initial commit

.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/
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+develop-eggs/
+dist/
+downloads/
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+lib64/
+parts/
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+wheels/
+*.egg-info/
+/wandb/
+.installed.cfg
+*.egg
+
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+# PyInstaller
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+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
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+# Translations
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+target/
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+.python-version
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+# 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
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+.idea/dictionaries
+.html  # Bokeh Plots
+.pg  # TensorFlow Frozen Graphs
+.avi # videos
+
+# Sensitive or high-churn files:
+.idea/**/dataSources/
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+.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/
+
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+.idea_modules/
+
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+atlassian-ide-plugin.xml
+
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+.idea/replstate.xml
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties

app.py

+from yolov7processor import YoloV7Processor
+
+detect = YoloV7Processor(source ="C:\\Users\\sikhin.vc\\Downloads\\2023-02-28_15-41-51", weights ="E:\\Download\\jk_v1.pt", imgsz = 416, trace = True, target_dir="C:\\Users\\sikhin.vc\\Downloads\\auto_annotation_dest")
+detect.detect()
\ No newline at end of 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)
+  ]

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)
+  ]

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)
+  ]

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)
+  ]

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)
+  ]
\ No newline at end of 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)
+  ]
\ No newline at end of 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)
+  ]
\ No newline at end of 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)
+  ]
\ No newline at end of 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)
+  ]

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)
+  ]

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)
+  ]

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)
+  ]

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)
+  ]

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)
+  ]

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)
+  ]

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)
+  ]

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)
+  ]

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)
+  ]

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)
+  ]

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)
+  ]

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)
+  ]

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)
+  ]

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)
+  ]

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)
+  ]

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)
+  ]

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)
+  ]

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' ]

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
\ No newline at end of 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
\ No newline at end of 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
\ No newline at end of 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

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$(pwd)/triton-deploy/models:/models nvcr.io/nvidia/tritonserver:22.06-py3 tritonserver --model-repository=/models --strict-model-config=false --log-verbose 1
+```
+
+In the log you should see:
+
+```
++--------+---------+--------+
+| Model  | Version | Status |
++--------+---------+--------+
+| yolov7 | 1       | READY  |
++--------+---------+--------+
+```
+
+## Performance with Model Analyzer
+
+See [Triton Model Analyzer Documentation](https://github.com/triton-inference-server/server/blob/main/docs/model_analyzer.md#model-analyzer) for more info.
+
+Performance numbers @ RTX 3090 + AMD Ryzen 9 5950X
+
+Example test for 16 concurrent clients using shared memory, each with batch size 1 requests:
+
+```bash
+docker run -it --ipc=host --net=host nvcr.io/nvidia/tritonserver:22.06-py3-sdk /bin/bash
+
+./install/bin/perf_analyzer -m yolov7 -u 127.0.0.1:8001 -i grpc --shared-memory system --concurrency-range 16
+
+# Result (truncated)
+Concurrency: 16, throughput: 590.119 infer/sec, latency 27080 usec
+```
+
+Throughput for 16 clients with batch size 1 is the same as for a single thread running the engine at 16 batch size locally thanks to Triton [Dynamic Batching Strategy](https://github.com/triton-inference-server/server/blob/main/docs/model_configuration.md#dynamic-batcher). Result without dynamic batching (disable in model configuration) considerably worse:
+
+```bash
+# Result (truncated)
+Concurrency: 16, throughput: 335.587 infer/sec, latency 47616 usec
+```
+
+## How to run model in your code
+
+Example client can be found in client.py. It can run dummy input, images and videos.
+
+```bash
+pip3 install tritonclient[all] opencv-python
+python3 client.py image data/dog.jpg
+```
+
+![exemplary output result](data/dog_result.jpg)
+
+```
+$ 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
+```

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)

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

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