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.dockerignore
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.dockerignore
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@ -1,217 +0,0 @@
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# Repo-specific DockerIgnore -------------------------------------------------------------------------------------------
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||||
# .git
|
||||
.cache
|
||||
.idea
|
||||
runs
|
||||
output
|
||||
coco
|
||||
storage.googleapis.com
|
||||
|
||||
data/samples/*
|
||||
!data/samples/zidane.jpg
|
||||
!data/samples/bus.jpg
|
||||
**/results*.txt
|
||||
*.jpg
|
||||
|
||||
# Neural Network weights -----------------------------------------------------------------------------------------------
|
||||
**/*.weights
|
||||
**/*.pt
|
||||
**/*.onnx
|
||||
**/*.mlmodel
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||||
**/darknet53.conv.74
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||||
**/yolov3-tiny.conv.15
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||||
|
||||
|
||||
# Below Copied From .gitignore -----------------------------------------------------------------------------------------
|
||||
# Below Copied From .gitignore -----------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
|
||||
# C extensions
|
||||
*.so
|
||||
|
||||
# Distribution / packaging
|
||||
.Python
|
||||
env/
|
||||
build/
|
||||
develop-eggs/
|
||||
dist/
|
||||
downloads/
|
||||
eggs/
|
||||
.eggs/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
*.egg-info/
|
||||
.installed.cfg
|
||||
*.egg
|
||||
|
||||
# PyInstaller
|
||||
# Usually these files are written by a python script from a template
|
||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
||||
*.manifest
|
||||
*.spec
|
||||
|
||||
# Installer logs
|
||||
pip-log.txt
|
||||
pip-delete-this-directory.txt
|
||||
|
||||
# Unit test / coverage reports
|
||||
htmlcov/
|
||||
.tox/
|
||||
.coverage
|
||||
.coverage.*
|
||||
.cache
|
||||
nosetests.xml
|
||||
coverage.xml
|
||||
*.cover
|
||||
.hypothesis/
|
||||
|
||||
# Translations
|
||||
*.mo
|
||||
*.pot
|
||||
|
||||
# Django stuff:
|
||||
*.log
|
||||
local_settings.py
|
||||
|
||||
# Flask stuff:
|
||||
instance/
|
||||
.webassets-cache
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
|
||||
|
||||
# PyBuilder
|
||||
target/
|
||||
|
||||
# Jupyter Notebook
|
||||
.ipynb_checkpoints
|
||||
|
||||
# pyenv
|
||||
.python-version
|
||||
|
||||
# celery beat schedule file
|
||||
celerybeat-schedule
|
||||
|
||||
# SageMath parsed files
|
||||
*.sage.py
|
||||
|
||||
# dotenv
|
||||
.env
|
||||
|
||||
# virtualenv
|
||||
.venv
|
||||
venv/
|
||||
ENV/
|
||||
|
||||
# Spyder project settings
|
||||
.spyderproject
|
||||
.spyproject
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||||
|
||||
# Rope project settings
|
||||
.ropeproject
|
||||
|
||||
# mkdocs documentation
|
||||
/site
|
||||
|
||||
# mypy
|
||||
.mypy_cache/
|
||||
|
||||
|
||||
# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------
|
||||
|
||||
# General
|
||||
.DS_Store
|
||||
.AppleDouble
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.LSOverride
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||||
|
||||
# Icon must end with two \r
|
||||
Icon
|
||||
Icon?
|
||||
|
||||
# Thumbnails
|
||||
._*
|
||||
|
||||
# Files that might appear in the root of a volume
|
||||
.DocumentRevisions-V100
|
||||
.fseventsd
|
||||
.Spotlight-V100
|
||||
.TemporaryItems
|
||||
.Trashes
|
||||
.VolumeIcon.icns
|
||||
.com.apple.timemachine.donotpresent
|
||||
|
||||
# Directories potentially created on remote AFP share
|
||||
.AppleDB
|
||||
.AppleDesktop
|
||||
Network Trash Folder
|
||||
Temporary Items
|
||||
.apdisk
|
||||
|
||||
|
||||
# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore
|
||||
# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
|
||||
# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
|
||||
|
||||
# User-specific stuff:
|
||||
.idea/*
|
||||
.idea/**/workspace.xml
|
||||
.idea/**/tasks.xml
|
||||
.idea/dictionaries
|
||||
.html # Bokeh Plots
|
||||
.pg # TensorFlow Frozen Graphs
|
||||
.avi # videos
|
||||
|
||||
# Sensitive or high-churn files:
|
||||
.idea/**/dataSources/
|
||||
.idea/**/dataSources.ids
|
||||
.idea/**/dataSources.local.xml
|
||||
.idea/**/sqlDataSources.xml
|
||||
.idea/**/dynamic.xml
|
||||
.idea/**/uiDesigner.xml
|
||||
|
||||
# Gradle:
|
||||
.idea/**/gradle.xml
|
||||
.idea/**/libraries
|
||||
|
||||
# CMake
|
||||
cmake-build-debug/
|
||||
cmake-build-release/
|
||||
|
||||
# Mongo Explorer plugin:
|
||||
.idea/**/mongoSettings.xml
|
||||
|
||||
## File-based project format:
|
||||
*.iws
|
||||
|
||||
## Plugin-specific files:
|
||||
|
||||
# IntelliJ
|
||||
out/
|
||||
|
||||
# mpeltonen/sbt-idea plugin
|
||||
.idea_modules/
|
||||
|
||||
# JIRA plugin
|
||||
atlassian-ide-plugin.xml
|
||||
|
||||
# Cursive Clojure plugin
|
||||
.idea/replstate.xml
|
||||
|
||||
# Crashlytics plugin (for Android Studio and IntelliJ)
|
||||
com_crashlytics_export_strings.xml
|
||||
crashlytics.properties
|
||||
crashlytics-build.properties
|
||||
fabric.properties
|
||||
|
|
@ -1,41 +0,0 @@
|
|||
---
|
||||
name: "\U0001F41BBug report"
|
||||
about: Create a report to help us improve
|
||||
title: ''
|
||||
labels: bug
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
Before submitting a bug report, please ensure that you are using the latest versions of:
|
||||
- Python
|
||||
- PyTorch
|
||||
- This repository (run `git fetch && git status -uno` to check and `git pull` to update)
|
||||
|
||||
**Your issue must be reproducible on a public dataset (i.e COCO) using the latest version of the repository, and you must supply code to reproduce, or we can not help you.**
|
||||
|
||||
If this is a custom training question we suggest you include your `train*.jpg`, `test*.jpg` and `results.png` figures.
|
||||
|
||||
|
||||
## 🐛 Bug
|
||||
A clear and concise description of what the bug is.
|
||||
|
||||
## To Reproduce
|
||||
**REQUIRED**: Code to reproduce your issue below
|
||||
```
|
||||
python train.py ...
|
||||
```
|
||||
|
||||
|
||||
## Expected behavior
|
||||
A clear and concise description of what you expected to happen.
|
||||
|
||||
## Environment
|
||||
If applicable, add screenshots to help explain your problem.
|
||||
|
||||
- OS: [e.g. Ubuntu]
|
||||
- GPU [e.g. 2080 Ti]
|
||||
|
||||
|
||||
## Additional context
|
||||
Add any other context about the problem here.
|
||||
|
|
@ -1,27 +0,0 @@
|
|||
---
|
||||
name: "\U0001F680Feature request"
|
||||
about: Suggest an idea for this project
|
||||
title: ''
|
||||
labels: enhancement
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Feature
|
||||
<!-- A clear and concise description of the feature proposal -->
|
||||
|
||||
## Motivation
|
||||
|
||||
<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too -->
|
||||
|
||||
## Pitch
|
||||
|
||||
<!-- A clear and concise description of what you want to happen. -->
|
||||
|
||||
## Alternatives
|
||||
|
||||
<!-- A clear and concise description of any alternative solutions or features you've considered, if any. -->
|
||||
|
||||
## Additional context
|
||||
|
||||
<!-- Add any other context or screenshots about the feature request here. -->
|
||||
|
|
@ -1,30 +0,0 @@
|
|||
name: Greetings
|
||||
|
||||
on: [pull_request, issues]
|
||||
|
||||
jobs:
|
||||
greeting:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/first-interaction@v1
|
||||
with:
|
||||
repo-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
pr-message: 'Hello @${{ github.actor }}, thank you for submitting a PR! We will respond as soon as possible.'
|
||||
issue-message: |
|
||||
Hello @${{ github.actor }}, thank you for your interest in our work! Ultralytics has open-sourced YOLOv5 at https://github.com/ultralytics/yolov5, featuring faster, lighter and more accurate object detection. YOLOv5 is recommended for all new projects.
|
||||
|
||||
<a href="https://apps.apple.com/app/id1452689527" target="_blank">
|
||||
<img src="https://user-images.githubusercontent.com/26833433/82944393-f7644d80-9f4f-11ea-8b87-1a5b04f555f1.jpg" width="800"></a>
|
||||
|
||||
<img src="https://user-images.githubusercontent.com/26833433/84200349-729f2680-aa5b-11ea-8f9a-604c9e01a658.png" width="800">
|
||||
|
||||
To continue with this repo, please visit our [Custom Training Tutorial](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) to get started, and see our [Google Colab Notebook](https://github.com/ultralytics/yolov3/blob/master/tutorial.ipynb), [Docker Image](https://hub.docker.com/r/ultralytics/yolov3), and [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart) for example environments.
|
||||
|
||||
If this is a bug report, please provide screenshots and **minimum viable code to reproduce your issue**, otherwise we can not help you.
|
||||
|
||||
If this is a custom model or data training question, please note that Ultralytics does **not** provide free personal support. As a leader in vision ML and AI, we do offer professional consulting, from simple expert advice up to delivery of fully customized, end-to-end production solutions for our clients, such as:
|
||||
- **Cloud-based AI** systems operating on **hundreds of HD video streams in realtime.**
|
||||
- **Edge AI** integrated into custom iOS and Android apps for realtime **30 FPS video inference.**
|
||||
- **Custom data training**, hyperparameter evolution, and model exportation to any destination.
|
||||
|
||||
For more information please visit https://www.ultralytics.com.
|
||||
|
|
@ -1,17 +0,0 @@
|
|||
name: Close stale issues
|
||||
on:
|
||||
schedule:
|
||||
- cron: "0 0 * * *"
|
||||
|
||||
jobs:
|
||||
stale:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/stale@v1
|
||||
with:
|
||||
repo-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
stale-issue-message: 'This issue is stale because it has been open 30 days with no activity. Remove Stale label or comment or this will be closed in 5 days.'
|
||||
stale-pr-message: 'This pull request is stale because it has been open 30 days with no activity. Remove Stale label or comment or this will be closed in 5 days.'
|
||||
days-before-stale: 30
|
||||
days-before-close: 5
|
||||
exempt-issue-label: 'tutorial'
|
||||
|
|
@ -1,244 +1,3 @@
|
|||
# Repo-specific GitIgnore ----------------------------------------------------------------------------------------------
|
||||
*.jpg
|
||||
*.jpeg
|
||||
*.png
|
||||
*.bmp
|
||||
*.tif
|
||||
*.tiff
|
||||
*.heic
|
||||
*.JPG
|
||||
*.JPEG
|
||||
*.PNG
|
||||
*.BMP
|
||||
*.TIF
|
||||
*.TIFF
|
||||
*.HEIC
|
||||
*.mp4
|
||||
*.mov
|
||||
*.MOV
|
||||
*.avi
|
||||
*.data
|
||||
*.json
|
||||
|
||||
*.cfg
|
||||
!cfg/yolov3*.cfg
|
||||
|
||||
storage.googleapis.com
|
||||
runs/*
|
||||
data/*
|
||||
!data/samples/zidane.jpg
|
||||
!data/samples/bus.jpg
|
||||
!data/coco.names
|
||||
!data/coco_paper.names
|
||||
!data/coco.data
|
||||
!data/coco_*.data
|
||||
!data/coco_*.txt
|
||||
!data/trainvalno5k.shapes
|
||||
!data/*.sh
|
||||
|
||||
pycocotools/*
|
||||
results*.txt
|
||||
gcp_test*.sh
|
||||
|
||||
# MATLAB GitIgnore -----------------------------------------------------------------------------------------------------
|
||||
*.m~
|
||||
*.mat
|
||||
!targets*.mat
|
||||
|
||||
# Neural Network weights -----------------------------------------------------------------------------------------------
|
||||
*.weights
|
||||
*.pt
|
||||
*.onnx
|
||||
*.mlmodel
|
||||
darknet53.conv.74
|
||||
yolov3-tiny.conv.15
|
||||
|
||||
# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
|
||||
# C extensions
|
||||
*.so
|
||||
|
||||
# Distribution / packaging
|
||||
.Python
|
||||
env/
|
||||
build/
|
||||
develop-eggs/
|
||||
dist/
|
||||
downloads/
|
||||
eggs/
|
||||
.eggs/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
*.egg-info/
|
||||
.installed.cfg
|
||||
*.egg
|
||||
|
||||
# PyInstaller
|
||||
# Usually these files are written by a python script from a template
|
||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
||||
*.manifest
|
||||
*.spec
|
||||
|
||||
# Installer logs
|
||||
pip-log.txt
|
||||
pip-delete-this-directory.txt
|
||||
|
||||
# Unit test / coverage reports
|
||||
htmlcov/
|
||||
.tox/
|
||||
.coverage
|
||||
.coverage.*
|
||||
.cache
|
||||
nosetests.xml
|
||||
coverage.xml
|
||||
*.cover
|
||||
.hypothesis/
|
||||
|
||||
# Translations
|
||||
*.mo
|
||||
*.pot
|
||||
|
||||
# Django stuff:
|
||||
*.log
|
||||
local_settings.py
|
||||
|
||||
# Flask stuff:
|
||||
instance/
|
||||
.webassets-cache
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
|
||||
|
||||
# PyBuilder
|
||||
target/
|
||||
|
||||
# Jupyter Notebook
|
||||
.ipynb_checkpoints
|
||||
|
||||
# pyenv
|
||||
.python-version
|
||||
|
||||
# celery beat schedule file
|
||||
celerybeat-schedule
|
||||
|
||||
# SageMath parsed files
|
||||
*.sage.py
|
||||
|
||||
# dotenv
|
||||
.env
|
||||
|
||||
# virtualenv
|
||||
.venv
|
||||
venv/
|
||||
ENV/
|
||||
|
||||
# Spyder project settings
|
||||
.spyderproject
|
||||
.spyproject
|
||||
|
||||
# Rope project settings
|
||||
.ropeproject
|
||||
|
||||
# mkdocs documentation
|
||||
/site
|
||||
|
||||
# mypy
|
||||
.mypy_cache/
|
||||
|
||||
|
||||
# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------
|
||||
|
||||
# General
|
||||
.DS_Store
|
||||
.AppleDouble
|
||||
.LSOverride
|
||||
|
||||
# Icon must end with two \r
|
||||
Icon
|
||||
Icon?
|
||||
|
||||
# Thumbnails
|
||||
._*
|
||||
|
||||
# Files that might appear in the root of a volume
|
||||
.DocumentRevisions-V100
|
||||
.fseventsd
|
||||
.Spotlight-V100
|
||||
.TemporaryItems
|
||||
.Trashes
|
||||
.VolumeIcon.icns
|
||||
.com.apple.timemachine.donotpresent
|
||||
|
||||
# Directories potentially created on remote AFP share
|
||||
.AppleDB
|
||||
.AppleDesktop
|
||||
Network Trash Folder
|
||||
Temporary Items
|
||||
.apdisk
|
||||
|
||||
|
||||
# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore
|
||||
# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
|
||||
# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
|
||||
|
||||
# User-specific stuff:
|
||||
.idea/*
|
||||
.idea/**/workspace.xml
|
||||
.idea/**/tasks.xml
|
||||
.idea/dictionaries
|
||||
.html # Bokeh Plots
|
||||
.pg # TensorFlow Frozen Graphs
|
||||
.avi # videos
|
||||
|
||||
# Sensitive or high-churn files:
|
||||
.idea/**/dataSources/
|
||||
.idea/**/dataSources.ids
|
||||
.idea/**/dataSources.local.xml
|
||||
.idea/**/sqlDataSources.xml
|
||||
.idea/**/dynamic.xml
|
||||
.idea/**/uiDesigner.xml
|
||||
|
||||
# Gradle:
|
||||
.idea/**/gradle.xml
|
||||
.idea/**/libraries
|
||||
|
||||
# CMake
|
||||
cmake-build-debug/
|
||||
cmake-build-release/
|
||||
|
||||
# Mongo Explorer plugin:
|
||||
.idea/**/mongoSettings.xml
|
||||
|
||||
## File-based project format:
|
||||
*.iws
|
||||
|
||||
## Plugin-specific files:
|
||||
|
||||
# IntelliJ
|
||||
out/
|
||||
|
||||
# mpeltonen/sbt-idea plugin
|
||||
.idea_modules/
|
||||
|
||||
# JIRA plugin
|
||||
atlassian-ide-plugin.xml
|
||||
|
||||
# Cursive Clojure plugin
|
||||
.idea/replstate.xml
|
||||
|
||||
# Crashlytics plugin (for Android Studio and IntelliJ)
|
||||
com_crashlytics_export_strings.xml
|
||||
crashlytics.properties
|
||||
crashlytics-build.properties
|
||||
fabric.properties
|
||||
weights/
|
||||
runs/
|
||||
data/widok01-11_images
|
||||
|
|
|
|||
62
Dockerfile
62
Dockerfile
|
|
@ -1,62 +0,0 @@
|
|||
# Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
|
||||
FROM nvcr.io/nvidia/pytorch:20.03-py3
|
||||
|
||||
# Install dependencies (pip or conda)
|
||||
RUN pip install -U gsutil
|
||||
# RUN pip install -U -r requirements.txt
|
||||
# RUN conda update -n base -c defaults conda
|
||||
# RUN conda install -y -c anaconda future numpy opencv matplotlib tqdm pillow
|
||||
# RUN conda install -y -c conda-forge scikit-image tensorboard pycocotools
|
||||
|
||||
## Install OpenCV with Gstreamer support
|
||||
#WORKDIR /usr/src
|
||||
#RUN pip uninstall -y opencv-python
|
||||
#RUN apt-get update
|
||||
#RUN apt-get install -y gstreamer1.0-tools gstreamer1.0-python3-dbg-plugin-loader libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev
|
||||
#RUN git clone https://github.com/opencv/opencv.git && cd opencv && git checkout 4.1.1 && mkdir build
|
||||
#RUN git clone https://github.com/opencv/opencv_contrib.git && cd opencv_contrib && git checkout 4.1.1
|
||||
#RUN cd opencv/build && cmake ../ \
|
||||
# -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \
|
||||
# -D BUILD_OPENCV_PYTHON3=ON \
|
||||
# -D PYTHON3_EXECUTABLE=/opt/conda/bin/python \
|
||||
# -D PYTHON3_INCLUDE_PATH=/opt/conda/include/python3.6m \
|
||||
# -D PYTHON3_LIBRARIES=/opt/conda/lib/python3.6/site-packages \
|
||||
# -D WITH_GSTREAMER=ON \
|
||||
# -D WITH_FFMPEG=OFF \
|
||||
# && make && make install && ldconfig
|
||||
#RUN cd /usr/local/lib/python3.6/site-packages/cv2/python-3.6/ && mv cv2.cpython-36m-x86_64-linux-gnu.so cv2.so
|
||||
#RUN cd /opt/conda/lib/python3.6/site-packages/ && ln -s /usr/local/lib/python3.6/site-packages/cv2/python-3.6/cv2.so cv2.so
|
||||
#RUN python3 -c "import cv2; print(cv2.getBuildInformation())"
|
||||
|
||||
# Create working directory
|
||||
RUN mkdir -p /usr/src/app
|
||||
WORKDIR /usr/src/app
|
||||
|
||||
# Copy contents
|
||||
COPY . /usr/src/app
|
||||
|
||||
# Copy weights
|
||||
#RUN python3 -c "from models import *; \
|
||||
#attempt_download('weights/yolov3.pt'); \
|
||||
#attempt_download('weights/yolov3-spp.pt')"
|
||||
|
||||
|
||||
# --------------------------------------------------- Extras Below ---------------------------------------------------
|
||||
|
||||
# Build and Push
|
||||
# t=ultralytics/yolov3:v0 && sudo docker build -t $t . && sudo docker push $t
|
||||
|
||||
# Run
|
||||
# t=ultralytics/yolov3:v0 && sudo docker pull $t && sudo docker run -it --gpus all --ipc=host $t bash
|
||||
|
||||
# Pull and Run with local directory access
|
||||
# t=ultralytics/yolov3:v0 && sudo docker pull $t && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/coco:/usr/src/coco $t bash
|
||||
|
||||
# Kill all
|
||||
# sudo docker kill "$(sudo docker ps -q)"
|
||||
|
||||
# Kill all image-based
|
||||
# sudo docker kill $(sudo docker ps -a -q --filter ancestor=ultralytics/yolov3:v0)
|
||||
|
||||
# Run bash for loop
|
||||
# sudo docker run --gpus all --ipc=host ultralytics/yolov3:v0 while true; do python3 train.py --evolve; done
|
||||
674
LICENSE
674
LICENSE
|
|
@ -1,674 +0,0 @@
|
|||
GNU GENERAL PUBLIC LICENSE
|
||||
Version 3, 29 June 2007
|
||||
|
||||
Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
|
||||
Everyone is permitted to copy and distribute verbatim copies
|
||||
of this license document, but changing it is not allowed.
|
||||
|
||||
Preamble
|
||||
|
||||
The GNU General Public License is a free, copyleft license for
|
||||
software and other kinds of works.
|
||||
|
||||
The licenses for most software and other practical works are designed
|
||||
to take away your freedom to share and change the works. By contrast,
|
||||
the GNU General Public License is intended to guarantee your freedom to
|
||||
share and change all versions of a program--to make sure it remains free
|
||||
software for all its users. We, the Free Software Foundation, use the
|
||||
GNU General Public License for most of our software; it applies also to
|
||||
any other work released this way by its authors. You can apply it to
|
||||
your programs, too.
|
||||
|
||||
When we speak of free software, we are referring to freedom, not
|
||||
price. Our General Public Licenses are designed to make sure that you
|
||||
have the freedom to distribute copies of free software (and charge for
|
||||
them if you wish), that you receive source code or can get it if you
|
||||
want it, that you can change the software or use pieces of it in new
|
||||
free programs, and that you know you can do these things.
|
||||
|
||||
To protect your rights, we need to prevent others from denying you
|
||||
these rights or asking you to surrender the rights. Therefore, you have
|
||||
certain responsibilities if you distribute copies of the software, or if
|
||||
you modify it: responsibilities to respect the freedom of others.
|
||||
|
||||
For example, if you distribute copies of such a program, whether
|
||||
gratis or for a fee, you must pass on to the recipients the same
|
||||
freedoms that you received. You must make sure that they, too, receive
|
||||
or can get the source code. And you must show them these terms so they
|
||||
know their rights.
|
||||
|
||||
Developers that use the GNU GPL protect your rights with two steps:
|
||||
(1) assert copyright on the software, and (2) offer you this License
|
||||
giving you legal permission to copy, distribute and/or modify it.
|
||||
|
||||
For the developers' and authors' protection, the GPL clearly explains
|
||||
that there is no warranty for this free software. For both users' and
|
||||
authors' sake, the GPL requires that modified versions be marked as
|
||||
changed, so that their problems will not be attributed erroneously to
|
||||
authors of previous versions.
|
||||
|
||||
Some devices are designed to deny users access to install or run
|
||||
modified versions of the software inside them, although the manufacturer
|
||||
can do so. This is fundamentally incompatible with the aim of
|
||||
protecting users' freedom to change the software. The systematic
|
||||
pattern of such abuse occurs in the area of products for individuals to
|
||||
use, which is precisely where it is most unacceptable. Therefore, we
|
||||
have designed this version of the GPL to prohibit the practice for those
|
||||
products. If such problems arise substantially in other domains, we
|
||||
stand ready to extend this provision to those domains in future versions
|
||||
of the GPL, as needed to protect the freedom of users.
|
||||
|
||||
Finally, every program is threatened constantly by software patents.
|
||||
States should not allow patents to restrict development and use of
|
||||
software on general-purpose computers, but in those that do, we wish to
|
||||
avoid the special danger that patents applied to a free program could
|
||||
make it effectively proprietary. To prevent this, the GPL assures that
|
||||
patents cannot be used to render the program non-free.
|
||||
|
||||
The precise terms and conditions for copying, distribution and
|
||||
modification follow.
|
||||
|
||||
TERMS AND CONDITIONS
|
||||
|
||||
0. Definitions.
|
||||
|
||||
"This License" refers to version 3 of the GNU General Public License.
|
||||
|
||||
"Copyright" also means copyright-like laws that apply to other kinds of
|
||||
works, such as semiconductor masks.
|
||||
|
||||
"The Program" refers to any copyrightable work licensed under this
|
||||
License. Each licensee is addressed as "you". "Licensees" and
|
||||
"recipients" may be individuals or organizations.
|
||||
|
||||
To "modify" a work means to copy from or adapt all or part of the work
|
||||
in a fashion requiring copyright permission, other than the making of an
|
||||
exact copy. The resulting work is called a "modified version" of the
|
||||
earlier work or a work "based on" the earlier work.
|
||||
|
||||
A "covered work" means either the unmodified Program or a work based
|
||||
on the Program.
|
||||
|
||||
To "propagate" a work means to do anything with it that, without
|
||||
permission, would make you directly or secondarily liable for
|
||||
infringement under applicable copyright law, except executing it on a
|
||||
computer or modifying a private copy. Propagation includes copying,
|
||||
distribution (with or without modification), making available to the
|
||||
public, and in some countries other activities as well.
|
||||
|
||||
To "convey" a work means any kind of propagation that enables other
|
||||
parties to make or receive copies. Mere interaction with a user through
|
||||
a computer network, with no transfer of a copy, is not conveying.
|
||||
|
||||
An interactive user interface displays "Appropriate Legal Notices"
|
||||
to the extent that it includes a convenient and prominently visible
|
||||
feature that (1) displays an appropriate copyright notice, and (2)
|
||||
tells the user that there is no warranty for the work (except to the
|
||||
extent that warranties are provided), that licensees may convey the
|
||||
work under this License, and how to view a copy of this License. If
|
||||
the interface presents a list of user commands or options, such as a
|
||||
menu, a prominent item in the list meets this criterion.
|
||||
|
||||
1. Source Code.
|
||||
|
||||
The "source code" for a work means the preferred form of the work
|
||||
for making modifications to it. "Object code" means any non-source
|
||||
form of a work.
|
||||
|
||||
A "Standard Interface" means an interface that either is an official
|
||||
standard defined by a recognized standards body, or, in the case of
|
||||
interfaces specified for a particular programming language, one that
|
||||
is widely used among developers working in that language.
|
||||
|
||||
The "System Libraries" of an executable work include anything, other
|
||||
than the work as a whole, that (a) is included in the normal form of
|
||||
packaging a Major Component, but which is not part of that Major
|
||||
Component, and (b) serves only to enable use of the work with that
|
||||
Major Component, or to implement a Standard Interface for which an
|
||||
implementation is available to the public in source code form. A
|
||||
"Major Component", in this context, means a major essential component
|
||||
(kernel, window system, and so on) of the specific operating system
|
||||
(if any) on which the executable work runs, or a compiler used to
|
||||
produce the work, or an object code interpreter used to run it.
|
||||
|
||||
The "Corresponding Source" for a work in object code form means all
|
||||
the source code needed to generate, install, and (for an executable
|
||||
work) run the object code and to modify the work, including scripts to
|
||||
control those activities. However, it does not include the work's
|
||||
System Libraries, or general-purpose tools or generally available free
|
||||
programs which are used unmodified in performing those activities but
|
||||
which are not part of the work. For example, Corresponding Source
|
||||
includes interface definition files associated with source files for
|
||||
the work, and the source code for shared libraries and dynamically
|
||||
linked subprograms that the work is specifically designed to require,
|
||||
such as by intimate data communication or control flow between those
|
||||
subprograms and other parts of the work.
|
||||
|
||||
The Corresponding Source need not include anything that users
|
||||
can regenerate automatically from other parts of the Corresponding
|
||||
Source.
|
||||
|
||||
The Corresponding Source for a work in source code form is that
|
||||
same work.
|
||||
|
||||
2. Basic Permissions.
|
||||
|
||||
All rights granted under this License are granted for the term of
|
||||
copyright on the Program, and are irrevocable provided the stated
|
||||
conditions are met. This License explicitly affirms your unlimited
|
||||
permission to run the unmodified Program. The output from running a
|
||||
covered work is covered by this License only if the output, given its
|
||||
content, constitutes a covered work. This License acknowledges your
|
||||
rights of fair use or other equivalent, as provided by copyright law.
|
||||
|
||||
You may make, run and propagate covered works that you do not
|
||||
convey, without conditions so long as your license otherwise remains
|
||||
in force. You may convey covered works to others for the sole purpose
|
||||
of having them make modifications exclusively for you, or provide you
|
||||
with facilities for running those works, provided that you comply with
|
||||
the terms of this License in conveying all material for which you do
|
||||
not control copyright. Those thus making or running the covered works
|
||||
for you must do so exclusively on your behalf, under your direction
|
||||
and control, on terms that prohibit them from making any copies of
|
||||
your copyrighted material outside their relationship with you.
|
||||
|
||||
Conveying under any other circumstances is permitted solely under
|
||||
the conditions stated below. Sublicensing is not allowed; section 10
|
||||
makes it unnecessary.
|
||||
|
||||
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
|
||||
|
||||
No covered work shall be deemed part of an effective technological
|
||||
measure under any applicable law fulfilling obligations under article
|
||||
11 of the WIPO copyright treaty adopted on 20 December 1996, or
|
||||
similar laws prohibiting or restricting circumvention of such
|
||||
measures.
|
||||
|
||||
When you convey a covered work, you waive any legal power to forbid
|
||||
circumvention of technological measures to the extent such circumvention
|
||||
is effected by exercising rights under this License with respect to
|
||||
the covered work, and you disclaim any intention to limit operation or
|
||||
modification of the work as a means of enforcing, against the work's
|
||||
users, your or third parties' legal rights to forbid circumvention of
|
||||
technological measures.
|
||||
|
||||
4. Conveying Verbatim Copies.
|
||||
|
||||
You may convey verbatim copies of the Program's source code as you
|
||||
receive it, in any medium, provided that you conspicuously and
|
||||
appropriately publish on each copy an appropriate copyright notice;
|
||||
keep intact all notices stating that this License and any
|
||||
non-permissive terms added in accord with section 7 apply to the code;
|
||||
keep intact all notices of the absence of any warranty; and give all
|
||||
recipients a copy of this License along with the Program.
|
||||
|
||||
You may charge any price or no price for each copy that you convey,
|
||||
and you may offer support or warranty protection for a fee.
|
||||
|
||||
5. Conveying Modified Source Versions.
|
||||
|
||||
You may convey a work based on the Program, or the modifications to
|
||||
produce it from the Program, in the form of source code under the
|
||||
terms of section 4, provided that you also meet all of these conditions:
|
||||
|
||||
a) The work must carry prominent notices stating that you modified
|
||||
it, and giving a relevant date.
|
||||
|
||||
b) The work must carry prominent notices stating that it is
|
||||
released under this License and any conditions added under section
|
||||
7. This requirement modifies the requirement in section 4 to
|
||||
"keep intact all notices".
|
||||
|
||||
c) You must license the entire work, as a whole, under this
|
||||
License to anyone who comes into possession of a copy. This
|
||||
License will therefore apply, along with any applicable section 7
|
||||
additional terms, to the whole of the work, and all its parts,
|
||||
regardless of how they are packaged. This License gives no
|
||||
permission to license the work in any other way, but it does not
|
||||
invalidate such permission if you have separately received it.
|
||||
|
||||
d) If the work has interactive user interfaces, each must display
|
||||
Appropriate Legal Notices; however, if the Program has interactive
|
||||
interfaces that do not display Appropriate Legal Notices, your
|
||||
work need not make them do so.
|
||||
|
||||
A compilation of a covered work with other separate and independent
|
||||
works, which are not by their nature extensions of the covered work,
|
||||
and which are not combined with it such as to form a larger program,
|
||||
in or on a volume of a storage or distribution medium, is called an
|
||||
"aggregate" if the compilation and its resulting copyright are not
|
||||
used to limit the access or legal rights of the compilation's users
|
||||
beyond what the individual works permit. Inclusion of a covered work
|
||||
in an aggregate does not cause this License to apply to the other
|
||||
parts of the aggregate.
|
||||
|
||||
6. Conveying Non-Source Forms.
|
||||
|
||||
You may convey a covered work in object code form under the terms
|
||||
of sections 4 and 5, provided that you also convey the
|
||||
machine-readable Corresponding Source under the terms of this License,
|
||||
in one of these ways:
|
||||
|
||||
a) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by the
|
||||
Corresponding Source fixed on a durable physical medium
|
||||
customarily used for software interchange.
|
||||
|
||||
b) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by a
|
||||
written offer, valid for at least three years and valid for as
|
||||
long as you offer spare parts or customer support for that product
|
||||
model, to give anyone who possesses the object code either (1) a
|
||||
copy of the Corresponding Source for all the software in the
|
||||
product that is covered by this License, on a durable physical
|
||||
medium customarily used for software interchange, for a price no
|
||||
more than your reasonable cost of physically performing this
|
||||
conveying of source, or (2) access to copy the
|
||||
Corresponding Source from a network server at no charge.
|
||||
|
||||
c) Convey individual copies of the object code with a copy of the
|
||||
written offer to provide the Corresponding Source. This
|
||||
alternative is allowed only occasionally and noncommercially, and
|
||||
only if you received the object code with such an offer, in accord
|
||||
with subsection 6b.
|
||||
|
||||
d) Convey the object code by offering access from a designated
|
||||
place (gratis or for a charge), and offer equivalent access to the
|
||||
Corresponding Source in the same way through the same place at no
|
||||
further charge. You need not require recipients to copy the
|
||||
Corresponding Source along with the object code. If the place to
|
||||
copy the object code is a network server, the Corresponding Source
|
||||
may be on a different server (operated by you or a third party)
|
||||
that supports equivalent copying facilities, provided you maintain
|
||||
clear directions next to the object code saying where to find the
|
||||
Corresponding Source. Regardless of what server hosts the
|
||||
Corresponding Source, you remain obligated to ensure that it is
|
||||
available for as long as needed to satisfy these requirements.
|
||||
|
||||
e) Convey the object code using peer-to-peer transmission, provided
|
||||
you inform other peers where the object code and Corresponding
|
||||
Source of the work are being offered to the general public at no
|
||||
charge under subsection 6d.
|
||||
|
||||
A separable portion of the object code, whose source code is excluded
|
||||
from the Corresponding Source as a System Library, need not be
|
||||
included in conveying the object code work.
|
||||
|
||||
A "User Product" is either (1) a "consumer product", which means any
|
||||
tangible personal property which is normally used for personal, family,
|
||||
or household purposes, or (2) anything designed or sold for incorporation
|
||||
into a dwelling. In determining whether a product is a consumer product,
|
||||
doubtful cases shall be resolved in favor of coverage. For a particular
|
||||
product received by a particular user, "normally used" refers to a
|
||||
typical or common use of that class of product, regardless of the status
|
||||
of the particular user or of the way in which the particular user
|
||||
actually uses, or expects or is expected to use, the product. A product
|
||||
is a consumer product regardless of whether the product has substantial
|
||||
commercial, industrial or non-consumer uses, unless such uses represent
|
||||
the only significant mode of use of the product.
|
||||
|
||||
"Installation Information" for a User Product means any methods,
|
||||
procedures, authorization keys, or other information required to install
|
||||
and execute modified versions of a covered work in that User Product from
|
||||
a modified version of its Corresponding Source. The information must
|
||||
suffice to ensure that the continued functioning of the modified object
|
||||
code is in no case prevented or interfered with solely because
|
||||
modification has been made.
|
||||
|
||||
If you convey an object code work under this section in, or with, or
|
||||
specifically for use in, a User Product, and the conveying occurs as
|
||||
part of a transaction in which the right of possession and use of the
|
||||
User Product is transferred to the recipient in perpetuity or for a
|
||||
fixed term (regardless of how the transaction is characterized), the
|
||||
Corresponding Source conveyed under this section must be accompanied
|
||||
by the Installation Information. But this requirement does not apply
|
||||
if neither you nor any third party retains the ability to install
|
||||
modified object code on the User Product (for example, the work has
|
||||
been installed in ROM).
|
||||
|
||||
The requirement to provide Installation Information does not include a
|
||||
requirement to continue to provide support service, warranty, or updates
|
||||
for a work that has been modified or installed by the recipient, or for
|
||||
the User Product in which it has been modified or installed. Access to a
|
||||
network may be denied when the modification itself materially and
|
||||
adversely affects the operation of the network or violates the rules and
|
||||
protocols for communication across the network.
|
||||
|
||||
Corresponding Source conveyed, and Installation Information provided,
|
||||
in accord with this section must be in a format that is publicly
|
||||
documented (and with an implementation available to the public in
|
||||
source code form), and must require no special password or key for
|
||||
unpacking, reading or copying.
|
||||
|
||||
7. Additional Terms.
|
||||
|
||||
"Additional permissions" are terms that supplement the terms of this
|
||||
License by making exceptions from one or more of its conditions.
|
||||
Additional permissions that are applicable to the entire Program shall
|
||||
be treated as though they were included in this License, to the extent
|
||||
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|
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|
||||
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|
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|
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|
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|
||||
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|
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|
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||||
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||||
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|
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||||
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|
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|
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|
||||
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|
||||
|
||||
If the disclaimer of warranty and limitation of liability provided
|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
How to Apply These Terms to Your New Programs
|
||||
|
||||
If you develop a new program, and you want it to be of the greatest
|
||||
possible use to the public, the best way to achieve this is to make it
|
||||
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|
||||
|
||||
To do so, attach the following notices to the program. It is safest
|
||||
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|
||||
state the exclusion of warranty; and each file should have at least
|
||||
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|
||||
|
||||
<one line to give the program's name and a brief idea of what it does.>
|
||||
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|
||||
|
||||
This program is free software: you can redistribute it and/or modify
|
||||
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|
||||
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|
||||
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|
||||
|
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|
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|
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|
||||
|
||||
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|
||||
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|
||||
|
||||
Also add information on how to contact you by electronic and paper mail.
|
||||
|
||||
If the program does terminal interaction, make it output a short
|
||||
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|
||||
|
||||
<program> Copyright (C) <year> <name of author>
|
||||
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
|
||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
might be different; for a GUI interface, you would use an "about box".
|
||||
|
||||
You should also get your employer (if you work as a programmer) or school,
|
||||
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
||||
For more information on this, and how to apply and follow the GNU GPL, see
|
||||
<http://www.gnu.org/licenses/>.
|
||||
|
||||
The GNU General Public License does not permit incorporating your program
|
||||
into proprietary programs. If your program is a subroutine library, you
|
||||
may consider it more useful to permit linking proprietary applications with
|
||||
the library. If this is what you want to do, use the GNU Lesser General
|
||||
Public License instead of this License. But first, please read
|
||||
<http://www.gnu.org/philosophy/why-not-lgpl.html>.
|
||||
197
README.md
197
README.md
|
|
@ -1,197 +0,0 @@
|
|||
<table style="width:100%">
|
||||
<tr>
|
||||
<td>
|
||||
<img src="https://user-images.githubusercontent.com/26833433/61591130-f7beea00-abc2-11e9-9dc0-d6abcf41d713.jpg">
|
||||
</td>
|
||||
<td align="center">
|
||||
<a href="https://www.ultralytics.com" target="_blank">
|
||||
<img src="https://storage.googleapis.com/ultralytics/logo/logoname1000.png" width="160"></a>
|
||||
<img src="https://user-images.githubusercontent.com/26833433/61591093-2b4d4480-abc2-11e9-8b46-d88eb1dabba1.jpg">
|
||||
<a href="https://itunes.apple.com/app/id1452689527" target="_blank">
|
||||
<img src="https://user-images.githubusercontent.com/26833433/50044365-9b22ac00-0082-11e9-862f-e77aee7aa7b0.png" width="180"></a>
|
||||
</td>
|
||||
<td>
|
||||
<img src="https://user-images.githubusercontent.com/26833433/61591100-55066b80-abc2-11e9-9647-52c0e045b288.jpg">
|
||||
</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
|
||||
## Introduction
|
||||
|
||||
The https://github.com/ultralytics/yolov3 repo contains inference and training code for YOLOv3 in PyTorch. The code works on Linux, MacOS and Windows. Training is done on the COCO dataset by default: https://cocodataset.org/#home. **Credit to Joseph Redmon for YOLO:** https://pjreddie.com/darknet/yolo/.
|
||||
|
||||
|
||||
## Requirements
|
||||
|
||||
Python 3.7 or later with all `requirements.txt` dependencies installed, including `torch >= 1.5`. To install run:
|
||||
```bash
|
||||
$ pip install -U -r requirements.txt
|
||||
```
|
||||
|
||||
|
||||
## Tutorials
|
||||
|
||||
* <a href="https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
|
||||
* [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) < highly recommended!!
|
||||
* [GCP Quickstart](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
|
||||
* [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart)
|
||||
* [A TensorRT Implementation of YOLOv3 and YOLOv4](https://github.com/wang-xinyu/tensorrtx/tree/master/yolov3-spp)
|
||||
|
||||
|
||||
## Training
|
||||
|
||||
**Start Training:** `python3 train.py` to begin training after downloading COCO data with `data/get_coco2017.sh`. Each epoch trains on 117,263 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set.
|
||||
|
||||
**Resume Training:** `python3 train.py --resume` to resume training from `weights/last.pt`.
|
||||
|
||||
**Plot Training:** `from utils import utils; utils.plot_results()`
|
||||
|
||||
<img src="https://user-images.githubusercontent.com/26833433/78175826-599d4800-7410-11ea-87d4-f629071838f6.png" width="900">
|
||||
|
||||
|
||||
### Image Augmentation
|
||||
|
||||
`datasets.py` applies OpenCV-powered (https://opencv.org/) augmentation to the input image. We use a **mosaic dataloader** to increase image variability during training.
|
||||
|
||||
<img src="https://user-images.githubusercontent.com/26833433/80769557-6e015d00-8b02-11ea-9c4b-69310eb2b962.jpg" width="900">
|
||||
|
||||
|
||||
### Speed
|
||||
|
||||
https://cloud.google.com/deep-learning-vm/
|
||||
**Machine type:** preemptible [n1-standard-8](https://cloud.google.com/compute/docs/machine-types) (8 vCPUs, 30 GB memory)
|
||||
**CPU platform:** Intel Skylake
|
||||
**GPUs:** K80 ($0.14/hr), T4 ($0.11/hr), V100 ($0.74/hr) CUDA with [Nvidia Apex](https://github.com/NVIDIA/apex) FP16/32
|
||||
**HDD:** 300 GB SSD
|
||||
**Dataset:** COCO train 2014 (117,263 images)
|
||||
**Model:** `yolov3-spp.cfg`
|
||||
**Command:** `python3 train.py --data coco2017.data --img 416 --batch 32`
|
||||
|
||||
GPU | n | `--batch-size` | img/s | epoch<br>time | epoch<br>cost
|
||||
--- |--- |--- |--- |--- |---
|
||||
K80 |1| 32 x 2 | 11 | 175 min | $0.41
|
||||
T4 |1<br>2| 32 x 2<br>64 x 1 | 41<br>61 | 48 min<br>32 min | $0.09<br>$0.11
|
||||
V100 |1<br>2| 32 x 2<br>64 x 1 | 122<br>**178** | 16 min<br>**11 min** | **$0.21**<br>$0.28
|
||||
2080Ti |1<br>2| 32 x 2<br>64 x 1 | 81<br>140 | 24 min<br>14 min | -<br>-
|
||||
|
||||
|
||||
## Inference
|
||||
|
||||
```bash
|
||||
python3 detect.py --source ...
|
||||
```
|
||||
|
||||
- Image: `--source file.jpg`
|
||||
- Video: `--source file.mp4`
|
||||
- Directory: `--source dir/`
|
||||
- Webcam: `--source 0`
|
||||
- RTSP stream: `--source rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa`
|
||||
- HTTP stream: `--source http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8`
|
||||
|
||||
**YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights yolov3.pt`
|
||||
<img src="https://user-images.githubusercontent.com/26833433/64067835-51d5b500-cc2f-11e9-982e-843f7f9a6ea2.jpg" width="500">
|
||||
|
||||
**YOLOv3-tiny:** `python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights yolov3-tiny.pt`
|
||||
<img src="https://user-images.githubusercontent.com/26833433/64067834-51d5b500-cc2f-11e9-9357-c485b159a20b.jpg" width="500">
|
||||
|
||||
**YOLOv3-SPP:** `python3 detect.py --cfg cfg/yolov3-spp.cfg --weights yolov3-spp.pt`
|
||||
<img src="https://user-images.githubusercontent.com/26833433/64067833-51d5b500-cc2f-11e9-8208-6fe197809131.jpg" width="500">
|
||||
|
||||
|
||||
## Pretrained Checkpoints
|
||||
|
||||
Download from: [https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0](https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0)
|
||||
|
||||
|
||||
## Darknet Conversion
|
||||
|
||||
```bash
|
||||
$ git clone https://github.com/ultralytics/yolov3 && cd yolov3
|
||||
|
||||
# convert darknet cfg/weights to pytorch model
|
||||
$ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights')"
|
||||
Success: converted 'weights/yolov3-spp.weights' to 'weights/yolov3-spp.pt'
|
||||
|
||||
# convert cfg/pytorch model to darknet weights
|
||||
$ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.pt')"
|
||||
Success: converted 'weights/yolov3-spp.pt' to 'weights/yolov3-spp.weights'
|
||||
```
|
||||
|
||||
|
||||
## mAP
|
||||
|
||||
<i></i> |Size |COCO mAP<br>@0.5...0.95 |COCO mAP<br>@0.5
|
||||
--- | --- | --- | ---
|
||||
YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |320 |14.0<br>28.7<br>30.5<br>**37.7** |29.1<br>51.8<br>52.3<br>**56.8**
|
||||
YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |416 |16.0<br>31.2<br>33.9<br>**41.2** |33.0<br>55.4<br>56.9<br>**60.6**
|
||||
YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |512 |16.6<br>32.7<br>35.6<br>**42.6** |34.9<br>57.7<br>59.5<br>**62.4**
|
||||
YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |608 |16.6<br>33.1<br>37.0<br>**43.1** |35.4<br>58.2<br>60.7<br>**62.8**
|
||||
|
||||
- mAP@0.5 run at `--iou-thr 0.5`, mAP@0.5...0.95 run at `--iou-thr 0.7`
|
||||
- Darknet results: https://arxiv.org/abs/1804.02767
|
||||
|
||||
```bash
|
||||
$ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt --img 640 --augment
|
||||
|
||||
Namespace(augment=True, batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='coco2014.data', device='', img_size=640, iou_thres=0.6, save_json=True, single_cls=False, task='test', weights='weight
|
||||
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
|
||||
|
||||
Class Images Targets P R mAP@0.5 F1: 100%|█████████| 313/313 [03:00<00:00, 1.74it/s]
|
||||
all 5e+03 3.51e+04 0.375 0.743 0.64 0.492
|
||||
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456
|
||||
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.647
|
||||
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.496
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.263
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.501
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.596
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.361
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.597
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.666
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.492
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.719
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.810
|
||||
|
||||
Speed: 17.5/2.3/19.9 ms inference/NMS/total per 640x640 image at batch-size 16
|
||||
```
|
||||
<!-- Speed: 11.4/2.2/13.6 ms inference/NMS/total per 608x608 image at batch-size 1 -->
|
||||
|
||||
|
||||
## Reproduce Our Results
|
||||
|
||||
Run commands below. Training takes about one week on a 2080Ti per model.
|
||||
```bash
|
||||
$ python train.py --data coco2014.data --weights '' --batch-size 16 --cfg yolov3-spp.cfg
|
||||
$ python train.py --data coco2014.data --weights '' --batch-size 32 --cfg yolov3-tiny.cfg
|
||||
```
|
||||
<img src="https://user-images.githubusercontent.com/26833433/80831822-57a9de80-8ba0-11ea-9684-c47afb0432dc.png" width="900">
|
||||
|
||||
|
||||
## Reproduce Our Environment
|
||||
|
||||
To access an up-to-date working environment (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled), consider a:
|
||||
|
||||
- **GCP** Deep Learning VM with $300 free credit offer: See our [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
|
||||
- **Google Colab Notebook** with 12 hours of free GPU time. <a href="https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
|
||||
- **Docker Image** https://hub.docker.com/r/ultralytics/yolov3. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) 
|
||||
|
||||
|
||||
## Citation
|
||||
|
||||
[](https://zenodo.org/badge/latestdoi/146165888)
|
||||
|
||||
|
||||
## About Us
|
||||
|
||||
Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including:
|
||||
- **Cloud-based AI** systems operating on **hundreds of HD video streams in realtime.**
|
||||
- **Edge AI** integrated into custom iOS and Android apps for realtime **30 FPS video inference.**
|
||||
- **Custom data training**, hyperparameter evolution, and model exportation to any destination.
|
||||
|
||||
For business inquiries and professional support requests please visit us at https://www.ultralytics.com.
|
||||
|
||||
|
||||
## Contact
|
||||
|
||||
**Issues should be raised directly in the repository.** For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.
|
||||
1033
cfg/cd53s-yolov3.cfg
1033
cfg/cd53s-yolov3.cfg
File diff suppressed because it is too large
Load Diff
1155
cfg/cd53s.cfg
1155
cfg/cd53s.cfg
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
|
|
@ -1,788 +0,0 @@
|
|||
[net]
|
||||
# Testing
|
||||
#batch=1
|
||||
#subdivisions=1
|
||||
# Training
|
||||
batch=16
|
||||
subdivisions=1
|
||||
width=416
|
||||
height=416
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 500200
|
||||
policy=steps
|
||||
steps=400000,450000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
######################
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 6,7,8
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 61
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 36
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
|
@ -1,804 +0,0 @@
|
|||
# Generated by Glenn Jocher (glenn.jocher@ultralytics.com) for https://github.com/ultralytics/yolov3
|
||||
# def kmean_anchors(path='../coco/train2017.txt', n=12, img_size=(320, 640)): # from utils.utils import *; kmean_anchors()
|
||||
# Evolving anchors: 100%|██████████| 1000/1000 [41:15<00:00, 2.48s/it]
|
||||
# 0.20 iou_thr: 0.992 best possible recall, 4.25 anchors > thr
|
||||
# kmeans anchors (n=12, img_size=(320, 640), IoU=0.005/0.184/0.634-min/mean/best): 6,9, 15,16, 17,35, 37,26, 36,67, 63,42, 57,100, 121,81, 112,169, 241,158, 195,310, 426,359
|
||||
|
||||
[net]
|
||||
# Testing
|
||||
# batch=1
|
||||
# subdivisions=1
|
||||
# Training
|
||||
batch=64
|
||||
subdivisions=16
|
||||
width=608
|
||||
height=608
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 500200
|
||||
policy=steps
|
||||
steps=400000,450000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
######################
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
# SPP --------------------------------------------------------------------------
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=5
|
||||
|
||||
[route]
|
||||
layers=-2
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=9
|
||||
|
||||
[route]
|
||||
layers=-4
|
||||
|
||||
[maxpool]
|
||||
stride=1
|
||||
size=13
|
||||
|
||||
[route]
|
||||
layers=-1,-3,-5,-6
|
||||
# SPP --------------------------------------------------------------------------
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=258
|
||||
activation=linear
|
||||
|
||||
# YOLO -------------------------------------------------------------------------
|
||||
|
||||
[route]
|
||||
layers = -3
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 61
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=258
|
||||
activation=linear
|
||||
|
||||
# YOLO -------------------------------------------------------------------------
|
||||
|
||||
[route]
|
||||
layers = -3
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 36
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=258
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
from=88,99,110
|
||||
mask = 6,7,8
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
|
||||
[yolo]
|
||||
from=88,99,110
|
||||
mask = 3,4,5
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
|
||||
[yolo]
|
||||
from=88,99,110
|
||||
mask = 0,1,2
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
|
|
@ -16,10 +16,10 @@ exposure = 1.5
|
|||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=100
|
||||
max_batches = 5000
|
||||
burn_in=1000
|
||||
max_batches = 500200
|
||||
policy=steps
|
||||
steps=4000,4500
|
||||
steps=400000,450000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
|
|
@ -633,14 +633,14 @@ activation=leaky
|
|||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
filters=69
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 6,7,8
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
anchors=26,16, 19,45, 36,27, 54,32, 34,80, 60,49, 74,71, 96,105, 135,145
|
||||
classes=18
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
|
|
@ -719,14 +719,14 @@ activation=leaky
|
|||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
filters=69
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
anchors=26,16, 19,45, 36,27, 54,32, 34,80, 60,49, 74,71, 96,105, 135,145
|
||||
classes=18
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
|
|
@ -806,14 +806,14 @@ activation=leaky
|
|||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
filters=69
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
anchors=26,16, 19,45, 36,27, 54,32, 34,80, 60,49, 74,71, 96,105, 135,145
|
||||
classes=18
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
|
|
@ -1,182 +0,0 @@
|
|||
[net]
|
||||
# Testing
|
||||
batch=1
|
||||
subdivisions=1
|
||||
# Training
|
||||
# batch=64
|
||||
# subdivisions=2
|
||||
width=416
|
||||
height=416
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 500200
|
||||
policy=steps
|
||||
steps=400000,450000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=16
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
###########
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
|
||||
classes=1
|
||||
num=6
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 8
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
|
||||
classes=1
|
||||
num=6
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
|
@ -1,182 +0,0 @@
|
|||
[net]
|
||||
# Testing
|
||||
batch=1
|
||||
subdivisions=1
|
||||
# Training
|
||||
# batch=64
|
||||
# subdivisions=2
|
||||
width=416
|
||||
height=416
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 500200
|
||||
policy=steps
|
||||
steps=400000,450000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=16
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
###########
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=24
|
||||
activation=linear
|
||||
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
|
||||
classes=3
|
||||
num=6
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 8
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=24
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
|
||||
classes=3
|
||||
num=6
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
|
@ -1,182 +0,0 @@
|
|||
[net]
|
||||
# Testing
|
||||
batch=1
|
||||
subdivisions=1
|
||||
# Training
|
||||
# batch=64
|
||||
# subdivisions=2
|
||||
width=416
|
||||
height=416
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 500200
|
||||
policy=steps
|
||||
steps=400000,450000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=16
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
###########
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
|
||||
classes=80
|
||||
num=6
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 8
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 1,2,3
|
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
|
||||
classes=80
|
||||
num=6
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
|
@ -1,227 +0,0 @@
|
|||
[net]
|
||||
# Testing
|
||||
# batch=1
|
||||
# subdivisions=1
|
||||
# Training
|
||||
batch=64
|
||||
subdivisions=16
|
||||
width=608
|
||||
height=608
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 200000
|
||||
policy=steps
|
||||
steps=180000,190000
|
||||
scales=.1,.1
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=16
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
###########
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 6,7,8
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 8
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
|
||||
[route]
|
||||
layers = -3
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 6
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=1
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
|
@ -1,227 +0,0 @@
|
|||
[net]
|
||||
# Testing
|
||||
# batch=1
|
||||
# subdivisions=1
|
||||
# Training
|
||||
batch=64
|
||||
subdivisions=16
|
||||
width=608
|
||||
height=608
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 200000
|
||||
policy=steps
|
||||
steps=180000,190000
|
||||
scales=.1,.1
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=16
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
###########
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 6,7,8
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 8
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
|
||||
[route]
|
||||
layers = -3
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 6
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
788
cfg/yolov3.cfg
788
cfg/yolov3.cfg
|
|
@ -1,788 +0,0 @@
|
|||
[net]
|
||||
# Testing
|
||||
#batch=1
|
||||
#subdivisions=1
|
||||
# Training
|
||||
batch=16
|
||||
subdivisions=1
|
||||
width=416
|
||||
height=416
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 500200
|
||||
policy=steps
|
||||
steps=400000,450000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
######################
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 6,7,8
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 61
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 36
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=255
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=80
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
|
@ -1,80 +0,0 @@
|
|||
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
|
||||
|
|
@ -1,4 +0,0 @@
|
|||
classes=80
|
||||
train=data/coco1.txt
|
||||
valid=data/coco1.txt
|
||||
names=data/coco.names
|
||||
|
|
@ -1 +0,0 @@
|
|||
../coco/images/train2017/000000109622.jpg
|
||||
|
|
@ -1,4 +0,0 @@
|
|||
classes=80
|
||||
train=data/coco16.txt
|
||||
valid=data/coco16.txt
|
||||
names=data/coco.names
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
../coco/images/train2017/000000109622.jpg
|
||||
../coco/images/train2017/000000160694.jpg
|
||||
../coco/images/train2017/000000308590.jpg
|
||||
../coco/images/train2017/000000327573.jpg
|
||||
../coco/images/train2017/000000062929.jpg
|
||||
../coco/images/train2017/000000512793.jpg
|
||||
../coco/images/train2017/000000371735.jpg
|
||||
../coco/images/train2017/000000148118.jpg
|
||||
../coco/images/train2017/000000309856.jpg
|
||||
../coco/images/train2017/000000141882.jpg
|
||||
../coco/images/train2017/000000318783.jpg
|
||||
../coco/images/train2017/000000337760.jpg
|
||||
../coco/images/train2017/000000298197.jpg
|
||||
../coco/images/train2017/000000042421.jpg
|
||||
../coco/images/train2017/000000328898.jpg
|
||||
../coco/images/train2017/000000458856.jpg
|
||||
|
|
@ -1,4 +0,0 @@
|
|||
classes=1
|
||||
train=data/coco1cls.txt
|
||||
valid=data/coco1cls.txt
|
||||
names=data/coco.names
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
../coco/images/train2017/000000000901.jpg
|
||||
../coco/images/train2017/000000001464.jpg
|
||||
../coco/images/train2017/000000003220.jpg
|
||||
../coco/images/train2017/000000003365.jpg
|
||||
../coco/images/train2017/000000004772.jpg
|
||||
../coco/images/train2017/000000009987.jpg
|
||||
../coco/images/train2017/000000010498.jpg
|
||||
../coco/images/train2017/000000012455.jpg
|
||||
../coco/images/train2017/000000013992.jpg
|
||||
../coco/images/train2017/000000014125.jpg
|
||||
../coco/images/train2017/000000016314.jpg
|
||||
../coco/images/train2017/000000016670.jpg
|
||||
../coco/images/train2017/000000018412.jpg
|
||||
../coco/images/train2017/000000021212.jpg
|
||||
../coco/images/train2017/000000021826.jpg
|
||||
../coco/images/train2017/000000030566.jpg
|
||||
|
|
@ -1,4 +0,0 @@
|
|||
classes=80
|
||||
train=../coco/trainvalno5k.txt
|
||||
valid=../coco/5k.txt
|
||||
names=data/coco.names
|
||||
|
|
@ -1,4 +0,0 @@
|
|||
classes=80
|
||||
train=../coco/train2017.txt
|
||||
valid=../coco/val2017.txt
|
||||
names=data/coco.names
|
||||
|
|
@ -1,4 +0,0 @@
|
|||
classes=80
|
||||
train=data/coco64.txt
|
||||
valid=data/coco64.txt
|
||||
names=data/coco.names
|
||||
|
|
@ -1,64 +0,0 @@
|
|||
../coco/images/train2017/000000109622.jpg
|
||||
../coco/images/train2017/000000160694.jpg
|
||||
../coco/images/train2017/000000308590.jpg
|
||||
../coco/images/train2017/000000327573.jpg
|
||||
../coco/images/train2017/000000062929.jpg
|
||||
../coco/images/train2017/000000512793.jpg
|
||||
../coco/images/train2017/000000371735.jpg
|
||||
../coco/images/train2017/000000148118.jpg
|
||||
../coco/images/train2017/000000309856.jpg
|
||||
../coco/images/train2017/000000141882.jpg
|
||||
../coco/images/train2017/000000318783.jpg
|
||||
../coco/images/train2017/000000337760.jpg
|
||||
../coco/images/train2017/000000298197.jpg
|
||||
../coco/images/train2017/000000042421.jpg
|
||||
../coco/images/train2017/000000328898.jpg
|
||||
../coco/images/train2017/000000458856.jpg
|
||||
../coco/images/train2017/000000073824.jpg
|
||||
../coco/images/train2017/000000252846.jpg
|
||||
../coco/images/train2017/000000459590.jpg
|
||||
../coco/images/train2017/000000273650.jpg
|
||||
../coco/images/train2017/000000331311.jpg
|
||||
../coco/images/train2017/000000156326.jpg
|
||||
../coco/images/train2017/000000262985.jpg
|
||||
../coco/images/train2017/000000253580.jpg
|
||||
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../coco/images/train2017/000000342499.jpg
|
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|
|
@ -1,91 +0,0 @@
|
|||
person
|
||||
bicycle
|
||||
car
|
||||
motorcycle
|
||||
airplane
|
||||
bus
|
||||
train
|
||||
truck
|
||||
boat
|
||||
traffic light
|
||||
fire hydrant
|
||||
street sign
|
||||
stop sign
|
||||
parking meter
|
||||
bench
|
||||
bird
|
||||
cat
|
||||
dog
|
||||
horse
|
||||
sheep
|
||||
cow
|
||||
elephant
|
||||
bear
|
||||
zebra
|
||||
giraffe
|
||||
hat
|
||||
backpack
|
||||
umbrella
|
||||
shoe
|
||||
eye glasses
|
||||
handbag
|
||||
tie
|
||||
suitcase
|
||||
frisbee
|
||||
skis
|
||||
snowboard
|
||||
sports ball
|
||||
kite
|
||||
baseball bat
|
||||
baseball glove
|
||||
skateboard
|
||||
surfboard
|
||||
tennis racket
|
||||
bottle
|
||||
plate
|
||||
wine glass
|
||||
cup
|
||||
fork
|
||||
knife
|
||||
spoon
|
||||
bowl
|
||||
banana
|
||||
apple
|
||||
sandwich
|
||||
orange
|
||||
broccoli
|
||||
carrot
|
||||
hot dog
|
||||
pizza
|
||||
donut
|
||||
cake
|
||||
chair
|
||||
couch
|
||||
potted plant
|
||||
bed
|
||||
mirror
|
||||
dining table
|
||||
window
|
||||
desk
|
||||
toilet
|
||||
door
|
||||
tv
|
||||
laptop
|
||||
mouse
|
||||
remote
|
||||
keyboard
|
||||
cell phone
|
||||
microwave
|
||||
oven
|
||||
toaster
|
||||
sink
|
||||
refrigerator
|
||||
blender
|
||||
book
|
||||
clock
|
||||
vase
|
||||
scissors
|
||||
teddy bear
|
||||
hair drier
|
||||
toothbrush
|
||||
hair brush
|
||||
|
|
@ -1,24 +0,0 @@
|
|||
#!/bin/bash
|
||||
# Zip coco folder
|
||||
# zip -r coco.zip coco
|
||||
# tar -czvf coco.tar.gz coco
|
||||
|
||||
# Download labels from Google Drive, accepting presented query
|
||||
filename="coco2014labels.zip"
|
||||
fileid="1s6-CmF5_SElM28r52P1OUrCcuXZN-SFo"
|
||||
curl -c ./cookie -s -L "https://drive.google.com/uc?export=download&id=${fileid}" > /dev/null
|
||||
curl -Lb ./cookie "https://drive.google.com/uc?export=download&confirm=`awk '/download/ {print $NF}' ./cookie`&id=${fileid}" -o ${filename}
|
||||
rm ./cookie
|
||||
|
||||
# Unzip labels
|
||||
unzip -q ${filename} # for coco.zip
|
||||
# tar -xzf ${filename} # for coco.tar.gz
|
||||
rm ${filename}
|
||||
|
||||
# Download and unzip images
|
||||
cd coco/images
|
||||
f="train2014.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f
|
||||
f="val2014.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f
|
||||
|
||||
# cd out
|
||||
cd ../..
|
||||
|
|
@ -1,24 +0,0 @@
|
|||
#!/bin/bash
|
||||
# Zip coco folder
|
||||
# zip -r coco.zip coco
|
||||
# tar -czvf coco.tar.gz coco
|
||||
|
||||
# Download labels from Google Drive, accepting presented query
|
||||
filename="coco2017labels.zip"
|
||||
fileid="1cXZR_ckHki6nddOmcysCuuJFM--T-Q6L"
|
||||
curl -c ./cookie -s -L "https://drive.google.com/uc?export=download&id=${fileid}" > /dev/null
|
||||
curl -Lb ./cookie "https://drive.google.com/uc?export=download&confirm=`awk '/download/ {print $NF}' ./cookie`&id=${fileid}" -o ${filename}
|
||||
rm ./cookie
|
||||
|
||||
# Unzip labels
|
||||
unzip -q ${filename} # for coco.zip
|
||||
# tar -xzf ${filename} # for coco.tar.gz
|
||||
rm ${filename}
|
||||
|
||||
# Download and unzip images
|
||||
cd coco/images
|
||||
f="train2017.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f
|
||||
f="val2017.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f
|
||||
|
||||
# cd out
|
||||
cd ../..
|
||||
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|
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|
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|
|
@ -1,21 +0,0 @@
|
|||
# pip install -U -r requirements.txt
|
||||
# pycocotools requires numpy 1.17 https://github.com/cocodataset/cocoapi/issues/356
|
||||
numpy == 1.17
|
||||
opencv-python >= 4.1
|
||||
torch >= 1.5
|
||||
matplotlib
|
||||
pycocotools
|
||||
tqdm
|
||||
pillow
|
||||
tensorboard >= 1.14
|
||||
|
||||
# Nvidia Apex (optional) for mixed precision training --------------------------
|
||||
# git clone https://github.com/NVIDIA/apex && cd apex && pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" . --user && cd .. && rm -rf apex
|
||||
|
||||
# Conda commands (in place of pip) ---------------------------------------------
|
||||
# conda update -yn base -c defaults conda
|
||||
# conda install -yc anaconda numpy opencv matplotlib tqdm pillow ipython
|
||||
# conda install -yc conda-forge scikit-image pycocotools tensorboard
|
||||
# conda install -yc spyder-ide spyder-line-profiler
|
||||
# conda install -yc pytorch pytorch torchvision
|
||||
# conda install -yc conda-forge protobuf numpy && pip install onnx # https://github.com/onnx/onnx#linux-and-macos
|
||||
495
tutorial.ipynb
495
tutorial.ipynb
File diff suppressed because one or more lines are too long
|
|
@ -7,7 +7,7 @@
|
|||
|
||||
while true; do
|
||||
# python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 100 --batch 64 --accum 1 --weights yolov3-tiny.conv.15 --multi --bucket ult/wer --evolve --cache --device $1 --cfg yolov3-tiny3-1cls.cfg --single --adam
|
||||
# python3 train.py --data ../out/data.data --img-size 608 --epochs 10 --batch 8 --accum 8 --weights ultralytics68.pt --multi --bucket ult/athena --evolve --device $1 --cfg yolov3-spp-1cls.cfg
|
||||
# python3 train.py --data ../out/data.data --img-size 608 --epochs 10 --batch 8 --accum 8 --weights ultralytics68.pt --multi --bucket ult/athena --evolve --device $1 --cfg yolov3-spp-18cls.cfg
|
||||
|
||||
python3 train.py --data coco2014.data --img-size 512 608 --epochs 27 --batch 8 --accum 8 --evolve --weights '' --bucket ult/coco/sppa_512 --device $1 --cfg yolov3-sppa.cfg --multi
|
||||
done
|
||||
|
|
|
|||
Loading…
Reference in New Issue