mirror of
https://github.com/THU-MIG/yolov10.git
synced 2025-05-22 21:04:21 +08:00
YAML reformat (#7669)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
d021524e85
commit
63e7db1dac
41
.github/workflows/ci.yaml
vendored
41
.github/workflows/ci.yaml
vendored
@ -9,27 +9,27 @@ on:
|
|||||||
pull_request:
|
pull_request:
|
||||||
branches: [main]
|
branches: [main]
|
||||||
schedule:
|
schedule:
|
||||||
- cron: '0 0 * * *' # runs at 00:00 UTC every day
|
- cron: "0 0 * * *" # runs at 00:00 UTC every day
|
||||||
workflow_dispatch:
|
workflow_dispatch:
|
||||||
inputs:
|
inputs:
|
||||||
hub:
|
hub:
|
||||||
description: 'Run HUB'
|
description: "Run HUB"
|
||||||
default: false
|
default: false
|
||||||
type: boolean
|
type: boolean
|
||||||
benchmarks:
|
benchmarks:
|
||||||
description: 'Run Benchmarks'
|
description: "Run Benchmarks"
|
||||||
default: false
|
default: false
|
||||||
type: boolean
|
type: boolean
|
||||||
tests:
|
tests:
|
||||||
description: 'Run Tests'
|
description: "Run Tests"
|
||||||
default: false
|
default: false
|
||||||
type: boolean
|
type: boolean
|
||||||
gpu:
|
gpu:
|
||||||
description: 'Run GPU'
|
description: "Run GPU"
|
||||||
default: false
|
default: false
|
||||||
type: boolean
|
type: boolean
|
||||||
conda:
|
conda:
|
||||||
description: 'Run Conda'
|
description: "Run Conda"
|
||||||
default: false
|
default: false
|
||||||
type: boolean
|
type: boolean
|
||||||
|
|
||||||
@ -41,15 +41,15 @@ jobs:
|
|||||||
fail-fast: false
|
fail-fast: false
|
||||||
matrix:
|
matrix:
|
||||||
os: [ubuntu-latest]
|
os: [ubuntu-latest]
|
||||||
python-version: ['3.11']
|
python-version: ["3.11"]
|
||||||
steps:
|
steps:
|
||||||
- uses: actions/checkout@v4
|
- uses: actions/checkout@v4
|
||||||
- uses: actions/setup-python@v5
|
- uses: actions/setup-python@v5
|
||||||
with:
|
with:
|
||||||
python-version: ${{ matrix.python-version }}
|
python-version: ${{ matrix.python-version }}
|
||||||
cache: 'pip' # caching pip dependencies
|
cache: "pip" # caching pip dependencies
|
||||||
- name: Install requirements
|
- name: Install requirements
|
||||||
shell: bash # for Windows compatibility
|
shell: bash # for Windows compatibility
|
||||||
run: |
|
run: |
|
||||||
python -m pip install --upgrade pip wheel
|
python -m pip install --upgrade pip wheel
|
||||||
pip install -e . --extra-index-url https://download.pytorch.org/whl/cpu
|
pip install -e . --extra-index-url https://download.pytorch.org/whl/cpu
|
||||||
@ -95,16 +95,16 @@ jobs:
|
|||||||
fail-fast: false
|
fail-fast: false
|
||||||
matrix:
|
matrix:
|
||||||
os: [ubuntu-latest]
|
os: [ubuntu-latest]
|
||||||
python-version: ['3.11']
|
python-version: ["3.11"]
|
||||||
model: [yolov8n]
|
model: [yolov8n]
|
||||||
steps:
|
steps:
|
||||||
- uses: actions/checkout@v4
|
- uses: actions/checkout@v4
|
||||||
- uses: actions/setup-python@v5
|
- uses: actions/setup-python@v5
|
||||||
with:
|
with:
|
||||||
python-version: ${{ matrix.python-version }}
|
python-version: ${{ matrix.python-version }}
|
||||||
cache: 'pip' # caching pip dependencies
|
cache: "pip" # caching pip dependencies
|
||||||
- name: Install requirements
|
- name: Install requirements
|
||||||
shell: bash # for Windows compatibility
|
shell: bash # for Windows compatibility
|
||||||
run: |
|
run: |
|
||||||
python -m pip install --upgrade pip wheel
|
python -m pip install --upgrade pip wheel
|
||||||
pip install -e ".[export]" "coverage[toml]" --extra-index-url https://download.pytorch.org/whl/cpu
|
pip install -e ".[export]" "coverage[toml]" --extra-index-url https://download.pytorch.org/whl/cpu
|
||||||
@ -150,21 +150,22 @@ jobs:
|
|||||||
fail-fast: false
|
fail-fast: false
|
||||||
matrix:
|
matrix:
|
||||||
os: [ubuntu-latest]
|
os: [ubuntu-latest]
|
||||||
python-version: ['3.11']
|
python-version: ["3.11"]
|
||||||
torch: [latest]
|
torch: [latest]
|
||||||
include:
|
include:
|
||||||
- os: ubuntu-latest
|
- os: ubuntu-latest
|
||||||
python-version: '3.8' # torch 1.8.0 requires python >=3.6, <=3.8
|
python-version: "3.8" # torch 1.8.0 requires python >=3.6, <=3.8
|
||||||
torch: '1.8.0' # min torch version CI https://pypi.org/project/torchvision/
|
torch: "1.8.0" # min torch version CI https://pypi.org/project/torchvision/
|
||||||
steps:
|
steps:
|
||||||
- uses: actions/checkout@v4
|
- uses: actions/checkout@v4
|
||||||
- uses: actions/setup-python@v5
|
- uses: actions/setup-python@v5
|
||||||
with:
|
with:
|
||||||
python-version: ${{ matrix.python-version }}
|
python-version: ${{ matrix.python-version }}
|
||||||
cache: 'pip' # caching pip dependencies
|
cache: "pip" # caching pip dependencies
|
||||||
- name: Install requirements
|
- name: Install requirements
|
||||||
shell: bash # for Windows compatibility
|
shell: bash # for Windows compatibility
|
||||||
run: | # CoreML must be installed before export due to protobuf error from AutoInstall
|
run: |
|
||||||
|
# CoreML must be installed before export due to protobuf error from AutoInstall
|
||||||
python -m pip install --upgrade pip wheel
|
python -m pip install --upgrade pip wheel
|
||||||
torch=""
|
torch=""
|
||||||
if [ "${{ matrix.torch }}" == "1.8.0" ]; then
|
if [ "${{ matrix.torch }}" == "1.8.0" ]; then
|
||||||
@ -176,7 +177,7 @@ jobs:
|
|||||||
yolo checks
|
yolo checks
|
||||||
pip list
|
pip list
|
||||||
- name: Pytest tests
|
- name: Pytest tests
|
||||||
shell: bash # for Windows compatibility
|
shell: bash # for Windows compatibility
|
||||||
run: |
|
run: |
|
||||||
slow=""
|
slow=""
|
||||||
if [[ "${{ github.event_name }}" == "schedule" ]] || [[ "${{ github.event_name }}" == "workflow_dispatch" ]]; then
|
if [[ "${{ github.event_name }}" == "schedule" ]] || [[ "${{ github.event_name }}" == "workflow_dispatch" ]]; then
|
||||||
@ -220,7 +221,7 @@ jobs:
|
|||||||
fail-fast: false
|
fail-fast: false
|
||||||
matrix:
|
matrix:
|
||||||
os: [ubuntu-latest]
|
os: [ubuntu-latest]
|
||||||
python-version: ['3.11']
|
python-version: ["3.11"]
|
||||||
defaults:
|
defaults:
|
||||||
run:
|
run:
|
||||||
shell: bash -el {0}
|
shell: bash -el {0}
|
||||||
|
|||||||
8
.github/workflows/cla.yml
vendored
8
.github/workflows/cla.yml
vendored
@ -24,14 +24,14 @@ jobs:
|
|||||||
# must be repository secret token
|
# must be repository secret token
|
||||||
PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
|
PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
|
||||||
with:
|
with:
|
||||||
path-to-signatures: 'signatures/version1/cla.json'
|
path-to-signatures: "signatures/version1/cla.json"
|
||||||
path-to-document: 'https://docs.ultralytics.com/help/CLA' # CLA document
|
path-to-document: "https://docs.ultralytics.com/help/CLA" # CLA document
|
||||||
# branch should not be protected
|
# branch should not be protected
|
||||||
branch: 'main'
|
branch: "main"
|
||||||
allowlist: dependabot[bot],github-actions,[pre-commit*,pre-commit*,bot*
|
allowlist: dependabot[bot],github-actions,[pre-commit*,pre-commit*,bot*
|
||||||
|
|
||||||
remote-organization-name: ultralytics
|
remote-organization-name: ultralytics
|
||||||
remote-repository-name: cla
|
remote-repository-name: cla
|
||||||
custom-pr-sign-comment: 'I have read the CLA Document and I sign the CLA'
|
custom-pr-sign-comment: "I have read the CLA Document and I sign the CLA"
|
||||||
custom-allsigned-prcomment: All Contributors have signed the CLA. ✅
|
custom-allsigned-prcomment: All Contributors have signed the CLA. ✅
|
||||||
#custom-notsigned-prcomment: 'pull request comment with Introductory message to ask new contributors to sign'
|
#custom-notsigned-prcomment: 'pull request comment with Introductory message to ask new contributors to sign'
|
||||||
|
|||||||
4
.github/workflows/codeql.yaml
vendored
4
.github/workflows/codeql.yaml
vendored
@ -4,7 +4,7 @@ name: "CodeQL"
|
|||||||
|
|
||||||
on:
|
on:
|
||||||
schedule:
|
schedule:
|
||||||
- cron: '0 0 1 * *'
|
- cron: "0 0 1 * *"
|
||||||
workflow_dispatch:
|
workflow_dispatch:
|
||||||
|
|
||||||
jobs:
|
jobs:
|
||||||
@ -19,7 +19,7 @@ jobs:
|
|||||||
strategy:
|
strategy:
|
||||||
fail-fast: false
|
fail-fast: false
|
||||||
matrix:
|
matrix:
|
||||||
language: ['python']
|
language: ["python"]
|
||||||
# CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python', 'ruby' ]
|
# CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python', 'ruby' ]
|
||||||
|
|
||||||
steps:
|
steps:
|
||||||
|
|||||||
8
.github/workflows/docker.yaml
vendored
8
.github/workflows/docker.yaml
vendored
@ -64,7 +64,7 @@ jobs:
|
|||||||
- name: Checkout repo
|
- name: Checkout repo
|
||||||
uses: actions/checkout@v4
|
uses: actions/checkout@v4
|
||||||
with:
|
with:
|
||||||
fetch-depth: 0 # copy full .git directory to access full git history in Docker images
|
fetch-depth: 0 # copy full .git directory to access full git history in Docker images
|
||||||
|
|
||||||
- name: Set up QEMU
|
- name: Set up QEMU
|
||||||
uses: docker/setup-qemu-action@v3
|
uses: docker/setup-qemu-action@v3
|
||||||
@ -115,12 +115,12 @@ jobs:
|
|||||||
-t ultralytics/ultralytics:${{ steps.get_version.outputs.version_tag }} .
|
-t ultralytics/ultralytics:${{ steps.get_version.outputs.version_tag }} .
|
||||||
|
|
||||||
- name: Run Tests
|
- name: Run Tests
|
||||||
if: (github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true') && matrix.platforms == 'linux/amd64' && matrix.dockerfile != 'Dockerfile-conda' # arm64 images not supported on GitHub CI runners
|
if: (github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true') && matrix.platforms == 'linux/amd64' && matrix.dockerfile != 'Dockerfile-conda' # arm64 images not supported on GitHub CI runners
|
||||||
run: docker run ultralytics/ultralytics:${{ matrix.tags }} /bin/bash -c "pip install pytest && pytest tests"
|
run: docker run ultralytics/ultralytics:${{ matrix.tags }} /bin/bash -c "pip install pytest && pytest tests"
|
||||||
|
|
||||||
- name: Run Benchmarks
|
- name: Run Benchmarks
|
||||||
# WARNING: Dockerfile (GPU) error on TF.js export 'module 'numpy' has no attribute 'object'.
|
# WARNING: Dockerfile (GPU) error on TF.js export 'module 'numpy' has no attribute 'object'.
|
||||||
if: (github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true') && matrix.platforms == 'linux/amd64' && matrix.dockerfile != 'Dockerfile' && matrix.dockerfile != 'Dockerfile-conda' # arm64 images not supported on GitHub CI runners
|
if: (github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true') && matrix.platforms == 'linux/amd64' && matrix.dockerfile != 'Dockerfile' && matrix.dockerfile != 'Dockerfile-conda' # arm64 images not supported on GitHub CI runners
|
||||||
run: docker run ultralytics/ultralytics:${{ matrix.tags }} yolo benchmark model=yolov8n.pt imgsz=160 verbose=0.318
|
run: docker run ultralytics/ultralytics:${{ matrix.tags }} yolo benchmark model=yolov8n.pt imgsz=160 verbose=0.318
|
||||||
|
|
||||||
- name: Push Docker Image with Ultralytics version tag
|
- name: Push Docker Image with Ultralytics version tag
|
||||||
@ -139,7 +139,7 @@ jobs:
|
|||||||
fi
|
fi
|
||||||
|
|
||||||
- name: Notify on failure
|
- name: Notify on failure
|
||||||
if: github.event_name == 'push' && failure() # do not notify on cancelled() as cancelling is performed by hand
|
if: github.event_name == 'push' && failure() # do not notify on cancelled() as cancelling is performed by hand
|
||||||
uses: slackapi/slack-github-action@v1.24.0
|
uses: slackapi/slack-github-action@v1.24.0
|
||||||
with:
|
with:
|
||||||
payload: |
|
payload: |
|
||||||
|
|||||||
2
.github/workflows/format.yml
vendored
2
.github/workflows/format.yml
vendored
@ -17,7 +17,7 @@ jobs:
|
|||||||
- name: Run Ultralytics Formatting
|
- name: Run Ultralytics Formatting
|
||||||
uses: ultralytics/actions@main
|
uses: ultralytics/actions@main
|
||||||
with:
|
with:
|
||||||
token: ${{ secrets.GITHUB_TOKEN }} # automatically generated
|
token: ${{ secrets.GITHUB_TOKEN }} # automatically generated
|
||||||
python: true
|
python: true
|
||||||
docstrings: true
|
docstrings: true
|
||||||
markdown: true
|
markdown: true
|
||||||
|
|||||||
2
.github/workflows/links.yml
vendored
2
.github/workflows/links.yml
vendored
@ -12,7 +12,7 @@ name: Check Broken links
|
|||||||
on:
|
on:
|
||||||
workflow_dispatch:
|
workflow_dispatch:
|
||||||
schedule:
|
schedule:
|
||||||
- cron: '0 0 * * *' # runs at 00:00 UTC every day
|
- cron: "0 0 * * *" # runs at 00:00 UTC every day
|
||||||
|
|
||||||
jobs:
|
jobs:
|
||||||
Links:
|
Links:
|
||||||
|
|||||||
4
.github/workflows/publish.yml
vendored
4
.github/workflows/publish.yml
vendored
@ -28,8 +28,8 @@ jobs:
|
|||||||
- name: Set up Python environment
|
- name: Set up Python environment
|
||||||
uses: actions/setup-python@v5
|
uses: actions/setup-python@v5
|
||||||
with:
|
with:
|
||||||
python-version: '3.11'
|
python-version: "3.11"
|
||||||
cache: 'pip' # caching pip dependencies
|
cache: "pip" # caching pip dependencies
|
||||||
- name: Install dependencies
|
- name: Install dependencies
|
||||||
run: |
|
run: |
|
||||||
python -m pip install --upgrade pip wheel build twine
|
python -m pip install --upgrade pip wheel build twine
|
||||||
|
|||||||
6
.github/workflows/stale.yml
vendored
6
.github/workflows/stale.yml
vendored
@ -3,7 +3,7 @@
|
|||||||
name: Close stale issues
|
name: Close stale issues
|
||||||
on:
|
on:
|
||||||
schedule:
|
schedule:
|
||||||
- cron: '0 0 * * *' # Runs at 00:00 UTC every day
|
- cron: "0 0 * * *" # Runs at 00:00 UTC every day
|
||||||
|
|
||||||
jobs:
|
jobs:
|
||||||
stale:
|
stale:
|
||||||
@ -43,5 +43,5 @@ jobs:
|
|||||||
days-before-issue-close: 10
|
days-before-issue-close: 10
|
||||||
days-before-pr-stale: 90
|
days-before-pr-stale: 90
|
||||||
days-before-pr-close: 30
|
days-before-pr-close: 30
|
||||||
exempt-issue-labels: 'documentation,tutorial,TODO'
|
exempt-issue-labels: "documentation,tutorial,TODO"
|
||||||
operations-per-run: 300 # The maximum number of operations per run, used to control rate limiting.
|
operations-per-run: 300 # The maximum number of operations per run, used to control rate limiting.
|
||||||
|
|||||||
@ -5,7 +5,7 @@
|
|||||||
# Define bot property if installed via https://github.com/marketplace/pre-commit-ci
|
# Define bot property if installed via https://github.com/marketplace/pre-commit-ci
|
||||||
ci:
|
ci:
|
||||||
autofix_prs: true
|
autofix_prs: true
|
||||||
autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions'
|
autoupdate_commit_msg: "[pre-commit.ci] pre-commit suggestions"
|
||||||
autoupdate_schedule: monthly
|
autoupdate_schedule: monthly
|
||||||
submodules: true
|
submodules: true
|
||||||
|
|
||||||
@ -55,7 +55,7 @@ repos:
|
|||||||
rev: v2.2.6
|
rev: v2.2.6
|
||||||
hooks:
|
hooks:
|
||||||
- id: codespell
|
- id: codespell
|
||||||
exclude: 'docs/de|docs/fr|docs/pt|docs/es|docs/mkdocs_de.yml'
|
exclude: "docs/de|docs/fr|docs/pt|docs/es|docs/mkdocs_de.yml"
|
||||||
args:
|
args:
|
||||||
- --ignore-words-list=crate,nd,ned,strack,dota,ane,segway,fo,gool,winn,commend,bloc,nam,afterall
|
- --ignore-words-list=crate,nd,ned,strack,dota,ane,segway,fo,gool,winn,commend,bloc,nam,afterall
|
||||||
|
|
||||||
@ -64,7 +64,7 @@ repos:
|
|||||||
hooks:
|
hooks:
|
||||||
- id: pycln
|
- id: pycln
|
||||||
args: [--all]
|
args: [--all]
|
||||||
|
#
|
||||||
# - repo: https://github.com/PyCQA/docformatter
|
# - repo: https://github.com/PyCQA/docformatter
|
||||||
# rev: v1.7.5
|
# rev: v1.7.5
|
||||||
# hooks:
|
# hooks:
|
||||||
|
|||||||
@ -7,12 +7,11 @@
|
|||||||
# └── datasets
|
# └── datasets
|
||||||
# └── Argoverse ← downloads here (31.5 GB)
|
# └── Argoverse ← downloads here (31.5 GB)
|
||||||
|
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/Argoverse # dataset root dir
|
path: ../datasets/Argoverse # dataset root dir
|
||||||
train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
|
train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
|
||||||
val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
|
val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
|
||||||
test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
|
test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
|
||||||
|
|
||||||
# Classes
|
# Classes
|
||||||
names:
|
names:
|
||||||
@ -25,7 +24,6 @@ names:
|
|||||||
6: traffic_light
|
6: traffic_light
|
||||||
7: stop_sign
|
7: stop_sign
|
||||||
|
|
||||||
|
|
||||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||||
download: |
|
download: |
|
||||||
import json
|
import json
|
||||||
|
|||||||
@ -8,10 +8,10 @@
|
|||||||
# └── dota1.5 ← downloads here (2GB)
|
# └── dota1.5 ← downloads here (2GB)
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/DOTAv1.5 # dataset root dir
|
path: ../datasets/DOTAv1.5 # dataset root dir
|
||||||
train: images/train # train images (relative to 'path') 1411 images
|
train: images/train # train images (relative to 'path') 1411 images
|
||||||
val: images/val # val images (relative to 'path') 458 images
|
val: images/val # val images (relative to 'path') 458 images
|
||||||
test: images/test # test images (optional) 937 images
|
test: images/test # test images (optional) 937 images
|
||||||
|
|
||||||
# Classes for DOTA 1.5
|
# Classes for DOTA 1.5
|
||||||
names:
|
names:
|
||||||
|
|||||||
@ -8,10 +8,10 @@
|
|||||||
# └── dota1 ← downloads here (2GB)
|
# └── dota1 ← downloads here (2GB)
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/DOTAv1 # dataset root dir
|
path: ../datasets/DOTAv1 # dataset root dir
|
||||||
train: images/train # train images (relative to 'path') 1411 images
|
train: images/train # train images (relative to 'path') 1411 images
|
||||||
val: images/val # val images (relative to 'path') 458 images
|
val: images/val # val images (relative to 'path') 458 images
|
||||||
test: images/test # test images (optional) 937 images
|
test: images/test # test images (optional) 937 images
|
||||||
|
|
||||||
# Classes for DOTA 1.0
|
# Classes for DOTA 1.0
|
||||||
names:
|
names:
|
||||||
|
|||||||
@ -7,9 +7,8 @@
|
|||||||
# └── datasets
|
# └── datasets
|
||||||
# └── GlobalWheat2020 ← downloads here (7.0 GB)
|
# └── GlobalWheat2020 ← downloads here (7.0 GB)
|
||||||
|
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/GlobalWheat2020 # dataset root dir
|
path: ../datasets/GlobalWheat2020 # dataset root dir
|
||||||
train: # train images (relative to 'path') 3422 images
|
train: # train images (relative to 'path') 3422 images
|
||||||
- images/arvalis_1
|
- images/arvalis_1
|
||||||
- images/arvalis_2
|
- images/arvalis_2
|
||||||
@ -30,7 +29,6 @@ test: # test images (optional) 1276 images
|
|||||||
names:
|
names:
|
||||||
0: wheat_head
|
0: wheat_head
|
||||||
|
|
||||||
|
|
||||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||||
download: |
|
download: |
|
||||||
from ultralytics.utils.downloads import download
|
from ultralytics.utils.downloads import download
|
||||||
|
|||||||
@ -8,12 +8,11 @@
|
|||||||
# └── datasets
|
# └── datasets
|
||||||
# └── imagenet ← downloads here (144 GB)
|
# └── imagenet ← downloads here (144 GB)
|
||||||
|
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/imagenet # dataset root dir
|
path: ../datasets/imagenet # dataset root dir
|
||||||
train: train # train images (relative to 'path') 1281167 images
|
train: train # train images (relative to 'path') 1281167 images
|
||||||
val: val # val images (relative to 'path') 50000 images
|
val: val # val images (relative to 'path') 50000 images
|
||||||
test: # test images (optional)
|
test: # test images (optional)
|
||||||
|
|
||||||
# Classes
|
# Classes
|
||||||
names:
|
names:
|
||||||
@ -2021,6 +2020,5 @@ map:
|
|||||||
n13133613: ear
|
n13133613: ear
|
||||||
n15075141: toilet_tissue
|
n15075141: toilet_tissue
|
||||||
|
|
||||||
|
|
||||||
# Download script/URL (optional)
|
# Download script/URL (optional)
|
||||||
download: yolo/data/scripts/get_imagenet.sh
|
download: yolo/data/scripts/get_imagenet.sh
|
||||||
|
|||||||
@ -7,12 +7,11 @@
|
|||||||
# └── datasets
|
# └── datasets
|
||||||
# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips)
|
# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips)
|
||||||
|
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/Objects365 # dataset root dir
|
path: ../datasets/Objects365 # dataset root dir
|
||||||
train: images/train # train images (relative to 'path') 1742289 images
|
train: images/train # train images (relative to 'path') 1742289 images
|
||||||
val: images/val # val images (relative to 'path') 80000 images
|
val: images/val # val images (relative to 'path') 80000 images
|
||||||
test: # test images (optional)
|
test: # test images (optional)
|
||||||
|
|
||||||
# Classes
|
# Classes
|
||||||
names:
|
names:
|
||||||
@ -382,7 +381,6 @@ names:
|
|||||||
363: Curling
|
363: Curling
|
||||||
364: Table Tennis
|
364: Table Tennis
|
||||||
|
|
||||||
|
|
||||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||||
download: |
|
download: |
|
||||||
from tqdm import tqdm
|
from tqdm import tqdm
|
||||||
|
|||||||
@ -7,18 +7,16 @@
|
|||||||
# └── datasets
|
# └── datasets
|
||||||
# └── SKU-110K ← downloads here (13.6 GB)
|
# └── SKU-110K ← downloads here (13.6 GB)
|
||||||
|
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/SKU-110K # dataset root dir
|
path: ../datasets/SKU-110K # dataset root dir
|
||||||
train: train.txt # train images (relative to 'path') 8219 images
|
train: train.txt # train images (relative to 'path') 8219 images
|
||||||
val: val.txt # val images (relative to 'path') 588 images
|
val: val.txt # val images (relative to 'path') 588 images
|
||||||
test: test.txt # test images (optional) 2936 images
|
test: test.txt # test images (optional) 2936 images
|
||||||
|
|
||||||
# Classes
|
# Classes
|
||||||
names:
|
names:
|
||||||
0: object
|
0: object
|
||||||
|
|
||||||
|
|
||||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||||
download: |
|
download: |
|
||||||
import shutil
|
import shutil
|
||||||
|
|||||||
@ -7,7 +7,6 @@
|
|||||||
# └── datasets
|
# └── datasets
|
||||||
# └── VOC ← downloads here (2.8 GB)
|
# └── VOC ← downloads here (2.8 GB)
|
||||||
|
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/VOC
|
path: ../datasets/VOC
|
||||||
train: # train images (relative to 'path') 16551 images
|
train: # train images (relative to 'path') 16551 images
|
||||||
@ -43,7 +42,6 @@ names:
|
|||||||
18: train
|
18: train
|
||||||
19: tvmonitor
|
19: tvmonitor
|
||||||
|
|
||||||
|
|
||||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||||
download: |
|
download: |
|
||||||
import xml.etree.ElementTree as ET
|
import xml.etree.ElementTree as ET
|
||||||
|
|||||||
@ -7,12 +7,11 @@
|
|||||||
# └── datasets
|
# └── datasets
|
||||||
# └── VisDrone ← downloads here (2.3 GB)
|
# └── VisDrone ← downloads here (2.3 GB)
|
||||||
|
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/VisDrone # dataset root dir
|
path: ../datasets/VisDrone # dataset root dir
|
||||||
train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
|
train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
|
||||||
val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
|
val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
|
||||||
test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
|
test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
|
||||||
|
|
||||||
# Classes
|
# Classes
|
||||||
names:
|
names:
|
||||||
@ -27,7 +26,6 @@ names:
|
|||||||
8: bus
|
8: bus
|
||||||
9: motor
|
9: motor
|
||||||
|
|
||||||
|
|
||||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||||
download: |
|
download: |
|
||||||
import os
|
import os
|
||||||
|
|||||||
@ -7,15 +7,14 @@
|
|||||||
# └── datasets
|
# └── datasets
|
||||||
# └── coco-pose ← downloads here (20.1 GB)
|
# └── coco-pose ← downloads here (20.1 GB)
|
||||||
|
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/coco-pose # dataset root dir
|
path: ../datasets/coco-pose # dataset root dir
|
||||||
train: train2017.txt # train images (relative to 'path') 118287 images
|
train: train2017.txt # train images (relative to 'path') 118287 images
|
||||||
val: val2017.txt # val images (relative to 'path') 5000 images
|
val: val2017.txt # val images (relative to 'path') 5000 images
|
||||||
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
|
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
|
||||||
|
|
||||||
# Keypoints
|
# Keypoints
|
||||||
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||||
flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
|
flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
|
||||||
|
|
||||||
# Classes
|
# Classes
|
||||||
|
|||||||
@ -7,12 +7,11 @@
|
|||||||
# └── datasets
|
# └── datasets
|
||||||
# └── coco ← downloads here (20.1 GB)
|
# └── coco ← downloads here (20.1 GB)
|
||||||
|
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/coco # dataset root dir
|
path: ../datasets/coco # dataset root dir
|
||||||
train: train2017.txt # train images (relative to 'path') 118287 images
|
train: train2017.txt # train images (relative to 'path') 118287 images
|
||||||
val: val2017.txt # val images (relative to 'path') 5000 images
|
val: val2017.txt # val images (relative to 'path') 5000 images
|
||||||
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
|
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
|
||||||
|
|
||||||
# Classes
|
# Classes
|
||||||
names:
|
names:
|
||||||
@ -97,7 +96,6 @@ names:
|
|||||||
78: hair drier
|
78: hair drier
|
||||||
79: toothbrush
|
79: toothbrush
|
||||||
|
|
||||||
|
|
||||||
# Download script/URL (optional)
|
# Download script/URL (optional)
|
||||||
download: |
|
download: |
|
||||||
from ultralytics.utils.downloads import download
|
from ultralytics.utils.downloads import download
|
||||||
|
|||||||
@ -7,12 +7,11 @@
|
|||||||
# └── datasets
|
# └── datasets
|
||||||
# └── coco128-seg ← downloads here (7 MB)
|
# └── coco128-seg ← downloads here (7 MB)
|
||||||
|
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/coco128-seg # dataset root dir
|
path: ../datasets/coco128-seg # dataset root dir
|
||||||
train: images/train2017 # train images (relative to 'path') 128 images
|
train: images/train2017 # train images (relative to 'path') 128 images
|
||||||
val: images/train2017 # val images (relative to 'path') 128 images
|
val: images/train2017 # val images (relative to 'path') 128 images
|
||||||
test: # test images (optional)
|
test: # test images (optional)
|
||||||
|
|
||||||
# Classes
|
# Classes
|
||||||
names:
|
names:
|
||||||
@ -97,6 +96,5 @@ names:
|
|||||||
78: hair drier
|
78: hair drier
|
||||||
79: toothbrush
|
79: toothbrush
|
||||||
|
|
||||||
|
|
||||||
# Download script/URL (optional)
|
# Download script/URL (optional)
|
||||||
download: https://ultralytics.com/assets/coco128-seg.zip
|
download: https://ultralytics.com/assets/coco128-seg.zip
|
||||||
|
|||||||
@ -7,12 +7,11 @@
|
|||||||
# └── datasets
|
# └── datasets
|
||||||
# └── coco128 ← downloads here (7 MB)
|
# └── coco128 ← downloads here (7 MB)
|
||||||
|
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/coco128 # dataset root dir
|
path: ../datasets/coco128 # dataset root dir
|
||||||
train: images/train2017 # train images (relative to 'path') 128 images
|
train: images/train2017 # train images (relative to 'path') 128 images
|
||||||
val: images/train2017 # val images (relative to 'path') 128 images
|
val: images/train2017 # val images (relative to 'path') 128 images
|
||||||
test: # test images (optional)
|
test: # test images (optional)
|
||||||
|
|
||||||
# Classes
|
# Classes
|
||||||
names:
|
names:
|
||||||
@ -97,6 +96,5 @@ names:
|
|||||||
78: hair drier
|
78: hair drier
|
||||||
79: toothbrush
|
79: toothbrush
|
||||||
|
|
||||||
|
|
||||||
# Download script/URL (optional)
|
# Download script/URL (optional)
|
||||||
download: https://ultralytics.com/assets/coco128.zip
|
download: https://ultralytics.com/assets/coco128.zip
|
||||||
|
|||||||
@ -7,15 +7,14 @@
|
|||||||
# └── datasets
|
# └── datasets
|
||||||
# └── coco8-pose ← downloads here (1 MB)
|
# └── coco8-pose ← downloads here (1 MB)
|
||||||
|
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/coco8-pose # dataset root dir
|
path: ../datasets/coco8-pose # dataset root dir
|
||||||
train: images/train # train images (relative to 'path') 4 images
|
train: images/train # train images (relative to 'path') 4 images
|
||||||
val: images/val # val images (relative to 'path') 4 images
|
val: images/val # val images (relative to 'path') 4 images
|
||||||
test: # test images (optional)
|
test: # test images (optional)
|
||||||
|
|
||||||
# Keypoints
|
# Keypoints
|
||||||
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||||
flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
|
flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
|
||||||
|
|
||||||
# Classes
|
# Classes
|
||||||
|
|||||||
@ -7,12 +7,11 @@
|
|||||||
# └── datasets
|
# └── datasets
|
||||||
# └── coco8-seg ← downloads here (1 MB)
|
# └── coco8-seg ← downloads here (1 MB)
|
||||||
|
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/coco8-seg # dataset root dir
|
path: ../datasets/coco8-seg # dataset root dir
|
||||||
train: images/train # train images (relative to 'path') 4 images
|
train: images/train # train images (relative to 'path') 4 images
|
||||||
val: images/val # val images (relative to 'path') 4 images
|
val: images/val # val images (relative to 'path') 4 images
|
||||||
test: # test images (optional)
|
test: # test images (optional)
|
||||||
|
|
||||||
# Classes
|
# Classes
|
||||||
names:
|
names:
|
||||||
@ -97,6 +96,5 @@ names:
|
|||||||
78: hair drier
|
78: hair drier
|
||||||
79: toothbrush
|
79: toothbrush
|
||||||
|
|
||||||
|
|
||||||
# Download script/URL (optional)
|
# Download script/URL (optional)
|
||||||
download: https://ultralytics.com/assets/coco8-seg.zip
|
download: https://ultralytics.com/assets/coco8-seg.zip
|
||||||
|
|||||||
@ -7,12 +7,11 @@
|
|||||||
# └── datasets
|
# └── datasets
|
||||||
# └── coco8 ← downloads here (1 MB)
|
# └── coco8 ← downloads here (1 MB)
|
||||||
|
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/coco8 # dataset root dir
|
path: ../datasets/coco8 # dataset root dir
|
||||||
train: images/train # train images (relative to 'path') 4 images
|
train: images/train # train images (relative to 'path') 4 images
|
||||||
val: images/val # val images (relative to 'path') 4 images
|
val: images/val # val images (relative to 'path') 4 images
|
||||||
test: # test images (optional)
|
test: # test images (optional)
|
||||||
|
|
||||||
# Classes
|
# Classes
|
||||||
names:
|
names:
|
||||||
@ -97,6 +96,5 @@ names:
|
|||||||
78: hair drier
|
78: hair drier
|
||||||
79: toothbrush
|
79: toothbrush
|
||||||
|
|
||||||
|
|
||||||
# Download script/URL (optional)
|
# Download script/URL (optional)
|
||||||
download: https://ultralytics.com/assets/coco8.zip
|
download: https://ultralytics.com/assets/coco8.zip
|
||||||
|
|||||||
@ -8,9 +8,9 @@
|
|||||||
# └── dota8 ← downloads here (1MB)
|
# └── dota8 ← downloads here (1MB)
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/dota8 # dataset root dir
|
path: ../datasets/dota8 # dataset root dir
|
||||||
train: images/train # train images (relative to 'path') 4 images
|
train: images/train # train images (relative to 'path') 4 images
|
||||||
val: images/val # val images (relative to 'path') 4 images
|
val: images/val # val images (relative to 'path') 4 images
|
||||||
|
|
||||||
# Classes for DOTA 1.0
|
# Classes for DOTA 1.0
|
||||||
names:
|
names:
|
||||||
|
|||||||
@ -7,12 +7,11 @@
|
|||||||
# └── datasets
|
# └── datasets
|
||||||
# └── open-images-v7 ← downloads here (561 GB)
|
# └── open-images-v7 ← downloads here (561 GB)
|
||||||
|
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/open-images-v7 # dataset root dir
|
path: ../datasets/open-images-v7 # dataset root dir
|
||||||
train: images/train # train images (relative to 'path') 1743042 images
|
train: images/train # train images (relative to 'path') 1743042 images
|
||||||
val: images/val # val images (relative to 'path') 41620 images
|
val: images/val # val images (relative to 'path') 41620 images
|
||||||
test: # test images (optional)
|
test: # test images (optional)
|
||||||
|
|
||||||
# Classes
|
# Classes
|
||||||
names:
|
names:
|
||||||
@ -618,7 +617,6 @@ names:
|
|||||||
599: Zebra
|
599: Zebra
|
||||||
600: Zucchini
|
600: Zucchini
|
||||||
|
|
||||||
|
|
||||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||||
download: |
|
download: |
|
||||||
from ultralytics.utils import LOGGER, SETTINGS, Path, is_ubuntu, get_ubuntu_version
|
from ultralytics.utils import LOGGER, SETTINGS, Path, is_ubuntu, get_ubuntu_version
|
||||||
|
|||||||
@ -7,14 +7,13 @@
|
|||||||
# └── datasets
|
# └── datasets
|
||||||
# └── tiger-pose ← downloads here (75.3 MB)
|
# └── tiger-pose ← downloads here (75.3 MB)
|
||||||
|
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/tiger-pose # dataset root dir
|
path: ../datasets/tiger-pose # dataset root dir
|
||||||
train: train # train images (relative to 'path') 210 images
|
train: train # train images (relative to 'path') 210 images
|
||||||
val: val # val images (relative to 'path') 53 images
|
val: val # val images (relative to 'path') 53 images
|
||||||
|
|
||||||
# Keypoints
|
# Keypoints
|
||||||
kpt_shape: [12, 2] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
kpt_shape: [12, 2] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||||
flip_idx: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
|
flip_idx: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
|
||||||
|
|
||||||
# Classes
|
# Classes
|
||||||
|
|||||||
@ -8,11 +8,10 @@
|
|||||||
# └── datasets
|
# └── datasets
|
||||||
# └── xView ← downloads here (20.7 GB)
|
# └── xView ← downloads here (20.7 GB)
|
||||||
|
|
||||||
|
|
||||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||||
path: ../datasets/xView # dataset root dir
|
path: ../datasets/xView # dataset root dir
|
||||||
train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images
|
train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images
|
||||||
val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images
|
val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images
|
||||||
|
|
||||||
# Classes
|
# Classes
|
||||||
names:
|
names:
|
||||||
@ -77,7 +76,6 @@ names:
|
|||||||
58: Pylon
|
58: Pylon
|
||||||
59: Tower
|
59: Tower
|
||||||
|
|
||||||
|
|
||||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||||
download: |
|
download: |
|
||||||
import json
|
import json
|
||||||
|
|||||||
@ -1,125 +1,125 @@
|
|||||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||||
# Default training settings and hyperparameters for medium-augmentation COCO training
|
# Default training settings and hyperparameters for medium-augmentation COCO training
|
||||||
|
|
||||||
task: detect # (str) YOLO task, i.e. detect, segment, classify, pose
|
task: detect # (str) YOLO task, i.e. detect, segment, classify, pose
|
||||||
mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark
|
mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark
|
||||||
|
|
||||||
# Train settings -------------------------------------------------------------------------------------------------------
|
# Train settings -------------------------------------------------------------------------------------------------------
|
||||||
model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml
|
model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml
|
||||||
data: # (str, optional) path to data file, i.e. coco128.yaml
|
data: # (str, optional) path to data file, i.e. coco128.yaml
|
||||||
epochs: 100 # (int) number of epochs to train for
|
epochs: 100 # (int) number of epochs to train for
|
||||||
time: # (float, optional) number of hours to train for, overrides epochs if supplied
|
time: # (float, optional) number of hours to train for, overrides epochs if supplied
|
||||||
patience: 50 # (int) epochs to wait for no observable improvement for early stopping of training
|
patience: 50 # (int) epochs to wait for no observable improvement for early stopping of training
|
||||||
batch: 16 # (int) number of images per batch (-1 for AutoBatch)
|
batch: 16 # (int) number of images per batch (-1 for AutoBatch)
|
||||||
imgsz: 640 # (int | list) input images size as int for train and val modes, or list[w,h] for predict and export modes
|
imgsz: 640 # (int | list) input images size as int for train and val modes, or list[w,h] for predict and export modes
|
||||||
save: True # (bool) save train checkpoints and predict results
|
save: True # (bool) save train checkpoints and predict results
|
||||||
save_period: -1 # (int) Save checkpoint every x epochs (disabled if < 1)
|
save_period: -1 # (int) Save checkpoint every x epochs (disabled if < 1)
|
||||||
cache: False # (bool) True/ram, disk or False. Use cache for data loading
|
cache: False # (bool) True/ram, disk or False. Use cache for data loading
|
||||||
device: # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
|
device: # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
|
||||||
workers: 8 # (int) number of worker threads for data loading (per RANK if DDP)
|
workers: 8 # (int) number of worker threads for data loading (per RANK if DDP)
|
||||||
project: # (str, optional) project name
|
project: # (str, optional) project name
|
||||||
name: # (str, optional) experiment name, results saved to 'project/name' directory
|
name: # (str, optional) experiment name, results saved to 'project/name' directory
|
||||||
exist_ok: False # (bool) whether to overwrite existing experiment
|
exist_ok: False # (bool) whether to overwrite existing experiment
|
||||||
pretrained: True # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
|
pretrained: True # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
|
||||||
optimizer: auto # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
|
optimizer: auto # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
|
||||||
verbose: True # (bool) whether to print verbose output
|
verbose: True # (bool) whether to print verbose output
|
||||||
seed: 0 # (int) random seed for reproducibility
|
seed: 0 # (int) random seed for reproducibility
|
||||||
deterministic: True # (bool) whether to enable deterministic mode
|
deterministic: True # (bool) whether to enable deterministic mode
|
||||||
single_cls: False # (bool) train multi-class data as single-class
|
single_cls: False # (bool) train multi-class data as single-class
|
||||||
rect: False # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
|
rect: False # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
|
||||||
cos_lr: False # (bool) use cosine learning rate scheduler
|
cos_lr: False # (bool) use cosine learning rate scheduler
|
||||||
close_mosaic: 10 # (int) disable mosaic augmentation for final epochs (0 to disable)
|
close_mosaic: 10 # (int) disable mosaic augmentation for final epochs (0 to disable)
|
||||||
resume: False # (bool) resume training from last checkpoint
|
resume: False # (bool) resume training from last checkpoint
|
||||||
amp: True # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
|
amp: True # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
|
||||||
fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images in train set)
|
fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images in train set)
|
||||||
profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers
|
profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers
|
||||||
freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training
|
freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training
|
||||||
multi_scale: False # (bool) Whether to use multi-scale during training
|
multi_scale: False # (bool) Whether to use multi-scale during training
|
||||||
# Segmentation
|
# Segmentation
|
||||||
overlap_mask: True # (bool) masks should overlap during training (segment train only)
|
overlap_mask: True # (bool) masks should overlap during training (segment train only)
|
||||||
mask_ratio: 4 # (int) mask downsample ratio (segment train only)
|
mask_ratio: 4 # (int) mask downsample ratio (segment train only)
|
||||||
# Classification
|
# Classification
|
||||||
dropout: 0.0 # (float) use dropout regularization (classify train only)
|
dropout: 0.0 # (float) use dropout regularization (classify train only)
|
||||||
|
|
||||||
# Val/Test settings ----------------------------------------------------------------------------------------------------
|
# Val/Test settings ----------------------------------------------------------------------------------------------------
|
||||||
val: True # (bool) validate/test during training
|
val: True # (bool) validate/test during training
|
||||||
split: val # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
|
split: val # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
|
||||||
save_json: False # (bool) save results to JSON file
|
save_json: False # (bool) save results to JSON file
|
||||||
save_hybrid: False # (bool) save hybrid version of labels (labels + additional predictions)
|
save_hybrid: False # (bool) save hybrid version of labels (labels + additional predictions)
|
||||||
conf: # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
|
conf: # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
|
||||||
iou: 0.7 # (float) intersection over union (IoU) threshold for NMS
|
iou: 0.7 # (float) intersection over union (IoU) threshold for NMS
|
||||||
max_det: 300 # (int) maximum number of detections per image
|
max_det: 300 # (int) maximum number of detections per image
|
||||||
half: False # (bool) use half precision (FP16)
|
half: False # (bool) use half precision (FP16)
|
||||||
dnn: False # (bool) use OpenCV DNN for ONNX inference
|
dnn: False # (bool) use OpenCV DNN for ONNX inference
|
||||||
plots: True # (bool) save plots and images during train/val
|
plots: True # (bool) save plots and images during train/val
|
||||||
|
|
||||||
# Predict settings -----------------------------------------------------------------------------------------------------
|
# Predict settings -----------------------------------------------------------------------------------------------------
|
||||||
source: # (str, optional) source directory for images or videos
|
source: # (str, optional) source directory for images or videos
|
||||||
vid_stride: 1 # (int) video frame-rate stride
|
vid_stride: 1 # (int) video frame-rate stride
|
||||||
stream_buffer: False # (bool) buffer all streaming frames (True) or return the most recent frame (False)
|
stream_buffer: False # (bool) buffer all streaming frames (True) or return the most recent frame (False)
|
||||||
visualize: False # (bool) visualize model features
|
visualize: False # (bool) visualize model features
|
||||||
augment: False # (bool) apply image augmentation to prediction sources
|
augment: False # (bool) apply image augmentation to prediction sources
|
||||||
agnostic_nms: False # (bool) class-agnostic NMS
|
agnostic_nms: False # (bool) class-agnostic NMS
|
||||||
classes: # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
|
classes: # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
|
||||||
retina_masks: False # (bool) use high-resolution segmentation masks
|
retina_masks: False # (bool) use high-resolution segmentation masks
|
||||||
embed: # (list[int], optional) return feature vectors/embeddings from given layers
|
embed: # (list[int], optional) return feature vectors/embeddings from given layers
|
||||||
|
|
||||||
# Visualize settings ---------------------------------------------------------------------------------------------------
|
# Visualize settings ---------------------------------------------------------------------------------------------------
|
||||||
show: False # (bool) show predicted images and videos if environment allows
|
show: False # (bool) show predicted images and videos if environment allows
|
||||||
save_frames: False # (bool) save predicted individual video frames
|
save_frames: False # (bool) save predicted individual video frames
|
||||||
save_txt: False # (bool) save results as .txt file
|
save_txt: False # (bool) save results as .txt file
|
||||||
save_conf: False # (bool) save results with confidence scores
|
save_conf: False # (bool) save results with confidence scores
|
||||||
save_crop: False # (bool) save cropped images with results
|
save_crop: False # (bool) save cropped images with results
|
||||||
show_labels: True # (bool) show prediction labels, i.e. 'person'
|
show_labels: True # (bool) show prediction labels, i.e. 'person'
|
||||||
show_conf: True # (bool) show prediction confidence, i.e. '0.99'
|
show_conf: True # (bool) show prediction confidence, i.e. '0.99'
|
||||||
show_boxes: True # (bool) show prediction boxes
|
show_boxes: True # (bool) show prediction boxes
|
||||||
line_width: # (int, optional) line width of the bounding boxes. Scaled to image size if None.
|
line_width: # (int, optional) line width of the bounding boxes. Scaled to image size if None.
|
||||||
|
|
||||||
# Export settings ------------------------------------------------------------------------------------------------------
|
# Export settings ------------------------------------------------------------------------------------------------------
|
||||||
format: torchscript # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats
|
format: torchscript # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats
|
||||||
keras: False # (bool) use Kera=s
|
keras: False # (bool) use Kera=s
|
||||||
optimize: False # (bool) TorchScript: optimize for mobile
|
optimize: False # (bool) TorchScript: optimize for mobile
|
||||||
int8: False # (bool) CoreML/TF INT8 quantization
|
int8: False # (bool) CoreML/TF INT8 quantization
|
||||||
dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes
|
dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes
|
||||||
simplify: False # (bool) ONNX: simplify model
|
simplify: False # (bool) ONNX: simplify model
|
||||||
opset: # (int, optional) ONNX: opset version
|
opset: # (int, optional) ONNX: opset version
|
||||||
workspace: 4 # (int) TensorRT: workspace size (GB)
|
workspace: 4 # (int) TensorRT: workspace size (GB)
|
||||||
nms: False # (bool) CoreML: add NMS
|
nms: False # (bool) CoreML: add NMS
|
||||||
|
|
||||||
# Hyperparameters ------------------------------------------------------------------------------------------------------
|
# Hyperparameters ------------------------------------------------------------------------------------------------------
|
||||||
lr0: 0.01 # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
|
lr0: 0.01 # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
|
||||||
lrf: 0.01 # (float) final learning rate (lr0 * lrf)
|
lrf: 0.01 # (float) final learning rate (lr0 * lrf)
|
||||||
momentum: 0.937 # (float) SGD momentum/Adam beta1
|
momentum: 0.937 # (float) SGD momentum/Adam beta1
|
||||||
weight_decay: 0.0005 # (float) optimizer weight decay 5e-4
|
weight_decay: 0.0005 # (float) optimizer weight decay 5e-4
|
||||||
warmup_epochs: 3.0 # (float) warmup epochs (fractions ok)
|
warmup_epochs: 3.0 # (float) warmup epochs (fractions ok)
|
||||||
warmup_momentum: 0.8 # (float) warmup initial momentum
|
warmup_momentum: 0.8 # (float) warmup initial momentum
|
||||||
warmup_bias_lr: 0.1 # (float) warmup initial bias lr
|
warmup_bias_lr: 0.1 # (float) warmup initial bias lr
|
||||||
box: 7.5 # (float) box loss gain
|
box: 7.5 # (float) box loss gain
|
||||||
cls: 0.5 # (float) cls loss gain (scale with pixels)
|
cls: 0.5 # (float) cls loss gain (scale with pixels)
|
||||||
dfl: 1.5 # (float) dfl loss gain
|
dfl: 1.5 # (float) dfl loss gain
|
||||||
pose: 12.0 # (float) pose loss gain
|
pose: 12.0 # (float) pose loss gain
|
||||||
kobj: 1.0 # (float) keypoint obj loss gain
|
kobj: 1.0 # (float) keypoint obj loss gain
|
||||||
label_smoothing: 0.0 # (float) label smoothing (fraction)
|
label_smoothing: 0.0 # (float) label smoothing (fraction)
|
||||||
nbs: 64 # (int) nominal batch size
|
nbs: 64 # (int) nominal batch size
|
||||||
hsv_h: 0.015 # (float) image HSV-Hue augmentation (fraction)
|
hsv_h: 0.015 # (float) image HSV-Hue augmentation (fraction)
|
||||||
hsv_s: 0.7 # (float) image HSV-Saturation augmentation (fraction)
|
hsv_s: 0.7 # (float) image HSV-Saturation augmentation (fraction)
|
||||||
hsv_v: 0.4 # (float) image HSV-Value augmentation (fraction)
|
hsv_v: 0.4 # (float) image HSV-Value augmentation (fraction)
|
||||||
degrees: 0.0 # (float) image rotation (+/- deg)
|
degrees: 0.0 # (float) image rotation (+/- deg)
|
||||||
translate: 0.1 # (float) image translation (+/- fraction)
|
translate: 0.1 # (float) image translation (+/- fraction)
|
||||||
scale: 0.5 # (float) image scale (+/- gain)
|
scale: 0.5 # (float) image scale (+/- gain)
|
||||||
shear: 0.0 # (float) image shear (+/- deg)
|
shear: 0.0 # (float) image shear (+/- deg)
|
||||||
perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001
|
perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001
|
||||||
flipud: 0.0 # (float) image flip up-down (probability)
|
flipud: 0.0 # (float) image flip up-down (probability)
|
||||||
fliplr: 0.5 # (float) image flip left-right (probability)
|
fliplr: 0.5 # (float) image flip left-right (probability)
|
||||||
mosaic: 1.0 # (float) image mosaic (probability)
|
mosaic: 1.0 # (float) image mosaic (probability)
|
||||||
mixup: 0.0 # (float) image mixup (probability)
|
mixup: 0.0 # (float) image mixup (probability)
|
||||||
copy_paste: 0.0 # (float) segment copy-paste (probability)
|
copy_paste: 0.0 # (float) segment copy-paste (probability)
|
||||||
auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)
|
auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)
|
||||||
erasing: 0.4 # (float) probability of random erasing during classification training (0-1)
|
erasing: 0.4 # (float) probability of random erasing during classification training (0-1)
|
||||||
crop_fraction: 1.0 # (float) image crop fraction for classification evaluation/inference (0-1)
|
crop_fraction: 1.0 # (float) image crop fraction for classification evaluation/inference (0-1)
|
||||||
|
|
||||||
# Custom config.yaml ---------------------------------------------------------------------------------------------------
|
# Custom config.yaml ---------------------------------------------------------------------------------------------------
|
||||||
cfg: # (str, optional) for overriding defaults.yaml
|
cfg: # (str, optional) for overriding defaults.yaml
|
||||||
|
|
||||||
# Tracker settings ------------------------------------------------------------------------------------------------------
|
# Tracker settings ------------------------------------------------------------------------------------------------------
|
||||||
tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]
|
tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]
|
||||||
|
|||||||
@ -2,49 +2,49 @@
|
|||||||
# RT-DETR-l object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/rtdetr
|
# RT-DETR-l object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/rtdetr
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
l: [1.00, 1.00, 1024]
|
l: [1.00, 1.00, 1024]
|
||||||
|
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, HGStem, [32, 48]] # 0-P2/4
|
- [-1, 1, HGStem, [32, 48]] # 0-P2/4
|
||||||
- [-1, 6, HGBlock, [48, 128, 3]] # stage 1
|
- [-1, 6, HGBlock, [48, 128, 3]] # stage 1
|
||||||
|
|
||||||
- [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
|
- [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
|
||||||
- [-1, 6, HGBlock, [96, 512, 3]] # stage 2
|
- [-1, 6, HGBlock, [96, 512, 3]] # stage 2
|
||||||
|
|
||||||
- [-1, 1, DWConv, [512, 3, 2, 1, False]] # 4-P3/16
|
- [-1, 1, DWConv, [512, 3, 2, 1, False]] # 4-P3/16
|
||||||
- [-1, 6, HGBlock, [192, 1024, 5, True, False]] # cm, c2, k, light, shortcut
|
- [-1, 6, HGBlock, [192, 1024, 5, True, False]] # cm, c2, k, light, shortcut
|
||||||
- [-1, 6, HGBlock, [192, 1024, 5, True, True]]
|
- [-1, 6, HGBlock, [192, 1024, 5, True, True]]
|
||||||
- [-1, 6, HGBlock, [192, 1024, 5, True, True]] # stage 3
|
- [-1, 6, HGBlock, [192, 1024, 5, True, True]] # stage 3
|
||||||
|
|
||||||
- [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 8-P4/32
|
- [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 8-P4/32
|
||||||
- [-1, 6, HGBlock, [384, 2048, 5, True, False]] # stage 4
|
- [-1, 6, HGBlock, [384, 2048, 5, True, False]] # stage 4
|
||||||
|
|
||||||
head:
|
head:
|
||||||
- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 10 input_proj.2
|
- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 10 input_proj.2
|
||||||
- [-1, 1, AIFI, [1024, 8]]
|
- [-1, 1, AIFI, [1024, 8]]
|
||||||
- [-1, 1, Conv, [256, 1, 1]] # 12, Y5, lateral_convs.0
|
- [-1, 1, Conv, [256, 1, 1]] # 12, Y5, lateral_convs.0
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [7, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14 input_proj.1
|
- [7, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14 input_proj.1
|
||||||
- [[-2, -1], 1, Concat, [1]]
|
- [[-2, -1], 1, Concat, [1]]
|
||||||
- [-1, 3, RepC3, [256]] # 16, fpn_blocks.0
|
- [-1, 3, RepC3, [256]] # 16, fpn_blocks.0
|
||||||
- [-1, 1, Conv, [256, 1, 1]] # 17, Y4, lateral_convs.1
|
- [-1, 1, Conv, [256, 1, 1]] # 17, Y4, lateral_convs.1
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 19 input_proj.0
|
- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 19 input_proj.0
|
||||||
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, RepC3, [256]] # X3 (21), fpn_blocks.1
|
- [-1, 3, RepC3, [256]] # X3 (21), fpn_blocks.1
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 22, downsample_convs.0
|
- [-1, 1, Conv, [256, 3, 2]] # 22, downsample_convs.0
|
||||||
- [[-1, 17], 1, Concat, [1]] # cat Y4
|
- [[-1, 17], 1, Concat, [1]] # cat Y4
|
||||||
- [-1, 3, RepC3, [256]] # F4 (24), pan_blocks.0
|
- [-1, 3, RepC3, [256]] # F4 (24), pan_blocks.0
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 25, downsample_convs.1
|
- [-1, 1, Conv, [256, 3, 2]] # 25, downsample_convs.1
|
||||||
- [[-1, 12], 1, Concat, [1]] # cat Y5
|
- [[-1, 12], 1, Concat, [1]] # cat Y5
|
||||||
- [-1, 3, RepC3, [256]] # F5 (27), pan_blocks.1
|
- [-1, 3, RepC3, [256]] # F5 (27), pan_blocks.1
|
||||||
|
|
||||||
- [[21, 24, 27], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
- [[21, 24, 27], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||||
|
|||||||
@ -2,41 +2,41 @@
|
|||||||
# RT-DETR-ResNet101 object detection model with P3-P5 outputs.
|
# RT-DETR-ResNet101 object detection model with P3-P5 outputs.
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
l: [1.00, 1.00, 1024]
|
l: [1.00, 1.00, 1024]
|
||||||
|
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0
|
- [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0
|
||||||
- [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1
|
- [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1
|
||||||
- [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2
|
- [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2
|
||||||
- [-1, 1, ResNetLayer, [512, 256, 2, False, 23]] # 3
|
- [-1, 1, ResNetLayer, [512, 256, 2, False, 23]] # 3
|
||||||
- [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4
|
- [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4
|
||||||
|
|
||||||
head:
|
head:
|
||||||
- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 5
|
- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 5
|
||||||
- [-1, 1, AIFI, [1024, 8]]
|
- [-1, 1, AIFI, [1024, 8]]
|
||||||
- [-1, 1, Conv, [256, 1, 1]] # 7
|
- [-1, 1, Conv, [256, 1, 1]] # 7
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9
|
- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9
|
||||||
- [[-2, -1], 1, Concat, [1]]
|
- [[-2, -1], 1, Concat, [1]]
|
||||||
- [-1, 3, RepC3, [256]] # 11
|
- [-1, 3, RepC3, [256]] # 11
|
||||||
- [-1, 1, Conv, [256, 1, 1]] # 12
|
- [-1, 1, Conv, [256, 1, 1]] # 12
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [2, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14
|
- [2, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14
|
||||||
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, RepC3, [256]] # X3 (16), fpn_blocks.1
|
- [-1, 3, RepC3, [256]] # X3 (16), fpn_blocks.1
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 17, downsample_convs.0
|
- [-1, 1, Conv, [256, 3, 2]] # 17, downsample_convs.0
|
||||||
- [[-1, 12], 1, Concat, [1]] # cat Y4
|
- [[-1, 12], 1, Concat, [1]] # cat Y4
|
||||||
- [-1, 3, RepC3, [256]] # F4 (19), pan_blocks.0
|
- [-1, 3, RepC3, [256]] # F4 (19), pan_blocks.0
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 20, downsample_convs.1
|
- [-1, 1, Conv, [256, 3, 2]] # 20, downsample_convs.1
|
||||||
- [[-1, 7], 1, Concat, [1]] # cat Y5
|
- [[-1, 7], 1, Concat, [1]] # cat Y5
|
||||||
- [-1, 3, RepC3, [256]] # F5 (22), pan_blocks.1
|
- [-1, 3, RepC3, [256]] # F5 (22), pan_blocks.1
|
||||||
|
|
||||||
- [[16, 19, 22], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
- [[16, 19, 22], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||||
|
|||||||
@ -2,41 +2,41 @@
|
|||||||
# RT-DETR-ResNet50 object detection model with P3-P5 outputs.
|
# RT-DETR-ResNet50 object detection model with P3-P5 outputs.
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
l: [1.00, 1.00, 1024]
|
l: [1.00, 1.00, 1024]
|
||||||
|
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0
|
- [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0
|
||||||
- [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1
|
- [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1
|
||||||
- [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2
|
- [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2
|
||||||
- [-1, 1, ResNetLayer, [512, 256, 2, False, 6]] # 3
|
- [-1, 1, ResNetLayer, [512, 256, 2, False, 6]] # 3
|
||||||
- [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4
|
- [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4
|
||||||
|
|
||||||
head:
|
head:
|
||||||
- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 5
|
- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 5
|
||||||
- [-1, 1, AIFI, [1024, 8]]
|
- [-1, 1, AIFI, [1024, 8]]
|
||||||
- [-1, 1, Conv, [256, 1, 1]] # 7
|
- [-1, 1, Conv, [256, 1, 1]] # 7
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9
|
- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9
|
||||||
- [[-2, -1], 1, Concat, [1]]
|
- [[-2, -1], 1, Concat, [1]]
|
||||||
- [-1, 3, RepC3, [256]] # 11
|
- [-1, 3, RepC3, [256]] # 11
|
||||||
- [-1, 1, Conv, [256, 1, 1]] # 12
|
- [-1, 1, Conv, [256, 1, 1]] # 12
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [2, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14
|
- [2, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14
|
||||||
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, RepC3, [256]] # X3 (16), fpn_blocks.1
|
- [-1, 3, RepC3, [256]] # X3 (16), fpn_blocks.1
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 17, downsample_convs.0
|
- [-1, 1, Conv, [256, 3, 2]] # 17, downsample_convs.0
|
||||||
- [[-1, 12], 1, Concat, [1]] # cat Y4
|
- [[-1, 12], 1, Concat, [1]] # cat Y4
|
||||||
- [-1, 3, RepC3, [256]] # F4 (19), pan_blocks.0
|
- [-1, 3, RepC3, [256]] # F4 (19), pan_blocks.0
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 20, downsample_convs.1
|
- [-1, 1, Conv, [256, 3, 2]] # 20, downsample_convs.1
|
||||||
- [[-1, 7], 1, Concat, [1]] # cat Y5
|
- [[-1, 7], 1, Concat, [1]] # cat Y5
|
||||||
- [-1, 3, RepC3, [256]] # F5 (22), pan_blocks.1
|
- [-1, 3, RepC3, [256]] # F5 (22), pan_blocks.1
|
||||||
|
|
||||||
- [[16, 19, 22], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
- [[16, 19, 22], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||||
|
|||||||
@ -2,53 +2,53 @@
|
|||||||
# RT-DETR-x object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/rtdetr
|
# RT-DETR-x object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/rtdetr
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
x: [1.00, 1.00, 2048]
|
x: [1.00, 1.00, 2048]
|
||||||
|
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, HGStem, [32, 64]] # 0-P2/4
|
- [-1, 1, HGStem, [32, 64]] # 0-P2/4
|
||||||
- [-1, 6, HGBlock, [64, 128, 3]] # stage 1
|
- [-1, 6, HGBlock, [64, 128, 3]] # stage 1
|
||||||
|
|
||||||
- [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
|
- [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
|
||||||
- [-1, 6, HGBlock, [128, 512, 3]]
|
- [-1, 6, HGBlock, [128, 512, 3]]
|
||||||
- [-1, 6, HGBlock, [128, 512, 3, False, True]] # 4-stage 2
|
- [-1, 6, HGBlock, [128, 512, 3, False, True]] # 4-stage 2
|
||||||
|
|
||||||
- [-1, 1, DWConv, [512, 3, 2, 1, False]] # 5-P3/16
|
- [-1, 1, DWConv, [512, 3, 2, 1, False]] # 5-P3/16
|
||||||
- [-1, 6, HGBlock, [256, 1024, 5, True, False]] # cm, c2, k, light, shortcut
|
- [-1, 6, HGBlock, [256, 1024, 5, True, False]] # cm, c2, k, light, shortcut
|
||||||
- [-1, 6, HGBlock, [256, 1024, 5, True, True]]
|
- [-1, 6, HGBlock, [256, 1024, 5, True, True]]
|
||||||
- [-1, 6, HGBlock, [256, 1024, 5, True, True]]
|
- [-1, 6, HGBlock, [256, 1024, 5, True, True]]
|
||||||
- [-1, 6, HGBlock, [256, 1024, 5, True, True]]
|
- [-1, 6, HGBlock, [256, 1024, 5, True, True]]
|
||||||
- [-1, 6, HGBlock, [256, 1024, 5, True, True]] # 10-stage 3
|
- [-1, 6, HGBlock, [256, 1024, 5, True, True]] # 10-stage 3
|
||||||
|
|
||||||
- [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 11-P4/32
|
- [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 11-P4/32
|
||||||
- [-1, 6, HGBlock, [512, 2048, 5, True, False]]
|
- [-1, 6, HGBlock, [512, 2048, 5, True, False]]
|
||||||
- [-1, 6, HGBlock, [512, 2048, 5, True, True]] # 13-stage 4
|
- [-1, 6, HGBlock, [512, 2048, 5, True, True]] # 13-stage 4
|
||||||
|
|
||||||
head:
|
head:
|
||||||
- [-1, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 14 input_proj.2
|
- [-1, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 14 input_proj.2
|
||||||
- [-1, 1, AIFI, [2048, 8]]
|
- [-1, 1, AIFI, [2048, 8]]
|
||||||
- [-1, 1, Conv, [384, 1, 1]] # 16, Y5, lateral_convs.0
|
- [-1, 1, Conv, [384, 1, 1]] # 16, Y5, lateral_convs.0
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [10, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 18 input_proj.1
|
- [10, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 18 input_proj.1
|
||||||
- [[-2, -1], 1, Concat, [1]]
|
- [[-2, -1], 1, Concat, [1]]
|
||||||
- [-1, 3, RepC3, [384]] # 20, fpn_blocks.0
|
- [-1, 3, RepC3, [384]] # 20, fpn_blocks.0
|
||||||
- [-1, 1, Conv, [384, 1, 1]] # 21, Y4, lateral_convs.1
|
- [-1, 1, Conv, [384, 1, 1]] # 21, Y4, lateral_convs.1
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [4, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 23 input_proj.0
|
- [4, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 23 input_proj.0
|
||||||
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, RepC3, [384]] # X3 (25), fpn_blocks.1
|
- [-1, 3, RepC3, [384]] # X3 (25), fpn_blocks.1
|
||||||
|
|
||||||
- [-1, 1, Conv, [384, 3, 2]] # 26, downsample_convs.0
|
- [-1, 1, Conv, [384, 3, 2]] # 26, downsample_convs.0
|
||||||
- [[-1, 21], 1, Concat, [1]] # cat Y4
|
- [[-1, 21], 1, Concat, [1]] # cat Y4
|
||||||
- [-1, 3, RepC3, [384]] # F4 (28), pan_blocks.0
|
- [-1, 3, RepC3, [384]] # F4 (28), pan_blocks.0
|
||||||
|
|
||||||
- [-1, 1, Conv, [384, 3, 2]] # 29, downsample_convs.1
|
- [-1, 1, Conv, [384, 3, 2]] # 29, downsample_convs.1
|
||||||
- [[-1, 16], 1, Concat, [1]] # cat Y5
|
- [[-1, 16], 1, Concat, [1]] # cat Y5
|
||||||
- [-1, 3, RepC3, [384]] # F5 (31), pan_blocks.1
|
- [-1, 3, RepC3, [384]] # F5 (31), pan_blocks.1
|
||||||
|
|
||||||
- [[25, 28, 31], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
- [[25, 28, 31], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||||
|
|||||||
@ -2,24 +2,24 @@
|
|||||||
# YOLOv3-SPP object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3
|
# YOLOv3-SPP object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
depth_multiple: 1.0 # model depth multiple
|
depth_multiple: 1.0 # model depth multiple
|
||||||
width_multiple: 1.0 # layer channel multiple
|
width_multiple: 1.0 # layer channel multiple
|
||||||
|
|
||||||
# darknet53 backbone
|
# darknet53 backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, number, module, args]
|
# [from, number, module, args]
|
||||||
- [-1, 1, Conv, [32, 3, 1]] # 0
|
- [-1, 1, Conv, [32, 3, 1]] # 0
|
||||||
- [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
|
- [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
|
||||||
- [-1, 1, Bottleneck, [64]]
|
- [-1, 1, Bottleneck, [64]]
|
||||||
- [-1, 1, Conv, [128, 3, 2]] # 3-P2/4
|
- [-1, 1, Conv, [128, 3, 2]] # 3-P2/4
|
||||||
- [-1, 2, Bottleneck, [128]]
|
- [-1, 2, Bottleneck, [128]]
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 5-P3/8
|
- [-1, 1, Conv, [256, 3, 2]] # 5-P3/8
|
||||||
- [-1, 8, Bottleneck, [256]]
|
- [-1, 8, Bottleneck, [256]]
|
||||||
- [-1, 1, Conv, [512, 3, 2]] # 7-P4/16
|
- [-1, 1, Conv, [512, 3, 2]] # 7-P4/16
|
||||||
- [-1, 8, Bottleneck, [512]]
|
- [-1, 8, Bottleneck, [512]]
|
||||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P5/32
|
- [-1, 1, Conv, [1024, 3, 2]] # 9-P5/32
|
||||||
- [-1, 4, Bottleneck, [1024]] # 10
|
- [-1, 4, Bottleneck, [1024]] # 10
|
||||||
|
|
||||||
# YOLOv3-SPP head
|
# YOLOv3-SPP head
|
||||||
head:
|
head:
|
||||||
@ -27,20 +27,20 @@ head:
|
|||||||
- [-1, 1, SPP, [512, [5, 9, 13]]]
|
- [-1, 1, SPP, [512, [5, 9, 13]]]
|
||||||
- [-1, 1, Conv, [1024, 3, 1]]
|
- [-1, 1, Conv, [1024, 3, 1]]
|
||||||
- [-1, 1, Conv, [512, 1, 1]]
|
- [-1, 1, Conv, [512, 1, 1]]
|
||||||
- [-1, 1, Conv, [1024, 3, 1]] # 15 (P5/32-large)
|
- [-1, 1, Conv, [1024, 3, 1]] # 15 (P5/32-large)
|
||||||
|
|
||||||
- [-2, 1, Conv, [256, 1, 1]]
|
- [-2, 1, Conv, [256, 1, 1]]
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 8], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 1, Bottleneck, [512, False]]
|
- [-1, 1, Bottleneck, [512, False]]
|
||||||
- [-1, 1, Bottleneck, [512, False]]
|
- [-1, 1, Bottleneck, [512, False]]
|
||||||
- [-1, 1, Conv, [256, 1, 1]]
|
- [-1, 1, Conv, [256, 1, 1]]
|
||||||
- [-1, 1, Conv, [512, 3, 1]] # 22 (P4/16-medium)
|
- [-1, 1, Conv, [512, 3, 1]] # 22 (P4/16-medium)
|
||||||
|
|
||||||
- [-2, 1, Conv, [128, 1, 1]]
|
- [-2, 1, Conv, [128, 1, 1]]
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P3
|
- [[-1, 6], 1, Concat, [1]] # cat backbone P3
|
||||||
- [-1, 1, Bottleneck, [256, False]]
|
- [-1, 1, Bottleneck, [256, False]]
|
||||||
- [-1, 2, Bottleneck, [256, False]] # 27 (P3/8-small)
|
- [-1, 2, Bottleneck, [256, False]] # 27 (P3/8-small)
|
||||||
|
|
||||||
- [[27, 22, 15], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
- [[27, 22, 15], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||||
|
|||||||
@ -2,36 +2,36 @@
|
|||||||
# YOLOv3-tiny object detection model with P4-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3
|
# YOLOv3-tiny object detection model with P4-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
depth_multiple: 1.0 # model depth multiple
|
depth_multiple: 1.0 # model depth multiple
|
||||||
width_multiple: 1.0 # layer channel multiple
|
width_multiple: 1.0 # layer channel multiple
|
||||||
|
|
||||||
# YOLOv3-tiny backbone
|
# YOLOv3-tiny backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, number, module, args]
|
# [from, number, module, args]
|
||||||
- [-1, 1, Conv, [16, 3, 1]] # 0
|
- [-1, 1, Conv, [16, 3, 1]] # 0
|
||||||
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 1-P1/2
|
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 1-P1/2
|
||||||
- [-1, 1, Conv, [32, 3, 1]]
|
- [-1, 1, Conv, [32, 3, 1]]
|
||||||
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 3-P2/4
|
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 3-P2/4
|
||||||
- [-1, 1, Conv, [64, 3, 1]]
|
- [-1, 1, Conv, [64, 3, 1]]
|
||||||
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 5-P3/8
|
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 5-P3/8
|
||||||
- [-1, 1, Conv, [128, 3, 1]]
|
- [-1, 1, Conv, [128, 3, 1]]
|
||||||
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 7-P4/16
|
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 7-P4/16
|
||||||
- [-1, 1, Conv, [256, 3, 1]]
|
- [-1, 1, Conv, [256, 3, 1]]
|
||||||
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 9-P5/32
|
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 9-P5/32
|
||||||
- [-1, 1, Conv, [512, 3, 1]]
|
- [-1, 1, Conv, [512, 3, 1]]
|
||||||
- [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]] # 11
|
- [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]] # 11
|
||||||
- [-1, 1, nn.MaxPool2d, [2, 1, 0]] # 12
|
- [-1, 1, nn.MaxPool2d, [2, 1, 0]] # 12
|
||||||
|
|
||||||
# YOLOv3-tiny head
|
# YOLOv3-tiny head
|
||||||
head:
|
head:
|
||||||
- [-1, 1, Conv, [1024, 3, 1]]
|
- [-1, 1, Conv, [1024, 3, 1]]
|
||||||
- [-1, 1, Conv, [256, 1, 1]]
|
- [-1, 1, Conv, [256, 1, 1]]
|
||||||
- [-1, 1, Conv, [512, 3, 1]] # 15 (P5/32-large)
|
- [-1, 1, Conv, [512, 3, 1]] # 15 (P5/32-large)
|
||||||
|
|
||||||
- [-2, 1, Conv, [128, 1, 1]]
|
- [-2, 1, Conv, [128, 1, 1]]
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 8], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 1, Conv, [256, 3, 1]] # 19 (P4/16-medium)
|
- [-1, 1, Conv, [256, 3, 1]] # 19 (P4/16-medium)
|
||||||
|
|
||||||
- [[19, 15], 1, Detect, [nc]] # Detect(P4, P5)
|
- [[19, 15], 1, Detect, [nc]] # Detect(P4, P5)
|
||||||
|
|||||||
@ -2,24 +2,24 @@
|
|||||||
# YOLOv3 object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3
|
# YOLOv3 object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
depth_multiple: 1.0 # model depth multiple
|
depth_multiple: 1.0 # model depth multiple
|
||||||
width_multiple: 1.0 # layer channel multiple
|
width_multiple: 1.0 # layer channel multiple
|
||||||
|
|
||||||
# darknet53 backbone
|
# darknet53 backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, number, module, args]
|
# [from, number, module, args]
|
||||||
- [-1, 1, Conv, [32, 3, 1]] # 0
|
- [-1, 1, Conv, [32, 3, 1]] # 0
|
||||||
- [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
|
- [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
|
||||||
- [-1, 1, Bottleneck, [64]]
|
- [-1, 1, Bottleneck, [64]]
|
||||||
- [-1, 1, Conv, [128, 3, 2]] # 3-P2/4
|
- [-1, 1, Conv, [128, 3, 2]] # 3-P2/4
|
||||||
- [-1, 2, Bottleneck, [128]]
|
- [-1, 2, Bottleneck, [128]]
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 5-P3/8
|
- [-1, 1, Conv, [256, 3, 2]] # 5-P3/8
|
||||||
- [-1, 8, Bottleneck, [256]]
|
- [-1, 8, Bottleneck, [256]]
|
||||||
- [-1, 1, Conv, [512, 3, 2]] # 7-P4/16
|
- [-1, 1, Conv, [512, 3, 2]] # 7-P4/16
|
||||||
- [-1, 8, Bottleneck, [512]]
|
- [-1, 8, Bottleneck, [512]]
|
||||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P5/32
|
- [-1, 1, Conv, [1024, 3, 2]] # 9-P5/32
|
||||||
- [-1, 4, Bottleneck, [1024]] # 10
|
- [-1, 4, Bottleneck, [1024]] # 10
|
||||||
|
|
||||||
# YOLOv3 head
|
# YOLOv3 head
|
||||||
head:
|
head:
|
||||||
@ -27,20 +27,20 @@ head:
|
|||||||
- [-1, 1, Conv, [512, 1, 1]]
|
- [-1, 1, Conv, [512, 1, 1]]
|
||||||
- [-1, 1, Conv, [1024, 3, 1]]
|
- [-1, 1, Conv, [1024, 3, 1]]
|
||||||
- [-1, 1, Conv, [512, 1, 1]]
|
- [-1, 1, Conv, [512, 1, 1]]
|
||||||
- [-1, 1, Conv, [1024, 3, 1]] # 15 (P5/32-large)
|
- [-1, 1, Conv, [1024, 3, 1]] # 15 (P5/32-large)
|
||||||
|
|
||||||
- [-2, 1, Conv, [256, 1, 1]]
|
- [-2, 1, Conv, [256, 1, 1]]
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 8], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 1, Bottleneck, [512, False]]
|
- [-1, 1, Bottleneck, [512, False]]
|
||||||
- [-1, 1, Bottleneck, [512, False]]
|
- [-1, 1, Bottleneck, [512, False]]
|
||||||
- [-1, 1, Conv, [256, 1, 1]]
|
- [-1, 1, Conv, [256, 1, 1]]
|
||||||
- [-1, 1, Conv, [512, 3, 1]] # 22 (P4/16-medium)
|
- [-1, 1, Conv, [512, 3, 1]] # 22 (P4/16-medium)
|
||||||
|
|
||||||
- [-2, 1, Conv, [128, 1, 1]]
|
- [-2, 1, Conv, [128, 1, 1]]
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P3
|
- [[-1, 6], 1, Concat, [1]] # cat backbone P3
|
||||||
- [-1, 1, Bottleneck, [256, False]]
|
- [-1, 1, Bottleneck, [256, False]]
|
||||||
- [-1, 2, Bottleneck, [256, False]] # 27 (P3/8-small)
|
- [-1, 2, Bottleneck, [256, False]] # 27 (P3/8-small)
|
||||||
|
|
||||||
- [[27, 22, 15], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
- [[27, 22, 15], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||||
|
|||||||
@ -2,7 +2,7 @@
|
|||||||
# YOLOv5 object detection model with P3-P6 outputs. For details see https://docs.ultralytics.com/models/yolov5
|
# YOLOv5 object detection model with P3-P6 outputs. For details see https://docs.ultralytics.com/models/yolov5
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov5n-p6.yaml' will call yolov5-p6.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov5n-p6.yaml' will call yolov5-p6.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
n: [0.33, 0.25, 1024]
|
n: [0.33, 0.25, 1024]
|
||||||
@ -14,46 +14,46 @@ scales: # model compound scaling constants, i.e. 'model=yolov5n-p6.yaml' will ca
|
|||||||
# YOLOv5 v6.0 backbone
|
# YOLOv5 v6.0 backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, number, module, args]
|
# [from, number, module, args]
|
||||||
- [-1, 1, Conv, [64, 6, 2, 2]] # 0-P1/2
|
- [-1, 1, Conv, [64, 6, 2, 2]] # 0-P1/2
|
||||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||||
- [-1, 3, C3, [128]]
|
- [-1, 3, C3, [128]]
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||||
- [-1, 6, C3, [256]]
|
- [-1, 6, C3, [256]]
|
||||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||||
- [-1, 9, C3, [512]]
|
- [-1, 9, C3, [512]]
|
||||||
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
||||||
- [-1, 3, C3, [768]]
|
- [-1, 3, C3, [768]]
|
||||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
||||||
- [-1, 3, C3, [1024]]
|
- [-1, 3, C3, [1024]]
|
||||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||||
|
|
||||||
# YOLOv5 v6.0 head
|
# YOLOv5 v6.0 head
|
||||||
head:
|
head:
|
||||||
- [-1, 1, Conv, [768, 1, 1]]
|
- [-1, 1, Conv, [768, 1, 1]]
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||||
- [-1, 3, C3, [768, False]] # 15
|
- [-1, 3, C3, [768, False]] # 15
|
||||||
|
|
||||||
- [-1, 1, Conv, [512, 1, 1]]
|
- [-1, 1, Conv, [512, 1, 1]]
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, C3, [512, False]] # 19
|
- [-1, 3, C3, [512, False]] # 19
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 1, 1]]
|
- [-1, 1, Conv, [256, 1, 1]]
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||||
- [-1, 3, C3, [256, False]] # 23 (P3/8-small)
|
- [-1, 3, C3, [256, False]] # 23 (P3/8-small)
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]]
|
- [-1, 1, Conv, [256, 3, 2]]
|
||||||
- [[-1, 20], 1, Concat, [1]] # cat head P4
|
- [[-1, 20], 1, Concat, [1]] # cat head P4
|
||||||
- [-1, 3, C3, [512, False]] # 26 (P4/16-medium)
|
- [-1, 3, C3, [512, False]] # 26 (P4/16-medium)
|
||||||
|
|
||||||
- [-1, 1, Conv, [512, 3, 2]]
|
- [-1, 1, Conv, [512, 3, 2]]
|
||||||
- [[-1, 16], 1, Concat, [1]] # cat head P5
|
- [[-1, 16], 1, Concat, [1]] # cat head P5
|
||||||
- [-1, 3, C3, [768, False]] # 29 (P5/32-large)
|
- [-1, 3, C3, [768, False]] # 29 (P5/32-large)
|
||||||
|
|
||||||
- [-1, 1, Conv, [768, 3, 2]]
|
- [-1, 1, Conv, [768, 3, 2]]
|
||||||
- [[-1, 12], 1, Concat, [1]] # cat head P6
|
- [[-1, 12], 1, Concat, [1]] # cat head P6
|
||||||
- [-1, 3, C3, [1024, False]] # 32 (P6/64-xlarge)
|
- [-1, 3, C3, [1024, False]] # 32 (P6/64-xlarge)
|
||||||
|
|
||||||
- [[23, 26, 29, 32], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)
|
- [[23, 26, 29, 32], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)
|
||||||
|
|||||||
@ -2,7 +2,7 @@
|
|||||||
# YOLOv5 object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/yolov5
|
# YOLOv5 object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/yolov5
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov5n.yaml' will call yolov5.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov5n.yaml' will call yolov5.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
n: [0.33, 0.25, 1024]
|
n: [0.33, 0.25, 1024]
|
||||||
@ -14,36 +14,35 @@ scales: # model compound scaling constants, i.e. 'model=yolov5n.yaml' will call
|
|||||||
# YOLOv5 v6.0 backbone
|
# YOLOv5 v6.0 backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, number, module, args]
|
# [from, number, module, args]
|
||||||
- [-1, 1, Conv, [64, 6, 2, 2]] # 0-P1/2
|
- [-1, 1, Conv, [64, 6, 2, 2]] # 0-P1/2
|
||||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||||
- [-1, 3, C3, [128]]
|
- [-1, 3, C3, [128]]
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||||
- [-1, 6, C3, [256]]
|
- [-1, 6, C3, [256]]
|
||||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||||
- [-1, 9, C3, [512]]
|
- [-1, 9, C3, [512]]
|
||||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||||
- [-1, 3, C3, [1024]]
|
- [-1, 3, C3, [1024]]
|
||||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||||
|
|
||||||
|
|
||||||
# YOLOv5 v6.0 head
|
# YOLOv5 v6.0 head
|
||||||
head:
|
head:
|
||||||
- [-1, 1, Conv, [512, 1, 1]]
|
- [-1, 1, Conv, [512, 1, 1]]
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, C3, [512, False]] # 13
|
- [-1, 3, C3, [512, False]] # 13
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 1, 1]]
|
- [-1, 1, Conv, [256, 1, 1]]
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||||
- [-1, 3, C3, [256, False]] # 17 (P3/8-small)
|
- [-1, 3, C3, [256, False]] # 17 (P3/8-small)
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]]
|
- [-1, 1, Conv, [256, 3, 2]]
|
||||||
- [[-1, 14], 1, Concat, [1]] # cat head P4
|
- [[-1, 14], 1, Concat, [1]] # cat head P4
|
||||||
- [-1, 3, C3, [512, False]] # 20 (P4/16-medium)
|
- [-1, 3, C3, [512, False]] # 20 (P4/16-medium)
|
||||||
|
|
||||||
- [-1, 1, Conv, [512, 3, 2]]
|
- [-1, 1, Conv, [512, 3, 2]]
|
||||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||||
- [-1, 3, C3, [1024, False]] # 23 (P5/32-large)
|
- [-1, 3, C3, [1024, False]] # 23 (P5/32-large)
|
||||||
|
|
||||||
- [[17, 20, 23], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
- [[17, 20, 23], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||||
|
|||||||
@ -2,8 +2,8 @@
|
|||||||
# YOLOv6 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/models/yolov6
|
# YOLOv6 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/models/yolov6
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
activation: nn.ReLU() # (optional) model default activation function
|
activation: nn.ReLU() # (optional) model default activation function
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov6n.yaml' will call yolov8.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov6n.yaml' will call yolov8.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
n: [0.33, 0.25, 1024]
|
n: [0.33, 0.25, 1024]
|
||||||
@ -15,39 +15,39 @@ scales: # model compound scaling constants, i.e. 'model=yolov6n.yaml' will call
|
|||||||
# YOLOv6-3.0s backbone
|
# YOLOv6-3.0s backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||||
- [-1, 6, Conv, [128, 3, 1]]
|
- [-1, 6, Conv, [128, 3, 1]]
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||||
- [-1, 12, Conv, [256, 3, 1]]
|
- [-1, 12, Conv, [256, 3, 1]]
|
||||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||||
- [-1, 18, Conv, [512, 3, 1]]
|
- [-1, 18, Conv, [512, 3, 1]]
|
||||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||||
- [-1, 6, Conv, [1024, 3, 1]]
|
- [-1, 6, Conv, [1024, 3, 1]]
|
||||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||||
|
|
||||||
# YOLOv6-3.0s head
|
# YOLOv6-3.0s head
|
||||||
head:
|
head:
|
||||||
- [-1, 1, Conv, [256, 1, 1]]
|
- [-1, 1, Conv, [256, 1, 1]]
|
||||||
- [-1, 1, nn.ConvTranspose2d, [256, 2, 2, 0]]
|
- [-1, 1, nn.ConvTranspose2d, [256, 2, 2, 0]]
|
||||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 1, Conv, [256, 3, 1]]
|
- [-1, 1, Conv, [256, 3, 1]]
|
||||||
- [-1, 9, Conv, [256, 3, 1]] # 14
|
- [-1, 9, Conv, [256, 3, 1]] # 14
|
||||||
|
|
||||||
- [-1, 1, Conv, [128, 1, 1]]
|
- [-1, 1, Conv, [128, 1, 1]]
|
||||||
- [-1, 1, nn.ConvTranspose2d, [128, 2, 2, 0]]
|
- [-1, 1, nn.ConvTranspose2d, [128, 2, 2, 0]]
|
||||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||||
- [-1, 1, Conv, [128, 3, 1]]
|
- [-1, 1, Conv, [128, 3, 1]]
|
||||||
- [-1, 9, Conv, [128, 3, 1]] # 19
|
- [-1, 9, Conv, [128, 3, 1]] # 19
|
||||||
|
|
||||||
- [-1, 1, Conv, [128, 3, 2]]
|
- [-1, 1, Conv, [128, 3, 2]]
|
||||||
- [[-1, 15], 1, Concat, [1]] # cat head P4
|
- [[-1, 15], 1, Concat, [1]] # cat head P4
|
||||||
- [-1, 1, Conv, [256, 3, 1]]
|
- [-1, 1, Conv, [256, 3, 1]]
|
||||||
- [-1, 9, Conv, [256, 3, 1]] # 23
|
- [-1, 9, Conv, [256, 3, 1]] # 23
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]]
|
- [-1, 1, Conv, [256, 3, 2]]
|
||||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||||
- [-1, 1, Conv, [512, 3, 1]]
|
- [-1, 1, Conv, [512, 3, 1]]
|
||||||
- [-1, 9, Conv, [512, 3, 1]] # 27
|
- [-1, 9, Conv, [512, 3, 1]] # 27
|
||||||
|
|
||||||
- [[19, 23, 27], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
- [[19, 23, 27], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||||
|
|||||||
@ -2,7 +2,7 @@
|
|||||||
# YOLOv8-cls image classification model. For Usage examples see https://docs.ultralytics.com/tasks/classify
|
# YOLOv8-cls image classification model. For Usage examples see https://docs.ultralytics.com/tasks/classify
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 1000 # number of classes
|
nc: 1000 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
n: [0.33, 0.25, 1024]
|
n: [0.33, 0.25, 1024]
|
||||||
@ -14,16 +14,16 @@ scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will c
|
|||||||
# YOLOv8.0n backbone
|
# YOLOv8.0n backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||||
- [-1, 3, C2f, [128, True]]
|
- [-1, 3, C2f, [128, True]]
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||||
- [-1, 6, C2f, [256, True]]
|
- [-1, 6, C2f, [256, True]]
|
||||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||||
- [-1, 6, C2f, [512, True]]
|
- [-1, 6, C2f, [512, True]]
|
||||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||||
- [-1, 3, C2f, [1024, True]]
|
- [-1, 3, C2f, [1024, True]]
|
||||||
|
|
||||||
# YOLOv8.0n head
|
# YOLOv8.0n head
|
||||||
head:
|
head:
|
||||||
- [-1, 1, Classify, [nc]] # Classify
|
- [-1, 1, Classify, [nc]] # Classify
|
||||||
|
|||||||
@ -2,53 +2,53 @@
|
|||||||
# YOLOv8 object detection model with P2-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
# YOLOv8 object detection model with P2-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
n: [0.33, 0.25, 1024] # YOLOv8n-ghost-p2 summary: 491 layers, 2033944 parameters, 2033928 gradients, 13.8 GFLOPs
|
n: [0.33, 0.25, 1024] # YOLOv8n-ghost-p2 summary: 491 layers, 2033944 parameters, 2033928 gradients, 13.8 GFLOPs
|
||||||
s: [0.33, 0.50, 1024] # YOLOv8s-ghost-p2 summary: 491 layers, 5562080 parameters, 5562064 gradients, 25.1 GFLOPs
|
s: [0.33, 0.50, 1024] # YOLOv8s-ghost-p2 summary: 491 layers, 5562080 parameters, 5562064 gradients, 25.1 GFLOPs
|
||||||
m: [0.67, 0.75, 768] # YOLOv8m-ghost-p2 summary: 731 layers, 9031728 parameters, 9031712 gradients, 42.8 GFLOPs
|
m: [0.67, 0.75, 768] # YOLOv8m-ghost-p2 summary: 731 layers, 9031728 parameters, 9031712 gradients, 42.8 GFLOPs
|
||||||
l: [1.00, 1.00, 512] # YOLOv8l-ghost-p2 summary: 971 layers, 12214448 parameters, 12214432 gradients, 69.1 GFLOPs
|
l: [1.00, 1.00, 512] # YOLOv8l-ghost-p2 summary: 971 layers, 12214448 parameters, 12214432 gradients, 69.1 GFLOPs
|
||||||
x: [1.00, 1.25, 512] # YOLOv8x-ghost-p2 summary: 971 layers, 18664776 parameters, 18664760 gradients, 103.3 GFLOPs
|
x: [1.00, 1.25, 512] # YOLOv8x-ghost-p2 summary: 971 layers, 18664776 parameters, 18664760 gradients, 103.3 GFLOPs
|
||||||
|
|
||||||
# YOLOv8.0-ghost backbone
|
# YOLOv8.0-ghost backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||||
- [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
|
- [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
|
||||||
- [-1, 3, C3Ghost, [128, True]]
|
- [-1, 3, C3Ghost, [128, True]]
|
||||||
- [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
|
- [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
|
||||||
- [-1, 6, C3Ghost, [256, True]]
|
- [-1, 6, C3Ghost, [256, True]]
|
||||||
- [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
|
- [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
|
||||||
- [-1, 6, C3Ghost, [512, True]]
|
- [-1, 6, C3Ghost, [512, True]]
|
||||||
- [-1, 1, GhostConv, [1024, 3, 2]] # 7-P5/32
|
- [-1, 1, GhostConv, [1024, 3, 2]] # 7-P5/32
|
||||||
- [-1, 3, C3Ghost, [1024, True]]
|
- [-1, 3, C3Ghost, [1024, True]]
|
||||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||||
|
|
||||||
# YOLOv8.0-ghost-p2 head
|
# YOLOv8.0-ghost-p2 head
|
||||||
head:
|
head:
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, C3Ghost, [512]] # 12
|
- [-1, 3, C3Ghost, [512]] # 12
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||||
- [-1, 3, C3Ghost, [256]] # 15 (P3/8-small)
|
- [-1, 3, C3Ghost, [256]] # 15 (P3/8-small)
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 2], 1, Concat, [1]] # cat backbone P2
|
- [[-1, 2], 1, Concat, [1]] # cat backbone P2
|
||||||
- [-1, 3, C3Ghost, [128]] # 18 (P2/4-xsmall)
|
- [-1, 3, C3Ghost, [128]] # 18 (P2/4-xsmall)
|
||||||
|
|
||||||
- [-1, 1, GhostConv, [128, 3, 2]]
|
- [-1, 1, GhostConv, [128, 3, 2]]
|
||||||
- [[-1, 15], 1, Concat, [1]] # cat head P3
|
- [[-1, 15], 1, Concat, [1]] # cat head P3
|
||||||
- [-1, 3, C3Ghost, [256]] # 21 (P3/8-small)
|
- [-1, 3, C3Ghost, [256]] # 21 (P3/8-small)
|
||||||
|
|
||||||
- [-1, 1, GhostConv, [256, 3, 2]]
|
- [-1, 1, GhostConv, [256, 3, 2]]
|
||||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||||
- [-1, 3, C3Ghost, [512]] # 24 (P4/16-medium)
|
- [-1, 3, C3Ghost, [512]] # 24 (P4/16-medium)
|
||||||
|
|
||||||
- [-1, 1, GhostConv, [512, 3, 2]]
|
- [-1, 1, GhostConv, [512, 3, 2]]
|
||||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||||
- [-1, 3, C3Ghost, [1024]] # 27 (P5/32-large)
|
- [-1, 3, C3Ghost, [1024]] # 27 (P5/32-large)
|
||||||
|
|
||||||
- [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)
|
- [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)
|
||||||
|
|||||||
@ -2,55 +2,55 @@
|
|||||||
# YOLOv8 object detection model with P3-P6 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
# YOLOv8 object detection model with P3-P6 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
n: [0.33, 0.25, 1024] # YOLOv8n-ghost-p6 summary: 529 layers, 2901100 parameters, 2901084 gradients, 5.8 GFLOPs
|
n: [0.33, 0.25, 1024] # YOLOv8n-ghost-p6 summary: 529 layers, 2901100 parameters, 2901084 gradients, 5.8 GFLOPs
|
||||||
s: [0.33, 0.50, 1024] # YOLOv8s-ghost-p6 summary: 529 layers, 9520008 parameters, 9519992 gradients, 16.4 GFLOPs
|
s: [0.33, 0.50, 1024] # YOLOv8s-ghost-p6 summary: 529 layers, 9520008 parameters, 9519992 gradients, 16.4 GFLOPs
|
||||||
m: [0.67, 0.75, 768] # YOLOv8m-ghost-p6 summary: 789 layers, 18002904 parameters, 18002888 gradients, 34.4 GFLOPs
|
m: [0.67, 0.75, 768] # YOLOv8m-ghost-p6 summary: 789 layers, 18002904 parameters, 18002888 gradients, 34.4 GFLOPs
|
||||||
l: [1.00, 1.00, 512] # YOLOv8l-ghost-p6 summary: 1049 layers, 21227584 parameters, 21227568 gradients, 55.3 GFLOPs
|
l: [1.00, 1.00, 512] # YOLOv8l-ghost-p6 summary: 1049 layers, 21227584 parameters, 21227568 gradients, 55.3 GFLOPs
|
||||||
x: [1.00, 1.25, 512] # YOLOv8x-ghost-p6 summary: 1049 layers, 33057852 parameters, 33057836 gradients, 85.7 GFLOPs
|
x: [1.00, 1.25, 512] # YOLOv8x-ghost-p6 summary: 1049 layers, 33057852 parameters, 33057836 gradients, 85.7 GFLOPs
|
||||||
|
|
||||||
# YOLOv8.0-ghost backbone
|
# YOLOv8.0-ghost backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||||
- [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
|
- [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
|
||||||
- [-1, 3, C3Ghost, [128, True]]
|
- [-1, 3, C3Ghost, [128, True]]
|
||||||
- [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
|
- [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
|
||||||
- [-1, 6, C3Ghost, [256, True]]
|
- [-1, 6, C3Ghost, [256, True]]
|
||||||
- [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
|
- [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
|
||||||
- [-1, 6, C3Ghost, [512, True]]
|
- [-1, 6, C3Ghost, [512, True]]
|
||||||
- [-1, 1, GhostConv, [768, 3, 2]] # 7-P5/32
|
- [-1, 1, GhostConv, [768, 3, 2]] # 7-P5/32
|
||||||
- [-1, 3, C3Ghost, [768, True]]
|
- [-1, 3, C3Ghost, [768, True]]
|
||||||
- [-1, 1, GhostConv, [1024, 3, 2]] # 9-P6/64
|
- [-1, 1, GhostConv, [1024, 3, 2]] # 9-P6/64
|
||||||
- [-1, 3, C3Ghost, [1024, True]]
|
- [-1, 3, C3Ghost, [1024, True]]
|
||||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||||
|
|
||||||
# YOLOv8.0-ghost-p6 head
|
# YOLOv8.0-ghost-p6 head
|
||||||
head:
|
head:
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||||
- [-1, 3, C3Ghost, [768]] # 14
|
- [-1, 3, C3Ghost, [768]] # 14
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, C3Ghost, [512]] # 17
|
- [-1, 3, C3Ghost, [512]] # 17
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||||
- [-1, 3, C3Ghost, [256]] # 20 (P3/8-small)
|
- [-1, 3, C3Ghost, [256]] # 20 (P3/8-small)
|
||||||
|
|
||||||
- [-1, 1, GhostConv, [256, 3, 2]]
|
- [-1, 1, GhostConv, [256, 3, 2]]
|
||||||
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
||||||
- [-1, 3, C3Ghost, [512]] # 23 (P4/16-medium)
|
- [-1, 3, C3Ghost, [512]] # 23 (P4/16-medium)
|
||||||
|
|
||||||
- [-1, 1, GhostConv, [512, 3, 2]]
|
- [-1, 1, GhostConv, [512, 3, 2]]
|
||||||
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
||||||
- [-1, 3, C3Ghost, [768]] # 26 (P5/32-large)
|
- [-1, 3, C3Ghost, [768]] # 26 (P5/32-large)
|
||||||
|
|
||||||
- [-1, 1, GhostConv, [768, 3, 2]]
|
- [-1, 1, GhostConv, [768, 3, 2]]
|
||||||
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
||||||
- [-1, 3, C3Ghost, [1024]] # 29 (P6/64-xlarge)
|
- [-1, 3, C3Ghost, [1024]] # 29 (P6/64-xlarge)
|
||||||
|
|
||||||
- [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)
|
- [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)
|
||||||
|
|||||||
@ -3,45 +3,45 @@
|
|||||||
# Employs Ghost convolutions and modules proposed in Huawei's GhostNet in https://arxiv.org/abs/1911.11907v2
|
# Employs Ghost convolutions and modules proposed in Huawei's GhostNet in https://arxiv.org/abs/1911.11907v2
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
n: [0.33, 0.25, 1024] # YOLOv8n-ghost summary: 403 layers, 1865316 parameters, 1865300 gradients, 5.8 GFLOPs
|
n: [0.33, 0.25, 1024] # YOLOv8n-ghost summary: 403 layers, 1865316 parameters, 1865300 gradients, 5.8 GFLOPs
|
||||||
s: [0.33, 0.50, 1024] # YOLOv8s-ghost summary: 403 layers, 5960072 parameters, 5960056 gradients, 16.4 GFLOPs
|
s: [0.33, 0.50, 1024] # YOLOv8s-ghost summary: 403 layers, 5960072 parameters, 5960056 gradients, 16.4 GFLOPs
|
||||||
m: [0.67, 0.75, 768] # YOLOv8m-ghost summary: 603 layers, 10336312 parameters, 10336296 gradients, 32.7 GFLOPs
|
m: [0.67, 0.75, 768] # YOLOv8m-ghost summary: 603 layers, 10336312 parameters, 10336296 gradients, 32.7 GFLOPs
|
||||||
l: [1.00, 1.00, 512] # YOLOv8l-ghost summary: 803 layers, 14277872 parameters, 14277856 gradients, 53.7 GFLOPs
|
l: [1.00, 1.00, 512] # YOLOv8l-ghost summary: 803 layers, 14277872 parameters, 14277856 gradients, 53.7 GFLOPs
|
||||||
x: [1.00, 1.25, 512] # YOLOv8x-ghost summary: 803 layers, 22229308 parameters, 22229292 gradients, 83.3 GFLOPs
|
x: [1.00, 1.25, 512] # YOLOv8x-ghost summary: 803 layers, 22229308 parameters, 22229292 gradients, 83.3 GFLOPs
|
||||||
|
|
||||||
# YOLOv8.0n-ghost backbone
|
# YOLOv8.0n-ghost backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||||
- [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
|
- [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
|
||||||
- [-1, 3, C3Ghost, [128, True]]
|
- [-1, 3, C3Ghost, [128, True]]
|
||||||
- [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
|
- [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
|
||||||
- [-1, 6, C3Ghost, [256, True]]
|
- [-1, 6, C3Ghost, [256, True]]
|
||||||
- [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
|
- [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
|
||||||
- [-1, 6, C3Ghost, [512, True]]
|
- [-1, 6, C3Ghost, [512, True]]
|
||||||
- [-1, 1, GhostConv, [1024, 3, 2]] # 7-P5/32
|
- [-1, 1, GhostConv, [1024, 3, 2]] # 7-P5/32
|
||||||
- [-1, 3, C3Ghost, [1024, True]]
|
- [-1, 3, C3Ghost, [1024, True]]
|
||||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||||
|
|
||||||
# YOLOv8.0n head
|
# YOLOv8.0n head
|
||||||
head:
|
head:
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, C3Ghost, [512]] # 12
|
- [-1, 3, C3Ghost, [512]] # 12
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||||
- [-1, 3, C3Ghost, [256]] # 15 (P3/8-small)
|
- [-1, 3, C3Ghost, [256]] # 15 (P3/8-small)
|
||||||
|
|
||||||
- [-1, 1, GhostConv, [256, 3, 2]]
|
- [-1, 1, GhostConv, [256, 3, 2]]
|
||||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||||
- [-1, 3, C3Ghost, [512]] # 18 (P4/16-medium)
|
- [-1, 3, C3Ghost, [512]] # 18 (P4/16-medium)
|
||||||
|
|
||||||
- [-1, 1, GhostConv, [512, 3, 2]]
|
- [-1, 1, GhostConv, [512, 3, 2]]
|
||||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||||
- [-1, 3, C3Ghost, [1024]] # 21 (P5/32-large)
|
- [-1, 3, C3Ghost, [1024]] # 21 (P5/32-large)
|
||||||
|
|
||||||
- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||||
|
|||||||
@ -2,45 +2,45 @@
|
|||||||
# YOLOv8 Oriented Bounding Boxes (OBB) model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
# YOLOv8 Oriented Bounding Boxes (OBB) model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
||||||
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
||||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||||
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
||||||
|
|
||||||
# YOLOv8.0n backbone
|
# YOLOv8.0n backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||||
- [-1, 3, C2f, [128, True]]
|
- [-1, 3, C2f, [128, True]]
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||||
- [-1, 6, C2f, [256, True]]
|
- [-1, 6, C2f, [256, True]]
|
||||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||||
- [-1, 6, C2f, [512, True]]
|
- [-1, 6, C2f, [512, True]]
|
||||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||||
- [-1, 3, C2f, [1024, True]]
|
- [-1, 3, C2f, [1024, True]]
|
||||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||||
|
|
||||||
# YOLOv8.0n head
|
# YOLOv8.0n head
|
||||||
head:
|
head:
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, C2f, [512]] # 12
|
- [-1, 3, C2f, [512]] # 12
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]]
|
- [-1, 1, Conv, [256, 3, 2]]
|
||||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||||
|
|
||||||
- [-1, 1, Conv, [512, 3, 2]]
|
- [-1, 1, Conv, [512, 3, 2]]
|
||||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||||
|
|
||||||
- [[15, 18, 21], 1, OBB, [nc, 1]] # OBB(P3, P4, P5)
|
- [[15, 18, 21], 1, OBB, [nc, 1]] # OBB(P3, P4, P5)
|
||||||
|
|||||||
@ -2,7 +2,7 @@
|
|||||||
# YOLOv8 object detection model with P2-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
# YOLOv8 object detection model with P2-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
n: [0.33, 0.25, 1024]
|
n: [0.33, 0.25, 1024]
|
||||||
@ -14,41 +14,41 @@ scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call
|
|||||||
# YOLOv8.0 backbone
|
# YOLOv8.0 backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||||
- [-1, 3, C2f, [128, True]]
|
- [-1, 3, C2f, [128, True]]
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||||
- [-1, 6, C2f, [256, True]]
|
- [-1, 6, C2f, [256, True]]
|
||||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||||
- [-1, 6, C2f, [512, True]]
|
- [-1, 6, C2f, [512, True]]
|
||||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||||
- [-1, 3, C2f, [1024, True]]
|
- [-1, 3, C2f, [1024, True]]
|
||||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||||
|
|
||||||
# YOLOv8.0-p2 head
|
# YOLOv8.0-p2 head
|
||||||
head:
|
head:
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, C2f, [512]] # 12
|
- [-1, 3, C2f, [512]] # 12
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 2], 1, Concat, [1]] # cat backbone P2
|
- [[-1, 2], 1, Concat, [1]] # cat backbone P2
|
||||||
- [-1, 3, C2f, [128]] # 18 (P2/4-xsmall)
|
- [-1, 3, C2f, [128]] # 18 (P2/4-xsmall)
|
||||||
|
|
||||||
- [-1, 1, Conv, [128, 3, 2]]
|
- [-1, 1, Conv, [128, 3, 2]]
|
||||||
- [[-1, 15], 1, Concat, [1]] # cat head P3
|
- [[-1, 15], 1, Concat, [1]] # cat head P3
|
||||||
- [-1, 3, C2f, [256]] # 21 (P3/8-small)
|
- [-1, 3, C2f, [256]] # 21 (P3/8-small)
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]]
|
- [-1, 1, Conv, [256, 3, 2]]
|
||||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||||
- [-1, 3, C2f, [512]] # 24 (P4/16-medium)
|
- [-1, 3, C2f, [512]] # 24 (P4/16-medium)
|
||||||
|
|
||||||
- [-1, 1, Conv, [512, 3, 2]]
|
- [-1, 1, Conv, [512, 3, 2]]
|
||||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||||
- [-1, 3, C2f, [1024]] # 27 (P5/32-large)
|
- [-1, 3, C2f, [1024]] # 27 (P5/32-large)
|
||||||
|
|
||||||
- [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)
|
- [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)
|
||||||
|
|||||||
@ -2,7 +2,7 @@
|
|||||||
# YOLOv8 object detection model with P3-P6 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
# YOLOv8 object detection model with P3-P6 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
n: [0.33, 0.25, 1024]
|
n: [0.33, 0.25, 1024]
|
||||||
@ -14,43 +14,43 @@ scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will ca
|
|||||||
# YOLOv8.0x6 backbone
|
# YOLOv8.0x6 backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||||
- [-1, 3, C2f, [128, True]]
|
- [-1, 3, C2f, [128, True]]
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||||
- [-1, 6, C2f, [256, True]]
|
- [-1, 6, C2f, [256, True]]
|
||||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||||
- [-1, 6, C2f, [512, True]]
|
- [-1, 6, C2f, [512, True]]
|
||||||
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
||||||
- [-1, 3, C2f, [768, True]]
|
- [-1, 3, C2f, [768, True]]
|
||||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
||||||
- [-1, 3, C2f, [1024, True]]
|
- [-1, 3, C2f, [1024, True]]
|
||||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||||
|
|
||||||
# YOLOv8.0x6 head
|
# YOLOv8.0x6 head
|
||||||
head:
|
head:
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||||
- [-1, 3, C2, [768, False]] # 14
|
- [-1, 3, C2, [768, False]] # 14
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, C2, [512, False]] # 17
|
- [-1, 3, C2, [512, False]] # 17
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||||
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
|
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]]
|
- [-1, 1, Conv, [256, 3, 2]]
|
||||||
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
||||||
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
|
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
|
||||||
|
|
||||||
- [-1, 1, Conv, [512, 3, 2]]
|
- [-1, 1, Conv, [512, 3, 2]]
|
||||||
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
||||||
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
|
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
|
||||||
|
|
||||||
- [-1, 1, Conv, [768, 3, 2]]
|
- [-1, 1, Conv, [768, 3, 2]]
|
||||||
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
||||||
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
|
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
|
||||||
|
|
||||||
- [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)
|
- [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)
|
||||||
|
|||||||
@ -2,8 +2,8 @@
|
|||||||
# YOLOv8-pose-p6 keypoints/pose estimation model. For Usage examples see https://docs.ultralytics.com/tasks/pose
|
# YOLOv8-pose-p6 keypoints/pose estimation model. For Usage examples see https://docs.ultralytics.com/tasks/pose
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 1 # number of classes
|
nc: 1 # number of classes
|
||||||
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
n: [0.33, 0.25, 1024]
|
n: [0.33, 0.25, 1024]
|
||||||
@ -15,43 +15,43 @@ scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will ca
|
|||||||
# YOLOv8.0x6 backbone
|
# YOLOv8.0x6 backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||||
- [-1, 3, C2f, [128, True]]
|
- [-1, 3, C2f, [128, True]]
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||||
- [-1, 6, C2f, [256, True]]
|
- [-1, 6, C2f, [256, True]]
|
||||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||||
- [-1, 6, C2f, [512, True]]
|
- [-1, 6, C2f, [512, True]]
|
||||||
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
||||||
- [-1, 3, C2f, [768, True]]
|
- [-1, 3, C2f, [768, True]]
|
||||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
||||||
- [-1, 3, C2f, [1024, True]]
|
- [-1, 3, C2f, [1024, True]]
|
||||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||||
|
|
||||||
# YOLOv8.0x6 head
|
# YOLOv8.0x6 head
|
||||||
head:
|
head:
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||||
- [-1, 3, C2, [768, False]] # 14
|
- [-1, 3, C2, [768, False]] # 14
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, C2, [512, False]] # 17
|
- [-1, 3, C2, [512, False]] # 17
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||||
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
|
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]]
|
- [-1, 1, Conv, [256, 3, 2]]
|
||||||
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
||||||
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
|
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
|
||||||
|
|
||||||
- [-1, 1, Conv, [512, 3, 2]]
|
- [-1, 1, Conv, [512, 3, 2]]
|
||||||
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
||||||
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
|
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
|
||||||
|
|
||||||
- [-1, 1, Conv, [768, 3, 2]]
|
- [-1, 1, Conv, [768, 3, 2]]
|
||||||
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
||||||
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
|
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
|
||||||
|
|
||||||
- [[20, 23, 26, 29], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5, P6)
|
- [[20, 23, 26, 29], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5, P6)
|
||||||
|
|||||||
@ -2,8 +2,8 @@
|
|||||||
# YOLOv8-pose keypoints/pose estimation model. For Usage examples see https://docs.ultralytics.com/tasks/pose
|
# YOLOv8-pose keypoints/pose estimation model. For Usage examples see https://docs.ultralytics.com/tasks/pose
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 1 # number of classes
|
nc: 1 # number of classes
|
||||||
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-pose.yaml' will call yolov8-pose.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov8n-pose.yaml' will call yolov8-pose.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
n: [0.33, 0.25, 1024]
|
n: [0.33, 0.25, 1024]
|
||||||
@ -15,33 +15,33 @@ scales: # model compound scaling constants, i.e. 'model=yolov8n-pose.yaml' will
|
|||||||
# YOLOv8.0n backbone
|
# YOLOv8.0n backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||||
- [-1, 3, C2f, [128, True]]
|
- [-1, 3, C2f, [128, True]]
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||||
- [-1, 6, C2f, [256, True]]
|
- [-1, 6, C2f, [256, True]]
|
||||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||||
- [-1, 6, C2f, [512, True]]
|
- [-1, 6, C2f, [512, True]]
|
||||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||||
- [-1, 3, C2f, [1024, True]]
|
- [-1, 3, C2f, [1024, True]]
|
||||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||||
|
|
||||||
# YOLOv8.0n head
|
# YOLOv8.0n head
|
||||||
head:
|
head:
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, C2f, [512]] # 12
|
- [-1, 3, C2f, [512]] # 12
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]]
|
- [-1, 1, Conv, [256, 3, 2]]
|
||||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||||
|
|
||||||
- [-1, 1, Conv, [512, 3, 2]]
|
- [-1, 1, Conv, [512, 3, 2]]
|
||||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||||
|
|
||||||
- [[15, 18, 21], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5)
|
- [[15, 18, 21], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5)
|
||||||
|
|||||||
@ -2,45 +2,45 @@
|
|||||||
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
||||||
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
||||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||||
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
||||||
|
|
||||||
# YOLOv8.0n backbone
|
# YOLOv8.0n backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||||
- [-1, 3, C2f, [128, True]]
|
- [-1, 3, C2f, [128, True]]
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||||
- [-1, 6, C2f, [256, True]]
|
- [-1, 6, C2f, [256, True]]
|
||||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||||
- [-1, 6, C2f, [512, True]]
|
- [-1, 6, C2f, [512, True]]
|
||||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||||
- [-1, 3, C2f, [1024, True]]
|
- [-1, 3, C2f, [1024, True]]
|
||||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||||
|
|
||||||
# YOLOv8.0n head
|
# YOLOv8.0n head
|
||||||
head:
|
head:
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, C2f, [512]] # 12
|
- [-1, 3, C2f, [512]] # 12
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]]
|
- [-1, 1, Conv, [256, 3, 2]]
|
||||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||||
|
|
||||||
- [-1, 1, Conv, [512, 3, 2]]
|
- [-1, 1, Conv, [512, 3, 2]]
|
||||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||||
|
|
||||||
- [[15, 18, 21], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
- [[15, 18, 21], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||||
|
|||||||
@ -2,7 +2,7 @@
|
|||||||
# YOLOv8-seg-p6 instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment
|
# YOLOv8-seg-p6 instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-seg-p6.yaml' will call yolov8-seg-p6.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov8n-seg-p6.yaml' will call yolov8-seg-p6.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
n: [0.33, 0.25, 1024]
|
n: [0.33, 0.25, 1024]
|
||||||
@ -14,43 +14,43 @@ scales: # model compound scaling constants, i.e. 'model=yolov8n-seg-p6.yaml' wil
|
|||||||
# YOLOv8.0x6 backbone
|
# YOLOv8.0x6 backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||||
- [-1, 3, C2f, [128, True]]
|
- [-1, 3, C2f, [128, True]]
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||||
- [-1, 6, C2f, [256, True]]
|
- [-1, 6, C2f, [256, True]]
|
||||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||||
- [-1, 6, C2f, [512, True]]
|
- [-1, 6, C2f, [512, True]]
|
||||||
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
||||||
- [-1, 3, C2f, [768, True]]
|
- [-1, 3, C2f, [768, True]]
|
||||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
||||||
- [-1, 3, C2f, [1024, True]]
|
- [-1, 3, C2f, [1024, True]]
|
||||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||||
|
|
||||||
# YOLOv8.0x6 head
|
# YOLOv8.0x6 head
|
||||||
head:
|
head:
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||||
- [-1, 3, C2, [768, False]] # 14
|
- [-1, 3, C2, [768, False]] # 14
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, C2, [512, False]] # 17
|
- [-1, 3, C2, [512, False]] # 17
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||||
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
|
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]]
|
- [-1, 1, Conv, [256, 3, 2]]
|
||||||
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
||||||
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
|
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
|
||||||
|
|
||||||
- [-1, 1, Conv, [512, 3, 2]]
|
- [-1, 1, Conv, [512, 3, 2]]
|
||||||
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
||||||
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
|
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
|
||||||
|
|
||||||
- [-1, 1, Conv, [768, 3, 2]]
|
- [-1, 1, Conv, [768, 3, 2]]
|
||||||
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
||||||
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
|
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
|
||||||
|
|
||||||
- [[20, 23, 26, 29], 1, Segment, [nc, 32, 256]] # Pose(P3, P4, P5, P6)
|
- [[20, 23, 26, 29], 1, Segment, [nc, 32, 256]] # Pose(P3, P4, P5, P6)
|
||||||
|
|||||||
@ -2,7 +2,7 @@
|
|||||||
# YOLOv8-seg instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment
|
# YOLOv8-seg instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-seg.yaml' will call yolov8-seg.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov8n-seg.yaml' will call yolov8-seg.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
n: [0.33, 0.25, 1024]
|
n: [0.33, 0.25, 1024]
|
||||||
@ -14,33 +14,33 @@ scales: # model compound scaling constants, i.e. 'model=yolov8n-seg.yaml' will c
|
|||||||
# YOLOv8.0n backbone
|
# YOLOv8.0n backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||||
- [-1, 3, C2f, [128, True]]
|
- [-1, 3, C2f, [128, True]]
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||||
- [-1, 6, C2f, [256, True]]
|
- [-1, 6, C2f, [256, True]]
|
||||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||||
- [-1, 6, C2f, [512, True]]
|
- [-1, 6, C2f, [512, True]]
|
||||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||||
- [-1, 3, C2f, [1024, True]]
|
- [-1, 3, C2f, [1024, True]]
|
||||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||||
|
|
||||||
# YOLOv8.0n head
|
# YOLOv8.0n head
|
||||||
head:
|
head:
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, C2f, [512]] # 12
|
- [-1, 3, C2f, [512]] # 12
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]]
|
- [-1, 1, Conv, [256, 3, 2]]
|
||||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||||
|
|
||||||
- [-1, 1, Conv, [512, 3, 2]]
|
- [-1, 1, Conv, [512, 3, 2]]
|
||||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||||
|
|
||||||
- [[15, 18, 21], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)
|
- [[15, 18, 21], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)
|
||||||
|
|||||||
@ -2,45 +2,45 @@
|
|||||||
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 80 # number of classes
|
nc: 80 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
||||||
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
||||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||||
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
||||||
|
|
||||||
# YOLOv8.0n backbone
|
# YOLOv8.0n backbone
|
||||||
backbone:
|
backbone:
|
||||||
# [from, repeats, module, args]
|
# [from, repeats, module, args]
|
||||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||||
- [-1, 3, C2f, [128, True]]
|
- [-1, 3, C2f, [128, True]]
|
||||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||||
- [-1, 6, C2f, [256, True]]
|
- [-1, 6, C2f, [256, True]]
|
||||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||||
- [-1, 6, C2f, [512, True]]
|
- [-1, 6, C2f, [512, True]]
|
||||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||||
- [-1, 3, C2f, [1024, True]]
|
- [-1, 3, C2f, [1024, True]]
|
||||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||||
|
|
||||||
# YOLOv8.0n head
|
# YOLOv8.0n head
|
||||||
head:
|
head:
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||||
- [-1, 3, C2f, [512]] # 12
|
- [-1, 3, C2f, [512]] # 12
|
||||||
|
|
||||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||||
|
|
||||||
- [-1, 1, Conv, [256, 3, 2]]
|
- [-1, 1, Conv, [256, 3, 2]]
|
||||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||||
|
|
||||||
- [-1, 1, Conv, [512, 3, 2]]
|
- [-1, 1, Conv, [512, 3, 2]]
|
||||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||||
|
|
||||||
- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||||
|
|||||||
@ -1,17 +1,17 @@
|
|||||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||||
# Default YOLO tracker settings for BoT-SORT tracker https://github.com/NirAharon/BoT-SORT
|
# Default YOLO tracker settings for BoT-SORT tracker https://github.com/NirAharon/BoT-SORT
|
||||||
|
|
||||||
tracker_type: botsort # tracker type, ['botsort', 'bytetrack']
|
tracker_type: botsort # tracker type, ['botsort', 'bytetrack']
|
||||||
track_high_thresh: 0.5 # threshold for the first association
|
track_high_thresh: 0.5 # threshold for the first association
|
||||||
track_low_thresh: 0.1 # threshold for the second association
|
track_low_thresh: 0.1 # threshold for the second association
|
||||||
new_track_thresh: 0.6 # threshold for init new track if the detection does not match any tracks
|
new_track_thresh: 0.6 # threshold for init new track if the detection does not match any tracks
|
||||||
track_buffer: 30 # buffer to calculate the time when to remove tracks
|
track_buffer: 30 # buffer to calculate the time when to remove tracks
|
||||||
match_thresh: 0.8 # threshold for matching tracks
|
match_thresh: 0.8 # threshold for matching tracks
|
||||||
# min_box_area: 10 # threshold for min box areas(for tracker evaluation, not used for now)
|
# min_box_area: 10 # threshold for min box areas(for tracker evaluation, not used for now)
|
||||||
# mot20: False # for tracker evaluation(not used for now)
|
# mot20: False # for tracker evaluation(not used for now)
|
||||||
|
|
||||||
# BoT-SORT settings
|
# BoT-SORT settings
|
||||||
gmc_method: sparseOptFlow # method of global motion compensation
|
gmc_method: sparseOptFlow # method of global motion compensation
|
||||||
# ReID model related thresh (not supported yet)
|
# ReID model related thresh (not supported yet)
|
||||||
proximity_thresh: 0.5
|
proximity_thresh: 0.5
|
||||||
appearance_thresh: 0.25
|
appearance_thresh: 0.25
|
||||||
|
|||||||
@ -1,11 +1,11 @@
|
|||||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||||
# Default YOLO tracker settings for ByteTrack tracker https://github.com/ifzhang/ByteTrack
|
# Default YOLO tracker settings for ByteTrack tracker https://github.com/ifzhang/ByteTrack
|
||||||
|
|
||||||
tracker_type: bytetrack # tracker type, ['botsort', 'bytetrack']
|
tracker_type: bytetrack # tracker type, ['botsort', 'bytetrack']
|
||||||
track_high_thresh: 0.5 # threshold for the first association
|
track_high_thresh: 0.5 # threshold for the first association
|
||||||
track_low_thresh: 0.1 # threshold for the second association
|
track_low_thresh: 0.1 # threshold for the second association
|
||||||
new_track_thresh: 0.6 # threshold for init new track if the detection does not match any tracks
|
new_track_thresh: 0.6 # threshold for init new track if the detection does not match any tracks
|
||||||
track_buffer: 30 # buffer to calculate the time when to remove tracks
|
track_buffer: 30 # buffer to calculate the time when to remove tracks
|
||||||
match_thresh: 0.8 # threshold for matching tracks
|
match_thresh: 0.8 # threshold for matching tracks
|
||||||
# min_box_area: 10 # threshold for min box areas(for tracker evaluation, not used for now)
|
# min_box_area: 10 # threshold for min box areas(for tracker evaluation, not used for now)
|
||||||
# mot20: False # for tracker evaluation(not used for now)
|
# mot20: False # for tracker evaluation(not used for now)
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user