,
default=24.0,
help='Video output fps, default 24.0 FPS')
parser.add_argument('-i',
'--model-info',
action="store_true",
required=False,
default=False,
help='Print model status, configuration and statistics')
parser.add_argument('-v',
'--verbose',
action="store_true",
required=False,
default=False,
help='Enable verbose client output')
parser.add_argument('-t',
'--client-timeout',
type=float,
required=False,
default=None,
help='Client timeout in seconds, default no timeout')
parser.add_argument('-s',
'--ssl',
action="store_true",
required=False,
default=False,
help='Enable SSL encrypted channel to the server')
parser.add_argument('-r',
'--root-certificates',
type=str,
required=False,
default=None,
help='File holding PEM-encoded root certificates, default none')
parser.add_argument('-p',
'--private-key',
type=str,
required=False,
default=None,
help='File holding PEM-encoded private key, default is none')
parser.add_argument('-x',
'--certificate-chain',
type=str,
required=False,
default=None,
help='File holding PEM-encoded certicate chain default is none')
FLAGS = parser.parse_args()
# Create server context
try:
triton_client = grpcclient.InferenceServerClient(
url=FLAGS.url,
verbose=FLAGS.verbose,
ssl=FLAGS.ssl,
root_certificates=FLAGS.root_certificates,
private_key=FLAGS.private_key,
certificate_chain=FLAGS.certificate_chain)
except Exception as e:
print("context creation failed: " + str(e))
sys.exit()
# Health check
if not triton_client.is_server_live():
print("FAILED : is_server_live")
sys.exit(1)
if not triton_client.is_server_ready():
print("FAILED : is_server_ready")
sys.exit(1)
if not triton_client.is_model_ready(FLAGS.model):
print("FAILED : is_model_ready")
sys.exit(1)
if FLAGS.model_info:
# Model metadata
try:
metadata = triton_client.get_model_metadata(FLAGS.model)
print(metadata)
except InferenceServerException as ex:
if "Request for unknown model" not in ex.message():
print("FAILED : get_model_metadata")
print("Got: {}".format(ex.message()))
sys.exit(1)
else:
print("FAILED : get_model_metadata")
sys.exit(1)
# Model configuration
try:
config = triton_client.get_model_config(FLAGS.model)
if not (config.config.name == FLAGS.model):
print("FAILED: get_model_config")
sys.exit(1)
print(config)
except InferenceServerException as ex:
print("FAILED : get_model_config")
print("Got: {}".format(ex.message()))
sys.exit(1)
# DUMMY MODE
if FLAGS.mode == 'dummy':
print("Running in 'dummy' mode")
print("Creating emtpy buffer filled with ones...")
inputs = []
outputs = []
inputs.append(grpcclient.InferInput(INPUT_NAMES[0], [1, 3, FLAGS.width, FLAGS.height], "FP32"))
inputs[0].set_data_from_numpy(np.ones(shape=(1, 3, FLAGS.width, FLAGS.height), dtype=np.float32))
outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[0]))
outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[1]))
outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[2]))
outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[3]))
print("Invoking inference...")
results = triton_client.infer(model_name=FLAGS.model,
inputs=inputs,
outputs=outputs,
client_timeout=FLAGS.client_timeout)
if FLAGS.model_info:
statistics = triton_client.get_inference_statistics(model_name=FLAGS.model)
if len(statistics.model_stats) != 1:
print("FAILED: get_inference_statistics")
sys.exit(1)
print(statistics)
print("Done")
for output in OUTPUT_NAMES:
result = results.as_numpy(output)
print(f"Received result buffer \"{output}\" of size {result.shape}")
print(f"Naive buffer sum: {np.sum(result)}")
# IMAGE MODE
if FLAGS.mode == 'image':
print("Running in 'image' mode")
if not FLAGS.input:
print("FAILED: no input image")
sys.exit(1)
inputs = []
outputs = []
inputs.append(grpcclient.InferInput(INPUT_NAMES[0], [1, 3, FLAGS.width, FLAGS.height], "FP32"))
outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[0]))
outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[1]))
outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[2]))
outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[3]))
print("Creating buffer from image file...")
input_image = cv2.imread(str(FLAGS.input))
if input_image is None:
print(f"FAILED: could not load input image {str(FLAGS.input)}")
sys.exit(1)
input_image_buffer = preprocess(input_image, [FLAGS.width, FLAGS.height])
input_image_buffer = np.expand_dims(input_image_buffer, axis=0)
inputs[0].set_data_from_numpy(input_image_buffer)
print("Invoking inference...")
results = triton_client.infer(model_name=FLAGS.model,
inputs=inputs,
outputs=outputs,
client_timeout=FLAGS.client_timeout)
if FLAGS.model_info:
statistics = triton_client.get_inference_statistics(model_name=FLAGS.model)
if len(statistics.model_stats) != 1:
print("FAILED: get_inference_statistics")
sys.exit(1)
print(statistics)
print("Done")
for output in OUTPUT_NAMES:
result = results.as_numpy(output)
print(f"Received result buffer \"{output}\" of size {result.shape}")
print(f"Naive buffer sum: {np.sum(result)}")
num_dets = results.as_numpy(OUTPUT_NAMES[0])
det_boxes = results.as_numpy(OUTPUT_NAMES[1])
det_scores = results.as_numpy(OUTPUT_NAMES[2])
det_classes = results.as_numpy(OUTPUT_NAMES[3])
detected_objects = postprocess(num_dets, det_boxes, det_scores, det_classes, input_image.shape[1], input_image.shape[0], [FLAGS.width, FLAGS.height])
print(f"Detected objects: {len(detected_objects)}")
for box in detected_objects:
print(f"{COCOLabels(box.classID).name}: {box.confidence}")
input_image = render_box(input_image, box.box(), color=tuple(RAND_COLORS[box.classID % 64].tolist()))
size = get_text_size(input_image, f"{COCOLabels(box.classID).name}: {box.confidence:.2f}", normalised_scaling=0.6)
input_image = render_filled_box(input_image, (box.x1 - 3, box.y1 - 3, box.x1 + size[0], box.y1 + size[1]), color=(220, 220, 220))
input_image = render_text(input_image, f"{COCOLabels(box.classID).name}: {box.confidence:.2f}", (box.x1, box.y1), color=(30, 30, 30), normalised_scaling=0.5)
if FLAGS.out:
cv2.imwrite(FLAGS.out, input_image)
print(f"Saved result to {FLAGS.out}")
else:
cv2.imshow('image', input_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
# VIDEO MODE
if FLAGS.mode == 'video':
print("Running in 'video' mode")
if not FLAGS.input:
print("FAILED: no input video")
sys.exit(1)
inputs = []
outputs = []
inputs.append(grpcclient.InferInput(INPUT_NAMES[0], [1, 3, FLAGS.width, FLAGS.height], "FP32"))
outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[0]))
outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[1]))
outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[2]))
outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[3]))
print("Opening input video stream...")
cap = cv2.VideoCapture(FLAGS.input)
if not cap.isOpened():
print(f"FAILED: cannot open video {FLAGS.input}")
sys.exit(1)
counter = 0
out = None
print("Invoking inference...")
while True:
ret, frame = cap.read()
if not ret:
print("failed to fetch next frame")
break
if counter == 0 and FLAGS.out:
print("Opening output video stream...")
fourcc = cv2.VideoWriter_fourcc('M', 'P', '4', 'V')
out = cv2.VideoWriter(FLAGS.out, fourcc, FLAGS.fps, (frame.shape[1], frame.shape[0]))
input_image_buffer = preprocess(frame, [FLAGS.width, FLAGS.height])
input_image_buffer = np.expand_dims(input_image_buffer, axis=0)
inputs[0].set_data_from_numpy(input_image_buffer)
results = triton_client.infer(model_name=FLAGS.model,
inputs=inputs,
outputs=outputs,
client_timeout=FLAGS.client_timeout)
num_dets = results.as_numpy("num_dets")
det_boxes = results.as_numpy("det_boxes")
det_scores = results.as_numpy("det_scores")
det_classes = results.as_numpy("det_classes")
detected_objects = postprocess(num_dets, det_boxes, det_scores, det_classes, frame.shape[1], frame.shape[0], [FLAGS.width, FLAGS.height])
print(f"Frame {counter}: {len(detected_objects)} objects")
counter += 1
for box in detected_objects:
print(f"{COCOLabels(box.classID).name}: {box.confidence}")
frame = render_box(frame, box.box(), color=tuple(RAND_COLORS[box.classID % 64].tolist()))
size = get_text_size(frame, f"{COCOLabels(box.classID).name}: {box.confidence:.2f}", normalised_scaling=0.6)
frame = render_filled_box(frame, (box.x1 - 3, box.y1 - 3, box.x1 + size[0], box.y1 + size[1]), color=(220, 220, 220))
frame = render_text(frame, f"{COCOLabels(box.classID).name}: {box.confidence:.2f}", (box.x1, box.y1), color=(30, 30, 30), normalised_scaling=0.5)
if FLAGS.out:
out.write(frame)
else:
cv2.imshow('image', frame)
if cv2.waitKey(1) == ord('q'):
break
if FLAGS.model_info:
statistics = triton_client.get_inference_statistics(model_name=FLAGS.model)
if len(statistics.model_stats) != 1:
print("FAILED: get_inference_statistics")
sys.exit(1)
print(statistics)
print("Done")
cap.release()
if FLAGS.out:
out.release()
else:
cv2.destroyAllWindows()
================================================
FILE: deploy/triton-inference-server/labels.py
================================================
from enum import Enum
class COCOLabels(Enum):
PERSON = 0
BICYCLE = 1
CAR = 2
MOTORBIKE = 3
AEROPLANE = 4
BUS = 5
TRAIN = 6
TRUCK = 7
BOAT = 8
TRAFFIC_LIGHT = 9
FIRE_HYDRANT = 10
STOP_SIGN = 11
PARKING_METER = 12
BENCH = 13
BIRD = 14
CAT = 15
DOG = 16
HORSE = 17
SHEEP = 18
COW = 19
ELEPHANT = 20
BEAR = 21
ZEBRA = 22
GIRAFFE = 23
BACKPACK = 24
UMBRELLA = 25
HANDBAG = 26
TIE = 27
SUITCASE = 28
FRISBEE = 29
SKIS = 30
SNOWBOARD = 31
SPORTS_BALL = 32
KITE = 33
BASEBALL_BAT = 34
BASEBALL_GLOVE = 35
SKATEBOARD = 36
SURFBOARD = 37
TENNIS_RACKET = 38
BOTTLE = 39
WINE_GLASS = 40
CUP = 41
FORK = 42
KNIFE = 43
SPOON = 44
BOWL = 45
BANANA = 46
APPLE = 47
SANDWICH = 48
ORANGE = 49
BROCCOLI = 50
CARROT = 51
HOT_DOG = 52
PIZZA = 53
DONUT = 54
CAKE = 55
CHAIR = 56
SOFA = 57
POTTEDPLANT = 58
BED = 59
DININGTABLE = 60
TOILET = 61
TVMONITOR = 62
LAPTOP = 63
MOUSE = 64
REMOTE = 65
KEYBOARD = 66
CELL_PHONE = 67
MICROWAVE = 68
OVEN = 69
TOASTER = 70
SINK = 71
REFRIGERATOR = 72
BOOK = 73
CLOCK = 74
VASE = 75
SCISSORS = 76
TEDDY_BEAR = 77
HAIR_DRIER = 78
TOOTHBRUSH = 79
================================================
FILE: deploy/triton-inference-server/processing.py
================================================
from boundingbox import BoundingBox
import cv2
import numpy as np
def preprocess(img, input_shape, letter_box=True):
if letter_box:
img_h, img_w, _ = img.shape
new_h, new_w = input_shape[0], input_shape[1]
offset_h, offset_w = 0, 0
if (new_w / img_w) <= (new_h / img_h):
new_h = int(img_h * new_w / img_w)
offset_h = (input_shape[0] - new_h) // 2
else:
new_w = int(img_w * new_h / img_h)
offset_w = (input_shape[1] - new_w) // 2
resized = cv2.resize(img, (new_w, new_h))
img = np.full((input_shape[0], input_shape[1], 3), 127, dtype=np.uint8)
img[offset_h:(offset_h + new_h), offset_w:(offset_w + new_w), :] = resized
else:
img = cv2.resize(img, (input_shape[1], input_shape[0]))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = img.transpose((2, 0, 1)).astype(np.float32)
img /= 255.0
return img
def postprocess(num_dets, det_boxes, det_scores, det_classes, img_w, img_h, input_shape, letter_box=True):
boxes = det_boxes[0, :num_dets[0][0]] / np.array([input_shape[0], input_shape[1], input_shape[0], input_shape[1]], dtype=np.float32)
scores = det_scores[0, :num_dets[0][0]]
classes = det_classes[0, :num_dets[0][0]].astype(np.int)
old_h, old_w = img_h, img_w
offset_h, offset_w = 0, 0
if letter_box:
if (img_w / input_shape[1]) >= (img_h / input_shape[0]):
old_h = int(input_shape[0] * img_w / input_shape[1])
offset_h = (old_h - img_h) // 2
else:
old_w = int(input_shape[1] * img_h / input_shape[0])
offset_w = (old_w - img_w) // 2
boxes = boxes * np.array([old_w, old_h, old_w, old_h], dtype=np.float32)
if letter_box:
boxes -= np.array([offset_w, offset_h, offset_w, offset_h], dtype=np.float32)
boxes = boxes.astype(np.int)
detected_objects = []
for box, score, label in zip(boxes, scores, classes):
detected_objects.append(BoundingBox(label, score, box[0], box[2], box[1], box[3], img_w, img_h))
return detected_objects
================================================
FILE: deploy/triton-inference-server/render.py
================================================
import numpy as np
import cv2
from math import sqrt
_LINE_THICKNESS_SCALING = 500.0
np.random.seed(0)
RAND_COLORS = np.random.randint(50, 255, (64, 3), "int") # used for class visu
RAND_COLORS[0] = [220, 220, 220]
def render_box(img, box, color=(200, 200, 200)):
"""
Render a box. Calculates scaling and thickness automatically.
:param img: image to render into
:param box: (x1, y1, x2, y2) - box coordinates
:param color: (b, g, r) - box color
:return: updated image
"""
x1, y1, x2, y2 = box
thickness = int(
round(
(img.shape[0] * img.shape[1])
/ (_LINE_THICKNESS_SCALING * _LINE_THICKNESS_SCALING)
)
)
thickness = max(1, thickness)
img = cv2.rectangle(
img,
(int(x1), int(y1)),
(int(x2), int(y2)),
color,
thickness=thickness
)
return img
def render_filled_box(img, box, color=(200, 200, 200)):
"""
Render a box. Calculates scaling and thickness automatically.
:param img: image to render into
:param box: (x1, y1, x2, y2) - box coordinates
:param color: (b, g, r) - box color
:return: updated image
"""
x1, y1, x2, y2 = box
img = cv2.rectangle(
img,
(int(x1), int(y1)),
(int(x2), int(y2)),
color,
thickness=cv2.FILLED
)
return img
_TEXT_THICKNESS_SCALING = 700.0
_TEXT_SCALING = 520.0
def get_text_size(img, text, normalised_scaling=1.0):
"""
Get calculated text size (as box width and height)
:param img: image reference, used to determine appropriate text scaling
:param text: text to display
:param normalised_scaling: additional normalised scaling. Default 1.0.
:return: (width, height) - width and height of text box
"""
thickness = int(
round(
(img.shape[0] * img.shape[1])
/ (_TEXT_THICKNESS_SCALING * _TEXT_THICKNESS_SCALING)
)
* normalised_scaling
)
thickness = max(1, thickness)
scaling = img.shape[0] / _TEXT_SCALING * normalised_scaling
return cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, scaling, thickness)[0]
def render_text(img, text, pos, color=(200, 200, 200), normalised_scaling=1.0):
"""
Render a text into the image. Calculates scaling and thickness automatically.
:param img: image to render into
:param text: text to display
:param pos: (x, y) - upper left coordinates of render position
:param color: (b, g, r) - text color
:param normalised_scaling: additional normalised scaling. Default 1.0.
:return: updated image
"""
x, y = pos
thickness = int(
round(
(img.shape[0] * img.shape[1])
/ (_TEXT_THICKNESS_SCALING * _TEXT_THICKNESS_SCALING)
)
* normalised_scaling
)
thickness = max(1, thickness)
scaling = img.shape[0] / _TEXT_SCALING * normalised_scaling
size = get_text_size(img, text, normalised_scaling)
cv2.putText(
img,
text,
(int(x), int(y + size[1])),
cv2.FONT_HERSHEY_SIMPLEX,
scaling,
color,
thickness=thickness,
)
return img
================================================
FILE: detect.py
================================================
[File too large to display: 9.1 KB]
================================================
FILE: export.py
================================================
import argparse
import sys
import time
import warnings
sys.path.append('./') # to run '$ python *.py' files in subdirectories
import torch
import torch.nn as nn
from torch.utils.mobile_optimizer import optimize_for_mobile
import models
from models.experimental import attempt_load, End2End
from utils.activations import Hardswish, SiLU
from utils.general import set_logging, check_img_size
from utils.torch_utils import select_device
from utils.add_nms import RegisterNMS
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default='./yolor-csp-c.pt', help='weights path')
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes')
parser.add_argument('--dynamic-batch', action='store_true', help='dynamic batch onnx for tensorrt and onnx-runtime')
parser.add_argument('--grid', action='store_true', help='export Detect() layer grid')
parser.add_argument('--end2end', action='store_true', help='export end2end onnx')
parser.add_argument('--max-wh', type=int, default=None, help='None for tensorrt nms, int value for onnx-runtime nms')
parser.add_argument('--topk-all', type=int, default=100, help='topk objects for every images')
parser.add_argument('--iou-thres', type=float, default=0.45, help='iou threshold for NMS')
parser.add_argument('--conf-thres', type=float, default=0.25, help='conf threshold for NMS')
parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--simplify', action='store_true', help='simplify onnx model')
parser.add_argument('--include-nms', action='store_true', help='export end2end onnx')
parser.add_argument('--fp16', action='store_true', help='CoreML FP16 half-precision export')
parser.add_argument('--int8', action='store_true', help='CoreML INT8 quantization')
opt = parser.parse_args()
opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand
opt.dynamic = opt.dynamic and not opt.end2end
opt.dynamic = False if opt.dynamic_batch else opt.dynamic
print(opt)
set_logging()
t = time.time()
# Load PyTorch model
device = select_device(opt.device)
model = attempt_load(opt.weights, map_location=device) # load FP32 model
labels = model.names
# Checks
gs = int(max(model.stride)) # grid size (max stride)
opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples
# Input
img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection
# Update model
for k, m in model.named_modules():
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
if isinstance(m, models.common.Conv): # assign export-friendly activations
if isinstance(m.act, nn.Hardswish):
m.act = Hardswish()
elif isinstance(m.act, nn.SiLU):
m.act = SiLU()
# elif isinstance(m, models.yolo.Detect):
# m.forward = m.forward_export # assign forward (optional)
model.model[-1].export = not opt.grid # set Detect() layer grid export
y = model(img) # dry run
if opt.include_nms:
model.model[-1].include_nms = True
y = None
# TorchScript export
try:
print('\nStarting TorchScript export with torch %s...' % torch.__version__)
f = opt.weights.replace('.pt', '.torchscript.pt') # filename
ts = torch.jit.trace(model, img, strict=False)
ts.save(f)
print('TorchScript export success, saved as %s' % f)
except Exception as e:
print('TorchScript export failure: %s' % e)
# CoreML export
try:
import coremltools as ct
gitextract_uttfz7r5/
├── .gitignore
├── LICENSE.md
├── README.md
├── cfg/
│ ├── baseline/
│ │ ├── r50-csp.yaml
│ │ ├── x50-csp.yaml
│ │ ├── yolor-csp-x.yaml
│ │ ├── yolor-csp.yaml
│ │ ├── yolor-d6.yaml
│ │ ├── yolor-e6.yaml
│ │ ├── yolor-p6.yaml
│ │ ├── yolor-w6.yaml
│ │ ├── yolov3-spp.yaml
│ │ ├── yolov3.yaml
│ │ └── yolov4-csp.yaml
│ ├── deploy/
│ │ ├── yolov7-d6.yaml
│ │ ├── yolov7-e6.yaml
│ │ ├── yolov7-e6e.yaml
│ │ ├── yolov7-tiny-silu.yaml
│ │ ├── yolov7-tiny.yaml
│ │ ├── yolov7-w6.yaml
│ │ ├── yolov7.yaml
│ │ └── yolov7x.yaml
│ └── training/
│ ├── yolov7-d6.yaml
│ ├── yolov7-e6.yaml
│ ├── yolov7-e6e.yaml
│ ├── yolov7-tiny.yaml
│ ├── yolov7-w6.yaml
│ ├── yolov7.yaml
│ └── yolov7x.yaml
├── data/
│ ├── coco.yaml
│ ├── hyp.scratch.custom.yaml
│ ├── hyp.scratch.p5.yaml
│ ├── hyp.scratch.p6.yaml
│ └── hyp.scratch.tiny.yaml
├── deploy/
│ └── triton-inference-server/
│ ├── README.md
│ ├── boundingbox.py
│ ├── client.py
│ ├── labels.py
│ ├── processing.py
│ └── render.py
├── detect.py
├── export.py
├── hubconf.py
├── models/
│ ├── __init__.py
│ ├── common.py
│ ├── experimental.py
│ └── yolo.py
├── requirements.txt
├── scripts/
│ └── get_coco.sh
├── test.py
├── tools/
│ ├── YOLOv7-Dynamic-Batch-ONNXRUNTIME.ipynb
│ ├── YOLOv7-Dynamic-Batch-TENSORRT.ipynb
│ ├── YOLOv7CoreML.ipynb
│ ├── YOLOv7onnx.ipynb
│ ├── YOLOv7trt.ipynb
│ ├── compare_YOLOv7_vs_YOLOv5m6.ipynb
│ ├── compare_YOLOv7_vs_YOLOv5m6_half.ipynb
│ ├── compare_YOLOv7_vs_YOLOv5s6.ipynb
│ ├── compare_YOLOv7e6_vs_YOLOv5x6.ipynb
│ ├── compare_YOLOv7e6_vs_YOLOv5x6_half.ipynb
│ ├── instance.ipynb
│ ├── keypoint.ipynb
│ ├── reparameterization.ipynb
│ └── visualization.ipynb
├── train.py
├── train_aux.py
└── utils/
├── __init__.py
├── activations.py
├── add_nms.py
├── autoanchor.py
├── aws/
│ ├── __init__.py
│ ├── mime.sh
│ ├── resume.py
│ └── userdata.sh
├── datasets.py
├── general.py
├── google_app_engine/
│ ├── Dockerfile
│ ├── additional_requirements.txt
│ └── app.yaml
├── google_utils.py
├── loss.py
├── metrics.py
├── plots.py
├── torch_utils.py
└── wandb_logging/
├── __init__.py
├── log_dataset.py
└── wandb_utils.py
SYMBOL INDEX (330 symbols across 10 files)
FILE: deploy/triton-inference-server/boundingbox.py
class BoundingBox (line 1) | class BoundingBox:
method __init__ (line 2) | def __init__(self, classID, confidence, x1, x2, y1, y2, image_width, i...
method box (line 14) | def box(self):
method width (line 17) | def width(self):
method height (line 20) | def height(self):
method center_absolute (line 23) | def center_absolute(self):
method center_normalized (line 26) | def center_normalized(self):
method size_absolute (line 29) | def size_absolute(self):
method size_normalized (line 32) | def size_normalized(self):
FILE: deploy/triton-inference-server/labels.py
class COCOLabels (line 3) | class COCOLabels(Enum):
FILE: deploy/triton-inference-server/processing.py
function preprocess (line 6) | def preprocess(img, input_shape, letter_box=True):
function postprocess (line 28) | def postprocess(num_dets, det_boxes, det_scores, det_classes, img_w, img...
FILE: deploy/triton-inference-server/render.py
function render_box (line 13) | def render_box(img, box, color=(200, 200, 200)):
function render_filled_box (line 38) | def render_filled_box(img, box, color=(200, 200, 200)):
function get_text_size (line 60) | def get_text_size(img, text, normalised_scaling=1.0):
function render_text (line 80) | def render_text(img, text, pos, color=(200, 200, 200), normalised_scalin...
FILE: hubconf.py
function create (line 22) | def create(name, pretrained, channels, classes, autoshape):
function custom (line 57) | def custom(path_or_model='path/to/model.pt', autoshape=True):
function yolov7 (line 81) | def yolov7(pretrained=True, channels=3, classes=80, autoshape=True):
FILE: models/common.py
function autopad (line 23) | def autopad(k, p=None): # kernel, padding
class MP (line 30) | class MP(nn.Module):
method __init__ (line 31) | def __init__(self, k=2):
method forward (line 35) | def forward(self, x):
class SP (line 39) | class SP(nn.Module):
method __init__ (line 40) | def __init__(self, k=3, s=1):
method forward (line 44) | def forward(self, x):
class ReOrg (line 48) | class ReOrg(nn.Module):
method __init__ (line 49) | def __init__(self):
method forward (line 52) | def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
class Concat (line 56) | class Concat(nn.Module):
method __init__ (line 57) | def __init__(self, dimension=1):
method forward (line 61) | def forward(self, x):
class Chuncat (line 65) | class Chuncat(nn.Module):
method __init__ (line 66) | def __init__(self, dimension=1):
method forward (line 70) | def forward(self, x):
class Shortcut (line 80) | class Shortcut(nn.Module):
method __init__ (line 81) | def __init__(self, dimension=0):
method forward (line 85) | def forward(self, x):
class Foldcut (line 89) | class Foldcut(nn.Module):
method __init__ (line 90) | def __init__(self, dimension=0):
method forward (line 94) | def forward(self, x):
class Conv (line 99) | class Conv(nn.Module):
method __init__ (line 101) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in,...
method forward (line 107) | def forward(self, x):
method fuseforward (line 110) | def fuseforward(self, x):
class RobustConv (line 114) | class RobustConv(nn.Module):
method __init__ (line 116) | def __init__(self, c1, c2, k=7, s=1, p=None, g=1, act=True, layer_scal...
method forward (line 122) | def forward(self, x):
class RobustConv2 (line 130) | class RobustConv2(nn.Module):
method __init__ (line 132) | def __init__(self, c1, c2, k=7, s=4, p=None, g=1, act=True, layer_scal...
method forward (line 140) | def forward(self, x):
function DWConv (line 147) | def DWConv(c1, c2, k=1, s=1, act=True):
class GhostConv (line 152) | class GhostConv(nn.Module):
method __init__ (line 154) | def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out,...
method forward (line 160) | def forward(self, x):
class Stem (line 165) | class Stem(nn.Module):
method __init__ (line 167) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in,...
method forward (line 176) | def forward(self, x):
class DownC (line 181) | class DownC(nn.Module):
method __init__ (line 183) | def __init__(self, c1, c2, n=1, k=2):
method forward (line 191) | def forward(self, x):
class SPP (line 195) | class SPP(nn.Module):
method __init__ (line 197) | def __init__(self, c1, c2, k=(5, 9, 13)):
method forward (line 204) | def forward(self, x):
class Bottleneck (line 209) | class Bottleneck(nn.Module):
method __init__ (line 211) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_ou...
method forward (line 218) | def forward(self, x):
class Res (line 222) | class Res(nn.Module):
method __init__ (line 224) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_ou...
method forward (line 232) | def forward(self, x):
class ResX (line 236) | class ResX(Res):
method __init__ (line 238) | def __init__(self, c1, c2, shortcut=True, g=32, e=0.5): # ch_in, ch_o...
class Ghost (line 243) | class Ghost(nn.Module):
method __init__ (line 245) | def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
method forward (line 254) | def forward(self, x):
class SPPCSPC (line 262) | class SPPCSPC(nn.Module):
method __init__ (line 264) | def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 1...
method forward (line 276) | def forward(self, x):
class GhostSPPCSPC (line 282) | class GhostSPPCSPC(SPPCSPC):
method __init__ (line 284) | def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 1...
class GhostStem (line 296) | class GhostStem(Stem):
method __init__ (line 298) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in,...
class BottleneckCSPA (line 307) | class BottleneckCSPA(nn.Module):
method __init__ (line 309) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
method forward (line 317) | def forward(self, x):
class BottleneckCSPB (line 323) | class BottleneckCSPB(nn.Module):
method __init__ (line 325) | def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in,...
method forward (line 333) | def forward(self, x):
class BottleneckCSPC (line 340) | class BottleneckCSPC(nn.Module):
method __init__ (line 342) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
method forward (line 351) | def forward(self, x):
class ResCSPA (line 357) | class ResCSPA(BottleneckCSPA):
method __init__ (line 359) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
class ResCSPB (line 365) | class ResCSPB(BottleneckCSPB):
method __init__ (line 367) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
class ResCSPC (line 373) | class ResCSPC(BottleneckCSPC):
method __init__ (line 375) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
class ResXCSPA (line 381) | class ResXCSPA(ResCSPA):
method __init__ (line 383) | def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in,...
class ResXCSPB (line 389) | class ResXCSPB(ResCSPB):
method __init__ (line 391) | def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in,...
class ResXCSPC (line 397) | class ResXCSPC(ResCSPC):
method __init__ (line 399) | def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in,...
class GhostCSPA (line 405) | class GhostCSPA(BottleneckCSPA):
method __init__ (line 407) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
class GhostCSPB (line 413) | class GhostCSPB(BottleneckCSPB):
method __init__ (line 415) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
class GhostCSPC (line 421) | class GhostCSPC(BottleneckCSPC):
method __init__ (line 423) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
class ImplicitA (line 433) | class ImplicitA(nn.Module):
method __init__ (line 434) | def __init__(self, channel, mean=0., std=.02):
method forward (line 442) | def forward(self, x):
class ImplicitM (line 446) | class ImplicitM(nn.Module):
method __init__ (line 447) | def __init__(self, channel, mean=1., std=.02):
method forward (line 455) | def forward(self, x):
class RepConv (line 463) | class RepConv(nn.Module):
method __init__ (line 467) | def __init__(self, c1, c2, k=3, s=1, p=None, g=1, act=True, deploy=Fal...
method forward (line 498) | def forward(self, inputs):
method get_equivalent_kernel_bias (line 509) | def get_equivalent_kernel_bias(self):
method _pad_1x1_to_3x3_tensor (line 518) | def _pad_1x1_to_3x3_tensor(self, kernel1x1):
method _fuse_bn_tensor (line 524) | def _fuse_bn_tensor(self, branch):
method repvgg_convert (line 554) | def repvgg_convert(self):
method fuse_conv_bn (line 561) | def fuse_conv_bn(self, conv, bn):
method fuse_repvgg_block (line 584) | def fuse_repvgg_block(self):
class RepBottleneck (line 646) | class RepBottleneck(Bottleneck):
method __init__ (line 648) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_ou...
class RepBottleneckCSPA (line 654) | class RepBottleneckCSPA(BottleneckCSPA):
method __init__ (line 656) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
class RepBottleneckCSPB (line 662) | class RepBottleneckCSPB(BottleneckCSPB):
method __init__ (line 664) | def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in,...
class RepBottleneckCSPC (line 670) | class RepBottleneckCSPC(BottleneckCSPC):
method __init__ (line 672) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
class RepRes (line 678) | class RepRes(Res):
method __init__ (line 680) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_ou...
class RepResCSPA (line 686) | class RepResCSPA(ResCSPA):
method __init__ (line 688) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
class RepResCSPB (line 694) | class RepResCSPB(ResCSPB):
method __init__ (line 696) | def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in,...
class RepResCSPC (line 702) | class RepResCSPC(ResCSPC):
method __init__ (line 704) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
class RepResX (line 710) | class RepResX(ResX):
method __init__ (line 712) | def __init__(self, c1, c2, shortcut=True, g=32, e=0.5): # ch_in, ch_o...
class RepResXCSPA (line 718) | class RepResXCSPA(ResXCSPA):
method __init__ (line 720) | def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in,...
class RepResXCSPB (line 726) | class RepResXCSPB(ResXCSPB):
method __init__ (line 728) | def __init__(self, c1, c2, n=1, shortcut=False, g=32, e=0.5): # ch_in...
class RepResXCSPC (line 734) | class RepResXCSPC(ResXCSPC):
method __init__ (line 736) | def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in,...
class TransformerLayer (line 746) | class TransformerLayer(nn.Module):
method __init__ (line 748) | def __init__(self, c, num_heads):
method forward (line 757) | def forward(self, x):
class TransformerBlock (line 763) | class TransformerBlock(nn.Module):
method __init__ (line 765) | def __init__(self, c1, c2, num_heads, num_layers):
method forward (line 774) | def forward(self, x):
class Focus (line 796) | class Focus(nn.Module):
method __init__ (line 798) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in,...
method forward (line 803) | def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
class SPPF (line 808) | class SPPF(nn.Module):
method __init__ (line 810) | def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
method forward (line 817) | def forward(self, x):
class Contract (line 824) | class Contract(nn.Module):
method __init__ (line 826) | def __init__(self, gain=2):
method forward (line 830) | def forward(self, x):
class Expand (line 838) | class Expand(nn.Module):
method __init__ (line 840) | def __init__(self, gain=2):
method forward (line 844) | def forward(self, x):
class NMS (line 852) | class NMS(nn.Module):
method __init__ (line 858) | def __init__(self):
method forward (line 861) | def forward(self, x):
class autoShape (line 865) | class autoShape(nn.Module):
method __init__ (line 871) | def __init__(self, model):
method autoshape (line 875) | def autoshape(self):
method forward (line 880) | def forward(self, imgs, size=640, augment=False, profile=False):
class Detections (line 935) | class Detections:
method __init__ (line 937) | def __init__(self, imgs, pred, files, times=None, names=None, shape=No...
method display (line 953) | def display(self, pprint=False, show=False, save=False, render=False, ...
method print (line 977) | def print(self):
method show (line 981) | def show(self):
method save (line 984) | def save(self, save_dir='runs/hub/exp'):
method render (line 989) | def render(self):
method pandas (line 993) | def pandas(self):
method tolist (line 1003) | def tolist(self):
method __len__ (line 1011) | def __len__(self):
class Classify (line 1015) | class Classify(nn.Module):
method __init__ (line 1017) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, k...
method forward (line 1023) | def forward(self, x):
function transI_fusebn (line 1032) | def transI_fusebn(kernel, bn):
class ConvBN (line 1038) | class ConvBN(nn.Module):
method __init__ (line 1039) | def __init__(self, in_channels, out_channels, kernel_size,
method forward (line 1054) | def forward(self, x):
method switch_to_deploy (line 1060) | def switch_to_deploy(self):
class OREPA_3x3_RepConv (line 1072) | class OREPA_3x3_RepConv(nn.Module):
method __init__ (line 1074) | def __init__(self, in_channels, out_channels, kernel_size,
method fre_init (line 1157) | def fre_init(self):
method weight_gen (line 1170) | def weight_gen(self):
method dwsc2full (line 1206) | def dwsc2full(self, weight_dw, weight_pw, groups):
method forward (line 1218) | def forward(self, inputs):
class RepConv_OREPA (line 1224) | class RepConv_OREPA(nn.Module):
method __init__ (line 1226) | def __init__(self, c1, c2, k=3, s=1, padding=1, dilation=1, groups=1, ...
method forward (line 1263) | def forward(self, inputs):
method get_custom_L2 (line 1290) | def get_custom_L2(self):
method get_equivalent_kernel_bias (line 1301) | def get_equivalent_kernel_bias(self):
method _pad_1x1_to_3x3_tensor (line 1307) | def _pad_1x1_to_3x3_tensor(self, kernel1x1):
method _fuse_bn_tensor (line 1313) | def _fuse_bn_tensor(self, branch):
method switch_to_deploy (line 1345) | def switch_to_deploy(self):
class WindowAttention (line 1367) | class WindowAttention(nn.Module):
method __init__ (line 1369) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scal...
method forward (line 1403) | def forward(self, x, mask=None):
class Mlp (line 1437) | class Mlp(nn.Module):
method __init__ (line 1439) | def __init__(self, in_features, hidden_features=None, out_features=Non...
method forward (line 1448) | def forward(self, x):
function window_partition (line 1456) | def window_partition(x, window_size):
function window_reverse (line 1464) | def window_reverse(windows, window_size, H, W):
class SwinTransformerLayer (line 1472) | class SwinTransformerLayer(nn.Module):
method __init__ (line 1474) | def __init__(self, dim, num_heads, window_size=8, shift_size=0,
method create_mask (line 1499) | def create_mask(self, H, W):
method forward (line 1521) | def forward(self, x):
class SwinTransformerBlock (line 1584) | class SwinTransformerBlock(nn.Module):
method __init__ (line 1585) | def __init__(self, c1, c2, num_heads, num_layers, window_size=8):
method forward (line 1595) | def forward(self, x):
class STCSPA (line 1602) | class STCSPA(nn.Module):
method __init__ (line 1604) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
method forward (line 1614) | def forward(self, x):
class STCSPB (line 1620) | class STCSPB(nn.Module):
method __init__ (line 1622) | def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in,...
method forward (line 1632) | def forward(self, x):
class STCSPC (line 1639) | class STCSPC(nn.Module):
method __init__ (line 1641) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
method forward (line 1652) | def forward(self, x):
class WindowAttention_v2 (line 1662) | class WindowAttention_v2(nn.Module):
method __init__ (line 1664) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_dr...
method forward (line 1723) | def forward(self, x, mask=None):
method extra_repr (line 1764) | def extra_repr(self) -> str:
method flops (line 1768) | def flops(self, N):
class Mlp_v2 (line 1781) | class Mlp_v2(nn.Module):
method __init__ (line 1782) | def __init__(self, in_features, hidden_features=None, out_features=Non...
method forward (line 1791) | def forward(self, x):
function window_partition_v2 (line 1800) | def window_partition_v2(x, window_size):
function window_reverse_v2 (line 1808) | def window_reverse_v2(windows, window_size, H, W):
class SwinTransformerLayer_v2 (line 1816) | class SwinTransformerLayer_v2(nn.Module):
method __init__ (line 1818) | def __init__(self, dim, num_heads, window_size=7, shift_size=0,
method create_mask (line 1845) | def create_mask(self, H, W):
method forward (line 1867) | def forward(self, x):
method extra_repr (line 1927) | def extra_repr(self) -> str:
method flops (line 1931) | def flops(self):
class SwinTransformer2Block (line 1946) | class SwinTransformer2Block(nn.Module):
method __init__ (line 1947) | def __init__(self, c1, c2, num_heads, num_layers, window_size=7):
method forward (line 1957) | def forward(self, x):
class ST2CSPA (line 1964) | class ST2CSPA(nn.Module):
method __init__ (line 1966) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
method forward (line 1976) | def forward(self, x):
class ST2CSPB (line 1982) | class ST2CSPB(nn.Module):
method __init__ (line 1984) | def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in,...
method forward (line 1994) | def forward(self, x):
class ST2CSPC (line 2001) | class ST2CSPC(nn.Module):
method __init__ (line 2003) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
method forward (line 2014) | def forward(self, x):
FILE: models/experimental.py
class CrossConv (line 10) | class CrossConv(nn.Module):
method __init__ (line 12) | def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
method forward (line 20) | def forward(self, x):
class Sum (line 24) | class Sum(nn.Module):
method __init__ (line 26) | def __init__(self, n, weight=False): # n: number of inputs
method forward (line 33) | def forward(self, x):
class MixConv2d (line 45) | class MixConv2d(nn.Module):
method __init__ (line 47) | def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
method forward (line 65) | def forward(self, x):
class Ensemble (line 69) | class Ensemble(nn.ModuleList):
method __init__ (line 71) | def __init__(self):
method forward (line 74) | def forward(self, x, augment=False):
class ORT_NMS (line 87) | class ORT_NMS(torch.autograd.Function):
method forward (line 90) | def forward(ctx,
method symbolic (line 107) | def symbolic(g, boxes, scores, max_output_boxes_per_class, iou_thresho...
class TRT_NMS (line 111) | class TRT_NMS(torch.autograd.Function):
method forward (line 114) | def forward(
method symbolic (line 134) | def symbolic(g,
class ONNX_ORT (line 159) | class ONNX_ORT(nn.Module):
method __init__ (line 161) | def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_...
method forward (line 173) | def forward(self, x):
class ONNX_TRT (line 195) | class ONNX_TRT(nn.Module):
method __init__ (line 197) | def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_...
method forward (line 210) | def forward(self, x):
class End2End (line 226) | class End2End(nn.Module):
method __init__ (line 228) | def __init__(self, model, max_obj=100, iou_thres=0.45, score_thres=0.2...
method forward (line 238) | def forward(self, x):
function attempt_load (line 247) | def attempt_load(weights, map_location=None):
FILE: models/yolo.py
class Detect (line 23) | class Detect(nn.Module):
method __init__ (line 30) | def __init__(self, nc=80, anchors=(), ch=()): # detection layer
method forward (line 42) | def forward(self, x):
method _make_grid (line 80) | def _make_grid(nx=20, ny=20):
method convert (line 84) | def convert(self, z):
class IDetect (line 97) | class IDetect(nn.Module):
method __init__ (line 104) | def __init__(self, nc=80, anchors=(), ch=()): # detection layer
method forward (line 119) | def forward(self, x):
method fuseforward (line 140) | def fuseforward(self, x):
method fuse (line 178) | def fuse(self):
method _make_grid (line 193) | def _make_grid(nx=20, ny=20):
method convert (line 197) | def convert(self, z):
class IKeypoint (line 210) | class IKeypoint(nn.Module):
method __init__ (line 214) | def __init__(self, nc=80, anchors=(), nkpt=17, ch=(), inplace=True, dw...
method forward (line 248) | def forward(self, x):
method _make_grid (line 306) | def _make_grid(nx=20, ny=20):
class IAuxDetect (line 311) | class IAuxDetect(nn.Module):
method __init__ (line 318) | def __init__(self, nc=80, anchors=(), ch=()): # detection layer
method forward (line 334) | def forward(self, x):
method fuseforward (line 364) | def fuseforward(self, x):
method fuse (line 401) | def fuse(self):
method _make_grid (line 416) | def _make_grid(nx=20, ny=20):
method convert (line 420) | def convert(self, z):
class IBin (line 433) | class IBin(nn.Module):
method __init__ (line 437) | def __init__(self, nc=80, anchors=(), ch=(), bin_count=21): # detecti...
method forward (line 460) | def forward(self, x):
method _make_grid (line 503) | def _make_grid(nx=20, ny=20):
class Model (line 508) | class Model(nn.Module):
method __init__ (line 509) | def __init__(self, cfg='yolor-csp-c.yaml', ch=3, nc=None, anchors=None...
method forward (line 581) | def forward(self, x, augment=False, profile=False):
method forward_once (line 601) | def forward_once(self, x, profile=False):
method _initialize_biases (line 633) | def _initialize_biases(self, cf=None): # initialize biases into Detec...
method _initialize_aux_biases (line 643) | def _initialize_aux_biases(self, cf=None): # initialize biases into D...
method _initialize_biases_bin (line 657) | def _initialize_biases_bin(self, cf=None): # initialize biases into D...
method _initialize_biases_kpt (line 672) | def _initialize_biases_kpt(self, cf=None): # initialize biases into D...
method _print_biases (line 682) | def _print_biases(self):
method fuse (line 693) | def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
method nms (line 712) | def nms(self, mode=True): # add or remove NMS module
method autoshape (line 726) | def autoshape(self): # add autoShape module
method info (line 732) | def info(self, verbose=False, img_size=640): # print model information
function parse_model (line 736) | def parse_model(d, ch): # model_dict, input_channels(3)
FILE: test.py
function test (line 21) | def test(data,
FILE: utils/autoanchor.py
function check_anchor_order (line 12) | def check_anchor_order(m):
function check_anchors (line 23) | def check_anchors(dataset, model, thr=4.0, imgsz=640):
function kmean_anchors (line 62) | def kmean_anchors(path='./data/coco.yaml', n=9, img_size=640, thr=4.0, g...
Copy disabled (too large)
Download .json
Condensed preview — 87 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (19,970K chars).
[
{
"path": ".gitignore",
"chars": 4062,
"preview": "# Repo-specific GitIgnore ----------------------------------------------------------------------------------------------"
},
{
"path": "LICENSE.md",
"chars": 35149,
"preview": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free "
},
{
"path": "README.md",
"chars": 14743,
"preview": "# Official YOLOv7\n\nImplementation of paper - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time "
},
{
"path": "cfg/baseline/r50-csp.yaml",
"chars": 1420,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/baseline/x50-csp.yaml",
"chars": 1430,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/baseline/yolor-csp-x.yaml",
"chars": 1596,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.33 # model depth multiple\nwidth_multiple: 1.25 # layer chan"
},
{
"path": "cfg/baseline/yolor-csp.yaml",
"chars": 1594,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/baseline/yolor-d6.yaml",
"chars": 2002,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # expand model depth\nwidth_multiple: 1.25 # expand layer "
},
{
"path": "cfg/baseline/yolor-e6.yaml",
"chars": 2000,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # expand model depth\nwidth_multiple: 1.25 # expand layer "
},
{
"path": "cfg/baseline/yolor-p6.yaml",
"chars": 2036,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # expand model depth\nwidth_multiple: 1.0 # expand layer c"
},
{
"path": "cfg/baseline/yolor-w6.yaml",
"chars": 2039,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # expand model depth\nwidth_multiple: 1.0 # expand layer c"
},
{
"path": "cfg/baseline/yolov3-spp.yaml",
"chars": 1531,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/baseline/yolov3.yaml",
"chars": 1524,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/baseline/yolov4-csp.yaml",
"chars": 1593,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/deploy/yolov7-d6.yaml",
"chars": 6104,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/deploy/yolov7-e6.yaml",
"chars": 5342,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/deploy/yolov7-e6e.yaml",
"chars": 9492,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/deploy/yolov7-tiny-silu.yaml",
"chars": 3041,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/deploy/yolov7-tiny.yaml",
"chars": 4617,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/deploy/yolov7-w6.yaml",
"chars": 4644,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/deploy/yolov7.yaml",
"chars": 3998,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/deploy/yolov7x.yaml",
"chars": 4491,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/training/yolov7-d6.yaml",
"chars": 6249,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/training/yolov7-e6.yaml",
"chars": 5485,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/training/yolov7-e6e.yaml",
"chars": 9637,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/training/yolov7-tiny.yaml",
"chars": 4618,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/training/yolov7-w6.yaml",
"chars": 4787,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/training/yolov7.yaml",
"chars": 3999,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "cfg/training/yolov7x.yaml",
"chars": 4490,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "data/coco.yaml",
"chars": 1425,
"preview": "# COCO 2017 dataset http://cocodataset.org\n\n# download command/URL (optional)\ndownload: bash ./scripts/get_coco.sh\n\n# tr"
},
{
"path": "data/hyp.scratch.custom.yaml",
"chars": 1505,
"preview": "lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)\nlrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)\nmomentum"
},
{
"path": "data/hyp.scratch.p5.yaml",
"chars": 1507,
"preview": "lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)\nlrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)\nmomentum"
},
{
"path": "data/hyp.scratch.p6.yaml",
"chars": 1507,
"preview": "lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)\nlrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf)\nmomentum"
},
{
"path": "data/hyp.scratch.tiny.yaml",
"chars": 1509,
"preview": "lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)\nlrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)\nmomentu"
},
{
"path": "deploy/triton-inference-server/README.md",
"chars": 7254,
"preview": "# YOLOv7 on Triton Inference Server\n\nInstructions to deploy YOLOv7 as TensorRT engine to [Triton Inference Server](https"
},
{
"path": "deploy/triton-inference-server/boundingbox.py",
"chars": 960,
"preview": "class BoundingBox:\n def __init__(self, classID, confidence, x1, x2, y1, y2, image_width, image_height):\n self."
},
{
"path": "deploy/triton-inference-server/client.py",
"chars": 14370,
"preview": "#!/usr/bin/env python\n\nimport argparse\nimport numpy as np\nimport sys\nimport cv2\n\nimport tritonclient.grpc as grpcclient\n"
},
{
"path": "deploy/triton-inference-server/labels.py",
"chars": 1382,
"preview": "from enum import Enum\n\nclass COCOLabels(Enum):\n PERSON = 0\n BICYCLE = 1\n CAR = 2\n MOTORBIKE = 3\n AEROPLAN"
},
{
"path": "deploy/triton-inference-server/processing.py",
"chars": 2107,
"preview": "from boundingbox import BoundingBox\n\nimport cv2\nimport numpy as np\n\ndef preprocess(img, input_shape, letter_box=True):\n "
},
{
"path": "deploy/triton-inference-server/render.py",
"chars": 3170,
"preview": "import numpy as np\n\nimport cv2\n\nfrom math import sqrt\n\n_LINE_THICKNESS_SCALING = 500.0\n\nnp.random.seed(0)\nRAND_COLORS = "
},
{
"path": "export.py",
"chars": 9162,
"preview": "import argparse\nimport sys\nimport time\nimport warnings\n\nsys.path.append('./') # to run '$ python *.py' files in subdire"
},
{
"path": "hubconf.py",
"chars": 3583,
"preview": "\"\"\"PyTorch Hub models\n\nUsage:\n import torch\n model = torch.hub.load('repo', 'model')\n\"\"\"\n\nfrom pathlib import Path"
},
{
"path": "models/__init__.py",
"chars": 6,
"preview": "# init"
},
{
"path": "models/common.py",
"chars": 84388,
"preview": "import math\nfrom copy import copy\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport requests\nimpor"
},
{
"path": "models/experimental.py",
"chars": 10898,
"preview": "import numpy as np\nimport random\nimport torch\nimport torch.nn as nn\n\nfrom models.common import Conv, DWConv\nfrom utils.g"
},
{
"path": "models/yolo.py",
"chars": 40024,
"preview": "import argparse\nimport logging\nimport sys\nfrom copy import deepcopy\n\nsys.path.append('./') # to run '$ python *.py' fil"
},
{
"path": "requirements.txt",
"chars": 958,
"preview": "# Usage: pip install -r requirements.txt\n\n# Base ----------------------------------------\nmatplotlib>=3.2.2\nnumpy>=1.18."
},
{
"path": "scripts/get_coco.sh",
"chars": 820,
"preview": "#!/bin/bash\n# COCO 2017 dataset http://cocodataset.org\n# Download command: bash ./scripts/get_coco.sh\n\n# Download/unzip "
},
{
"path": "test.py",
"chars": 17281,
"preview": "import argparse\nimport json\nimport os\nfrom pathlib import Path\nfrom threading import Thread\n\nimport numpy as np\nimport t"
},
{
"path": "tools/YOLOv7-Dynamic-Batch-ONNXRUNTIME.ipynb",
"chars": 5930137,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"52dd366d-8533-4092-855c-7978f7d396ba\",\n \""
},
{
"path": "tools/YOLOv7CoreML.ipynb",
"chars": 893918,
"preview": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"YOLOv7CoreML.ipynb\",\n \"prov"
},
{
"path": "tools/YOLOv7onnx.ipynb",
"chars": 1538769,
"preview": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"YOLOv7ONNXandTRT.ipynb\",\n \""
},
{
"path": "tools/YOLOv7trt.ipynb",
"chars": 1768452,
"preview": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"YOLOv7ONNXandTRT.ipynb\",\n \""
},
{
"path": "tools/compare_YOLOv7_vs_YOLOv5m6_half.ipynb",
"chars": 3932428,
"preview": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"YOLOv7_vs_YOLOv5m6.ipynb\",\n "
},
{
"path": "tools/compare_YOLOv7_vs_YOLOv5s6.ipynb",
"chars": 3913762,
"preview": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"YOLOv7_vs_YOLOv5s6.ipynb\",\n "
},
{
"path": "tools/instance.ipynb",
"chars": 488390,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"failing-secret\",\n \"metadata\": {},\n \""
},
{
"path": "tools/keypoint.ipynb",
"chars": 476415,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c691ce2d\",\n \"metadata\": {},\n \"output"
},
{
"path": "tools/visualization.ipynb",
"chars": 493866,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"0ab662ce\",\n \"metadata\": {},\n \"outputs\":"
},
{
"path": "utils/__init__.py",
"chars": 6,
"preview": "# init"
},
{
"path": "utils/autoanchor.py",
"chars": 7147,
"preview": "# Auto-anchor utils\n\nimport numpy as np\nimport torch\nimport yaml\nfrom scipy.cluster.vq import kmeans\nfrom tqdm import tq"
}
]
// ... and 27 more files (download for full content)
About this extraction
This page contains the full source code of the WongKinYiu/yolov7 GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 87 files (42.5 MB), approximately 5.0M tokens, and a symbol index with 330 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.