CI: deploy Docker/PDM Space

.gitattributes

@@ -33,3 +33,15 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+models/*.pt filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.mov filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+annotated_video/* filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.avi filter=lfs diff=lfs merge=lfs -text
+*.json filter=lfs diff=lfs merge=lfs -text
+services/app_service/models/model_deimhgnetV2m_cpu_v0.pt.backup filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,139 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# PyCharm
+training/DEIM/.idea
+
+*.onnx
+*.zip
+*.html
+.DS_Store
+
+# idea
+.idea

Dockerfile

@@ -0,0 +1,68 @@
+FROM python:3.11-slim
+
+WORKDIR /services/app_service
+
+# System packages for video/image processing and OpenCV runtime
+RUN apt-get update && apt-get install -y \
+    ffmpeg \
+    curl \
+    git \
+    git-lfs \
+    libgl1 \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/* \
+    && git lfs install
+
+# Reduce build-time and run-time memory usage and set writable config dirs
+ENV PIP_NO_CACHE_DIR=1 \
+    PIP_ROOT_USER_ACTION=ignore \
+    OMP_NUM_THREADS=1 \
+    MKL_NUM_THREADS=1 \
+    HOME=/tmp \
+    XDG_CACHE_HOME=/tmp/.cache \
+    MPLCONFIGDIR=/tmp/matplotlib \
+    YOLO_CONFIG_DIR=/tmp/Ultralytics \
+    STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
+
+# Create config/cache dirs to avoid permission issues
+RUN mkdir -p /tmp/.streamlit /tmp/.cache /tmp/matplotlib /tmp/Ultralytics \
+    && chmod -R 777 /tmp/.streamlit /tmp/.cache /tmp/matplotlib /tmp/Ultralytics
+
+# Python deps (CPU-only PyTorch wheels)
+RUN python -m pip install --upgrade pip setuptools wheel && \
+    pip install --no-cache-dir \
+    streamlit==1.49.1 \
+    moviepy==2.2.1 \
+    pillow>=10.0.0 \
+    ultralytics==8.0.196 \
+    torch==2.5.1 torchvision==0.20.1 --extra-index-url https://download.pytorch.org/whl/cpu
+
+
+# Copy entire repository and download script
+COPY . /tmp/repo
+WORKDIR /tmp/repo
+
+# Pull LFS files and copy app service
+RUN git lfs pull && cp -r services/app_service/* /services/app_service/
+
+WORKDIR /services/app_service
+
+# Ensure app directory is writable at runtime
+RUN chmod -R 777 /services/app_service
+
+# Streamlit config via env
+ENV STREAMLIT_SERVER_ENABLECORS=false \
+    STREAMLIT_SERVER_ENABLE_XSRF_PROTECTION=false \
+    STREAMLIT_SERVER_ADDRESS=0.0.0.0 \
+    STREAMLIT_SERVER_HEADLESS=true \
+    STREAMLIT_SERVER_MAX_UPLOAD_SIZE=200
+
+# Hugging Face Spaces will set $PORT (default 7860) — listen on that 
+ENV PORT=7860
+EXPOSE 7860
+
+# Healthcheck should also use $PORT
+HEALTHCHECK CMD curl --fail http://localhost:${PORT}/_stcore/health || exit 1
+
+# Use $PORT instead of hard-coding 8501
+ENTRYPOINT ["sh", "-c", "streamlit run app.py --server.port=$PORT --server.address=0.0.0.0"]

README.md

@@ -1,12 +1,52 @@
 ---
-title: Lucid Natsar Dev
-emoji: 🚀
-colorFrom: pink
-colorTo: blue
+title: Natsar Demo
+emoji: "🚀"
+colorFrom: red
+colorTo: red
 sdk: docker
 pinned: false
-license: apache-2.0
-short_description: natsar project development environment
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Introduction
+
+Computer Vision object detection for National Search and Rescue (NATSAR)
+
+## Prerequisites
+
+1. Install conda (environment management) using terminal on VScode
+
+   - for mac user [https://www.anaconda.com/docs/getting-started/miniconda/main]
+   - for window user [https://www.anaconda.com/docs/getting-started/miniconda/main]
+
+2. Create env from conda by using the following command
+   `conda create --name natsar python=3.11`
+   it will create `natsar` (can be diffent name) environtment for this project.
+   and it is also a good practice to create separate environtment for specific project.
+
+3. Activate the environment
+   `conda activate natsar`
+
+4. Install PDM (package and dependency manager) to avoid conflict dependency
+   `pip install pdm`
+   sometimes `conda` doesn't support some libraries, then `pip` will be allowed to do. BUT use pip within the `natsar` env.
+
+5. Intstall packages and dependencies
+
+hello
+   `pdm install`
+
+## Running the project locally
+
+after install dependencies, make sure to activate the environment
+
+1. go to folder src using `cd src` on terminal
+2. run `app.py` file using `pdm run streamlit run app.py`
+
+\*\*if cloning from huggingface it might need to mount large file with git lfs
+use `pip install git-lfs` then `git lfs install`
+then `git lfs pull` to pull the files to local and `pdm run streamlit run app.py` to run
+
+## Build and Test
+
+- Main app.py file to be placed at root of NATSAR-DEMO repo.
+- The app to point to different models that sit within the nominated sub-folders

deploy-development.yaml

@@ -0,0 +1,78 @@
+# azure-pipelines.yml
+# CI/CD: Deploy entire repo to Hugging Face Space (Docker + PDM)
+# Space will build from your Dockerfile and run `src/app.py`.
+
+trigger:
+  branches:
+    include:
+      - development
+
+pool:
+  vmImage: "ubuntu-latest"
+
+variables:
+  - group: HF_TOKEN_NATSAR
+  - name: PYTHON_VERSION
+    value: "3.10"
+  - name: HF_SPACE_ID_DEV
+    value: "lucid-hf/lucid-natsar-dev"
+
+steps:
+  - checkout: self
+    lfs: true
+
+  - script: |
+      git lfs install --local
+      git lfs pull
+    displayName: "Fetch Git LFS assets"
+
+  - task: UsePythonVersion@0
+    inputs:
+      versionSpec: "$(PYTHON_VERSION)"
+
+  - script: |
+      python -m pip install --upgrade pip
+      pip install huggingface_hub==0.25.*
+      python - <<'PY'
+      import os
+      from huggingface_hub import HfApi, upload_folder
+
+      token = os.environ["HF_TOKEN"]           # provided via Pipeline variable (Secret)
+      space_id = os.environ["HF_SPACE_ID"]     # from variables above
+
+      api = HfApi(token=token)
+
+      # Ensure Space exists and uses Docker
+      api.create_repo(
+          repo_id=space_id,
+          repo_type="space",
+          exist_ok=True,
+          space_sdk="docker"
+      )
+
+      # Upload repo contents (respect ignore patterns to speed builds)
+      upload_folder(
+          folder_path=".",              # whole repo: Dockerfile, pyproject.toml, src/, models/, etc.
+          repo_id=space_id,
+          repo_type="space",
+          path_in_repo=".",             # put at Space root
+          token=token,
+          commit_message="CI: deploy Docker/PDM Space",
+          ignore_patterns=[
+              ".git/*",
+              "__pycache__/*",
+              "*.mp4", "*.avi", "*.mov",
+              "*.zip", "*.tar", "*.tar.gz",
+              "*.ipynb", "*.ipynb_checkpoints/*",
+              "venv/*", ".venv/*",
+              "dist/*", "build/*",
+              ".mypy_cache/*", ".pytest_cache/*",
+              "annotated_video/*", "annotated_images/*",
+              "training_model/*"
+          ]
+      )
+      PY
+    displayName: "Deploy to Hugging Face Space (Docker/PDM)"
+    env:
+      HF_TOKEN: $(HF_TOKEN_DEV) # Add this as a secret variable in Pipeline settings
+      HF_SPACE_ID: $(HF_SPACE_ID_DEV)

deploy-main.yaml

@@ -0,0 +1,78 @@
+# azure-pipelines.yml
+# CI/CD: Deploy entire repo to Hugging Face Space (Docker + PDM)
+# Space will build from your Dockerfile and run `src/app.py`.
+
+trigger:
+  branches:
+    include:
+      - main
+
+pool:
+  vmImage: "ubuntu-latest"
+
+variables:
+  - group: HF_TOKEN_NATSAR
+  - name: PYTHON_VERSION
+    value: "3.10"
+  - name: HF_SPACE_ID_PROD
+    value: "lucid-hf/lucid-natsar"
+
+steps:
+  - checkout: self
+    lfs: true
+
+  - script: |
+      git lfs install --local
+      git lfs pull
+    displayName: "Fetch Git LFS assets"
+
+  - task: UsePythonVersion@0
+    inputs:
+      versionSpec: "$(PYTHON_VERSION)"
+
+  - script: |
+      python -m pip install --upgrade pip
+      pip install huggingface_hub==0.25.*
+      python - <<'PY'
+      import os
+      from huggingface_hub import HfApi, upload_folder
+
+      token = os.environ["HF_TOKEN_PROD"]           # provided via Pipeline variable (Secret)
+      space_id = os.environ["HF_SPACE_ID_PROD"]     # from variables above
+
+      api = HfApi(token=token)
+
+      # Ensure Space exists and uses Docker
+      api.create_repo(
+          repo_id=space_id,
+          repo_type="space",
+          exist_ok=True,
+          space_sdk="docker"
+      )
+
+      # Upload repo contents (respect ignore patterns to speed builds)
+      upload_folder(
+          folder_path=".",              # whole repo: Dockerfile, pyproject.toml, src/, models/, etc.
+          repo_id=space_id,
+          repo_type="space",
+          path_in_repo=".",             # put at Space root
+          token=token,
+          commit_message="CI: deploy Docker/PDM Space",
+          ignore_patterns=[
+              ".git/*",
+              "__pycache__/*",
+              "*.mp4", "*.avi", "*.mov",
+              "*.zip", "*.tar", "*.tar.gz",
+              "*.ipynb", "*.ipynb_checkpoints/*",
+              "venv/*", ".venv/*",
+              "dist/*", "build/*",
+              ".mypy_cache/*", ".pytest_cache/*",
+              "annotated_video/*", "annotated_images/*",
+              "training_model/*"
+          ]
+      )
+      PY
+    displayName: "Deploy to Hugging Face Space (Docker/PDM)"
+    env:
+      HF_TOKEN: $(HF_TOKEN_PROD) # Add this as a secret variable in Pipeline settings
+      HF_SPACE_ID: $(HF_SPACE_ID_PROD)

pdm.lock

@@ -0,0 +1,1339 @@
+# This file is @generated by PDM.
+# It is not intended for manual editing.
+
+[metadata]
+groups = ["default", "dev"]
+strategy = ["inherit_metadata"]
+lock_version = "4.5.0"
+content_hash = "sha256:41dd8a8c5e0a214f7540666a09b18cdbb412b540a64ee20a349849fb951b8ae4"
+
+[[metadata.targets]]
+requires_python = "==3.11.*"
+
+[[package]]
+name = "altair"
+version = "5.5.0"
+requires_python = ">=3.9"
+summary = "Vega-Altair: A declarative statistical visualization library for Python."
+groups = ["default"]
+dependencies = [
+    "jinja2",
+    "jsonschema>=3.0",
+    "narwhals>=1.14.2",
+    "packaging",
+    "typing-extensions>=4.10.0; python_version < \"3.14\"",
+]
+files = [
+    {file = "altair-5.5.0-py3-none-any.whl", hash = "sha256:91a310b926508d560fe0148d02a194f38b824122641ef528113d029fcd129f8c"},
+    {file = "altair-5.5.0.tar.gz", hash = "sha256:d960ebe6178c56de3855a68c47b516be38640b73fb3b5111c2a9ca90546dd73d"},
+]
+
+[[package]]
+name = "attrs"
+version = "25.3.0"
+requires_python = ">=3.8"
+summary = "Classes Without Boilerplate"
+groups = ["default"]
+files = [
+    {file = "attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3"},
+    {file = "attrs-25.3.0.tar.gz", hash = "sha256:75d7cefc7fb576747b2c81b4442d4d4a1ce0900973527c011d1030fd3bf4af1b"},
+]
+
+[[package]]
+name = "blinker"
+version = "1.9.0"
+requires_python = ">=3.9"
+summary = "Fast, simple object-to-object and broadcast signaling"
+groups = ["default"]
+files = [
+    {file = "blinker-1.9.0-py3-none-any.whl", hash = "sha256:ba0efaa9080b619ff2f3459d1d500c57bddea4a6b424b60a91141db6fd2f08bc"},
+    {file = "blinker-1.9.0.tar.gz", hash = "sha256:b4ce2265a7abece45e7cc896e98dbebe6cead56bcf805a3d23136d145f5445bf"},
+]
+
+[[package]]
+name = "cachetools"
+version = "6.2.0"
+requires_python = ">=3.9"
+summary = "Extensible memoizing collections and decorators"
+groups = ["default"]
+files = [
+    {file = "cachetools-6.2.0-py3-none-any.whl", hash = "sha256:1c76a8960c0041fcc21097e357f882197c79da0dbff766e7317890a65d7d8ba6"},
+    {file = "cachetools-6.2.0.tar.gz", hash = "sha256:38b328c0889450f05f5e120f56ab68c8abaf424e1275522b138ffc93253f7e32"},
+]
+
+[[package]]
+name = "certifi"
+version = "2025.8.3"
+requires_python = ">=3.7"
+summary = "Python package for providing Mozilla's CA Bundle."
+groups = ["default"]
+files = [
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+
+[[package]]
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+version = "3.4.3"
+requires_python = ">=3.7"
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+]
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+[[package]]
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+version = "8.2.1"
+requires_python = ">=3.10"
+summary = "Composable command line interface toolkit"
+groups = ["default"]
+dependencies = [
+    "colorama; platform_system == \"Windows\"",
+]
+files = [
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+]
+
+[[package]]
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+version = "0.4.6"
+requires_python = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+summary = "Cross-platform colored terminal text."
+groups = ["default"]
+marker = "platform_system == \"Windows\""
+files = [
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+]
+
+[[package]]
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+version = "1.3.3"
+requires_python = ">=3.11"
+summary = "Python library for calculating contours of 2D quadrilateral grids"
+groups = ["default"]
+dependencies = [
+    "numpy>=1.25",
+]
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+groups = ["default"]
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+]
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+groups = ["default"]
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+
+[[package]]
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+summary = "Python Library for Tom's Obvious, Minimal Language"
+groups = ["default"]
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+
+[[package]]
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+requires_python = ">=3.9.0"
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+groups = ["default"]
+dependencies = [
+    "filelock",
+    "fsspec",
+    "jinja2",
+    "networkx",
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+    "nvidia-cuda-cupti-cu12==12.8.90; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
+    "nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
+    "nvidia-cuda-runtime-cu12==12.8.90; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
+    "nvidia-cudnn-cu12==9.10.2.21; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
+    "nvidia-cufft-cu12==11.3.3.83; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
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+    "nvidia-curand-cu12==10.3.9.90; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
+    "nvidia-cusolver-cu12==11.7.3.90; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
+    "nvidia-cusparse-cu12==12.5.8.93; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
+    "nvidia-cusparselt-cu12==0.7.1; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
+    "nvidia-nccl-cu12==2.27.3; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
+    "nvidia-nvjitlink-cu12==12.8.93; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
+    "nvidia-nvtx-cu12==12.8.90; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
+    "setuptools; python_version >= \"3.12\"",
+    "sympy>=1.13.3",
+    "triton==3.4.0; platform_system == \"Linux\" and platform_machine == \"x86_64\"",
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+]
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+[[package]]
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+groups = ["default"]
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+    "numpy",
+    "pillow!=8.3.*,>=5.3.0",
+    "torch==2.8.0",
+]
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+[[package]]
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+summary = "Fast, Extensible Progress Meter"
+groups = ["default"]
+dependencies = [
+    "colorama; platform_system == \"Windows\"",
+]
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+summary = "A language and compiler for custom Deep Learning operations"
+groups = ["default"]
+marker = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+dependencies = [
+    "importlib-metadata; python_version < \"3.10\"",
+    "setuptools>=40.8.0",
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+    "numpy>=1.23.0",
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+    "polars",
+    "psutil",
+    "pyyaml>=5.3.1",
+    "requests>=2.23.0",
+    "scipy>=1.4.1",
+    "torch!=2.4.0,>=1.8.0; sys_platform == \"win32\"",
+    "torch>=1.8.0",
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+groups = ["default"]
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+groups = ["default"]
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+]

pyproject.toml

@@ -0,0 +1,148 @@
+[project]
+name = "object_detection_project"
+version = "0.1.0"
+description = "Default template for PDM package"
+authors = [
+    {name = "", email = ""},
+]
+dependencies = [
+    "streamlit>=1.49.1",
+    "moviepy>=2.2.1",
+    "pillow>=10.0.0",
+    "ultralytics>=8.3.0",
+    "numpy>=2.2.6",
+    "torch>=2.8.0",
+    "pandas>=2.3.2",
+]
+requires-python = "==3.11.*"
+readme = "README.md"
+license = {text = "MIT"}
+
+
+[tool.pdm]
+distribution = false
+
+
+
+[tool.ruff]
+ignore = [
+
+    # (missing public docstrings) These work off of the Python sense of "public", rather than our
+    # bespoke definition based off of `@public`. When ruff supports custom plugins then we can write
+    # appropriate rules to require docstrings for `@public`.
+    "D100",
+    "D101",
+    "D102",
+    "D103",
+    "D104",
+    "D105",
+    "D106",
+    "D107",
+
+    # (docstring imperative mood) Overly restrictive.
+    "D401",
+
+    # (module level import not at top) There are several places where we use e.g.
+    # warnings.filterwarings calls before imports.
+    "E402",
+
+    # (line too long): This fires for comments, which black won't wrap.
+    # Disabling until there is an autoformat solution available for comments.
+    "E501",
+
+    # Pandas sometime need `==` for comparison instead of `is`
+    # Comparision to `None` should be done with `is` rather than `==`.
+    "E712",
+
+    # (no type comparison): There are a few places where we use `== type(None)` which are more clear
+    # than the equivalent `isinstance` check.
+    'E721',
+
+    # (bare exception): There are many places where we want to catch a maximally generic exception.
+    'E722',
+
+    # (no assign lambda): existing code assigns lambdas in a few places. With black formatting
+    # requiring extra empty lines between defs, disallowing lambda assignment can make code less
+    # readable.
+    "E731",
+
+    # (try-except-in-loop) we use this pattern in many places and the performance impact is negligible
+    "PERF203",
+
+    # (no concatenation) Existing codebase has many concatentations, no reason to disallow them.
+    "RUF005",
+
+    # (use ClassVar for attr declarations with defaults) This is a good rule for vanilla Python, but
+    # triggers false positives for many libs that have DSLs that make use of attr defaults.
+    "RUF012",
+
+
+    ##### TEMPORARY DISABLES
+
+    # (assorted docstring rules) There are too many violations of these to enable
+    # right now, but we should enable after fixing the violations.
+    "D200", # (one-line docstring should fit)
+    "D205", # (blank line after summary)
+    "D417", # (missing arg in docstring)
+    # (assorted perf rules) We have a lot of violations, enable when autofix is available
+    "PERF401", # (manual-list-comprehension)
+    "PERF402", # (manual-list-copy)
+]
+# By default, ruff only uses all "E" (pycodestyle) and "F" (pyflakes) rules.
+# Here we append to the defaults.
+select = [
+    # (flake8-builtins) detect shadowing of python builtin symbols by variables and arguments.
+    # Attributes are OK (which is why A003) is not included here.
+    "A001",
+    "A002",
+
+    # (useless expression): Expressions that aren't assigned to anything are typically bugs.
+    "B018",
+
+    # (pydocstyle) Docstring-related rules. A large subset of these are ignored by the
+    # "convention=google" setting, we set under tool.ruff.pydocstyle.
+    "D",
+
+    # (pycodestyle) pycodestyle rules
+    "E",
+
+    # (pyflakes) pyflakes rules
+    "F",
+
+    # (isort) detect improperly sorted imports
+    "I001",
+
+    # (performance) perflint rules
+    "PERF",
+
+    # (pylint) use all pylint rules from categories "Convention", "Error", and "Warning" (ruff
+    # currently implements only a subset of pylint's rules)
+    "PLE",
+    "PLW",
+
+    # (no commented out code) keep commented out code blocks out of the codebase
+    # "ERA001",
+
+    # (ruff-specific) Enable all ruff-specific checks (i.e. not ports of
+    # functionality from an existing linter).
+    "RUF",
+
+    # (private member access) Flag access to `_`-prefixed symbols. By default the various special
+    # methods on `NamedTuple` are ignored (e.g. `_replace`).
+    "SLF001",
+
+    # (flake8-type-checking) Auto-sort imports into TYPE_CHECKING blocks depending on whether
+    # they are runtime or type-only imports.
+    "TCH",
+
+    # (f-strings) use f-strings instead of .format()
+    "UP032",
+
+    # (invalid escape sequence) flag errant backslashes
+    "W605",
+]
+
+[dependency-groups]
+dev = [
+    "ruff>=0.13.1",
+]

services/app_service/.streamlit/config.toml

@@ -0,0 +1,10 @@
+[theme]
+base="dark"
+primaryColor="#f63366"
+backgroundColor="#1e1e1e"
+secondaryBackgroundColor="#31333F"
+textColor="#FAFAFA"
+font="sans serif"
+
+[client]
+showSidebarNavigation = false

services/app_service/app.py

@@ -0,0 +1,148 @@
+import base64
+from pathlib import Path
+
+import streamlit as st
+
+
+def local_image_to_data_url(path: str | Path) -> str:
+    """Convert a local image into a base64 data URL for inlined CSS backgrounds."""
+    p = Path(path)
+    if not p.is_absolute():
+        p = Path(__file__).parent / p
+    mime = "image/png" if p.suffix.lower() == ".png" else "image/jpeg"
+    b64 = base64.b64encode(p.read_bytes()).decode()
+    return f"data:{mime};base64,{b64}"
+
+
+def main() -> None:
+    st.set_page_config(
+        page_title="Home",
+        layout="wide",
+        initial_sidebar_state="expanded",
+    )
+
+    with st.sidebar:
+        st.header("Menu")
+        st.page_link("app.py", label="Home")
+        st.page_link("pages/bushland_beacon.py", label="Bushland beacon")
+        st.page_link("pages/lost_at_sea.py", label="Lost at sea")
+        st.page_link("pages/signal_watch.py", label="Detection")
+
+    hide_default_css = """
+    <style>
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    .block-container {padding-top: 0rem; padding-bottom: 0rem;}
+    </style>
+    """
+    st.markdown(hide_default_css, unsafe_allow_html=True)
+
+    bg_image = local_image_to_data_url("resources/images/rescue.png")
+
+    st.markdown(
+        f"""
+        <style>
+        :root {{
+            --text: #ffffff;
+            --text-dim: rgba(255,255,255,0.85);
+            --cta: #3e6ae1;
+            --cta-alt: #f4f4f4;
+        }}
+
+        .hero {{
+            position: relative;
+            min-height: 92vh;
+            width: 100%;
+            display: grid;
+            place-items: center;
+            text-align: center;
+            color: var(--text);
+            background-image:
+            linear-gradient(180deg, rgba(0,0,0,0.10) 0%, rgba(0,0,0,0.20) 35%, rgba(0,0,0,0.35) 100%),
+            url('{bg_image}');
+            background-size: cover;
+            background-position: center;
+            background-repeat: no-repeat;
+        }}
+
+        .hero .content {{
+            max-width: 1100px;
+            padding: 2rem 1rem 5rem;
+        }}
+
+        .hero h1 {{
+            font-size: clamp(2.6rem, 6vw, 5rem);
+            font-weight: 700;
+            letter-spacing: 0.3px;
+            margin: 0 0 0.7rem 0;
+        }}
+
+        .hero .subtitle {{
+            font-size: clamp(1rem, 2.1vw, 1.3rem);
+            color: var(--text-dim);
+            margin-bottom: 1.4rem;
+        }}
+
+        .hero .ctas {{
+            display: flex;
+            gap: 0.8rem;
+            justify-content: center;
+            flex-wrap: wrap;
+        }}
+
+        .hero .btn {{
+            border: 0;
+            border-radius: 4px;
+            padding: 0.9rem 1.4rem;
+            font-weight: 600;
+            cursor: pointer;
+            transition: transform 120ms ease, opacity 120ms ease;
+        }}
+
+        .hero .btn.primary {{ background: var(--cta); color: white; }}
+        .hero .btn.secondary {{ background: var(--cta-alt); color: #171a20; }}
+
+        .hero .btn:hover {{ transform: translateY(-1px); }}
+        .nav {{
+            position: fixed;
+            top: 0; left: 0; right: 0;
+            height: 54px;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            gap: 1.2rem;
+            backdrop-filter: saturate(180%) blur(10px);
+            background: rgba(255,255,255,0.2);
+            color: white;
+            z-index: 9999;
+        }}
+        .nav a {{
+            color: white;
+            text-decoration: none;
+            font-weight: 600;
+            font-size: 0.95rem;
+            opacity: 0.95;
+        }}
+        .nav a:hover {{ opacity: 1; }}
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )
+
+    st.markdown(
+        """
+        <section class="hero">
+            <div class="content">
+                <h2>NATSAR Search & Rescue Simulation Hub</h2>
+                <div class="subtitle">
+                    This web application helps search and rescue teams and researchers detect critical objects from images captured via drones, bodycams, or other camera systems.
+                </div>
+            </div>
+        </section>
+        """,
+        unsafe_allow_html=True,
+    )
+
+
+if __name__ == "__main__":
+    main()

services/app_service/app_old.py

@@ -0,0 +1,204 @@
+import os
+import tempfile
+import time
+from glob import glob
+from pathlib import Path
+from shutil import which
+
+import streamlit as st
+from deim_model import DeimHgnetV2MDrone
+from model import *
+from PIL import Image
+
+
+def main():
+    model = DeimHgnetV2MDrone()
+    minimum_confidence_threshold = 0.5
+
+    st.set_page_config(page_title="SatSense Demo")
+    st.title(":satellite: SatSense Demo")
+    st.markdown(
+        """
+        The SatSense demo app simplifies annotating images and videos taken by satellites. 
+        It employs cutting-edge object detection models to automatically analyze and recognize 
+        various objects in satellite imagery, including vehicles and ships.
+
+        #### How to get started
+
+        1. **Upload Satellite Imagery:** Use the sidebar to upload your satellite imagery media 
+        files for analysis.
+        2. **Review Identified Objects:** Explore the annotated objects marked by the model.
+
+        #### Tips for usage
+
+        1. Please clear any existing uploads in the sidebar before uploading a new file.
+        2. For optimal results, please upload clear and high-resolution satellite media files.
+        3. [Location SA Map Viewer](https://location.sa.gov.au/viewer/) provides satellite imagery that can be used as image input.
+
+        SatSense simplifies the process of annotating satellite imagery and allows you to 
+        export the annotated media files. Start annotating and discovering objects of interest 
+        effortlessly!
+
+        ***Note:** In its current MVP stage, the SatSense demo offers a glimpse into the 
+        world of automatic object detection in satellite imagery. Your feedback can help shape 
+        its future improvements!*
+        """
+    )
+
+    # Sidebar to set minimum confidence threshold
+    st.sidebar.header("Parameters")
+    minimum_confidence_threshold = st.sidebar.slider(
+        "Minimum confidence threshold",
+        min_value=0.0,
+        max_value=1.0,
+        step=0.1,
+        value=minimum_confidence_threshold,
+        format="%.1f",
+    )
+    st.sidebar.markdown("---")
+
+    # Sidebar for image detection
+    st.sidebar.header("Image Detection")
+    uploaded_image = st.sidebar.file_uploader(
+        "Upload an image", type=["jpg", "jpeg", "png"]
+    )
+
+    st.sidebar.markdown("---")
+
+    # Sidebar for video detection
+    st.sidebar.header("Video Detection")
+    uploaded_video = st.sidebar.file_uploader(
+        "Upload a video", type=["mp4", "avi", "mov"]
+    )
+
+    if uploaded_image:
+        st.markdown("---")
+        st.write("")
+
+        st.markdown("#### Uploaded image")
+        image = Image.open(uploaded_image)
+        st.image(image, use_column_width=True)
+
+        st.write("")
+        st.write("")
+
+        with st.spinner("Processing..."):
+            annotated_image = model.predict_image(
+                image, min_confidence=minimum_confidence_threshold
+            )
+
+        st.markdown("#### Annotated image")
+        st.image(annotated_image, use_column_width=True)
+
+    if uploaded_video:
+        st.markdown("---")
+        st.write("")
+
+        temp_dir = tempfile.mkdtemp()
+        # Preserve uploaded extension to maximize compatibility with OpenCV/YOLO
+        uploaded_ext = Path(uploaded_video.name).suffix.lower() or ".mp4"
+        temp_video_path = os.path.join(temp_dir, f"temp_video{uploaded_ext}")
+        annotated_dir = "./annotated_video"
+        os.makedirs(annotated_dir, exist_ok=True)
+        annotated_video_path_input_ext = os.path.join(
+            annotated_dir, f"temp_video{uploaded_ext}"
+        )
+        annotated_video_path_mp4 = os.path.join(annotated_dir, "temp_video.mp4")
+
+        st.markdown("#### Uploaded video")
+        uploaded_video_bytes = uploaded_video.getvalue()
+        st.video(uploaded_video_bytes)
+
+        st.write("")
+        st.write("")
+
+        progress_bar = st.progress(0.3, text="Performing object detection...")
+
+        with open(temp_video_path, "wb") as video_file:
+            video_file.write(uploaded_video.getvalue())
+
+        model.predict_video(
+            temp_video_path,
+            min_confidence=minimum_confidence_threshold,
+            target_dir_name="annotated_video",
+        )
+
+        # Resolve the actual saved annotated video. Ultralytics may write .avi even if input is .mp4
+        final_video_path = None
+        preferred_candidates = [
+            annotated_video_path_input_ext,
+            os.path.join(annotated_dir, "temp_video.mp4"),
+            os.path.join(annotated_dir, "temp_video.avi"),
+        ]
+        for cand in preferred_candidates:
+            if os.path.exists(cand):
+                final_video_path = cand
+                break
+
+        if final_video_path is None:
+            candidates = []
+            for pattern in ("*.mp4", "*.avi", "*.mov", "*.mkv", "*.webm"):
+                candidates.extend(glob(os.path.join(annotated_dir, pattern)))
+            if candidates:
+                final_video_path = max(candidates, key=os.path.getmtime)
+            else:
+                progress_bar.empty()
+                st.error(
+                    "Annotated video not found after detection. Please try again or check logs."
+                )
+                return
+
+        # If the annotated output isn't mp4, try converting with ffmpeg if available
+        if Path(final_video_path).suffix.lower() != ".mp4":
+            progress_bar.progress(0.67, text="Converting video format...")
+            if which("ffmpeg"):
+                import subprocess
+
+                try:
+                    subprocess.run(
+                        [
+                            "ffmpeg",
+                            "-y",
+                            "-i",
+                            final_video_path,
+                            "-c:v",
+                            "libx264",
+                            "-pix_fmt",
+                            "yuv420p",
+                            "-crf",
+                            "23",
+                            "-preset",
+                            "veryfast",
+                            "-an",
+                            annotated_video_path_mp4,
+                        ],
+                        check=True,
+                        stdout=subprocess.DEVNULL,
+                        stderr=subprocess.STDOUT,
+                    )
+                    final_video_path = annotated_video_path_mp4
+                except Exception:
+                    st.warning(
+                        "ffmpeg failed to convert the video. Attempting to display original format."
+                    )
+            else:
+                st.info(
+                    "Install ffmpeg to enable conversion to mp4 (e.g. `brew install ffmpeg` on macOS) or use the provided Dockerfile."
+                )
+
+        progress_bar.progress(1.0, text="Done!")
+        time.sleep(1)
+        progress_bar.empty()
+
+        st.markdown("#### Annotated video")
+        annotated_video_file = open(final_video_path, "rb")
+        annotated_video_bytes = annotated_video_file.read()
+        # Let Streamlit infer format from the file when possible
+        st.video(annotated_video_bytes)
+
+    st.markdown("---")
+    st.markdown("Demo built by [Lucid Insights Pty Ltd](https://lucidinsights.com.au).")
+
+
+if __name__ == "__main__":
+    main()

services/app_service/deim_model.py

@@ -0,0 +1,228 @@
+import json
+import os
+from pathlib import Path
+from typing import Tuple
+
+import cv2
+import numpy as np
+import torch
+import torchvision.transforms as T
+from model import BaseModel
+from PIL import Image
+
+_TORCH_MIN_VERSION = (2, 5)
+
+
+def _parse_version(version_str: str) -> Tuple[int, ...]:
+    parts = []
+    for piece in version_str.split("+")[0].split("."):
+        try:
+            parts.append(int(piece))
+        except ValueError:
+            break
+    return tuple(parts)
+
+
+class DeimHgnetV2MDrone(BaseModel):
+    def __init__(self, device: str = "cpu", version: str = "v2"):
+        repo_root = Path(__file__).resolve().parents[1]
+        default_rel = (
+            Path("app_service") / "models" / f"model_deimhgnetV2m_{device}_{version}.pt"
+        )
+        # Allow explicit override via env var
+        override = (
+            Path(os.environ["DEIM_WEIGHTS_PATH"])
+            if "DEIM_WEIGHTS_PATH" in os.environ
+            else None
+        )
+
+        candidate_paths = [
+            override,
+            repo_root / default_rel,
+            Path(__file__).resolve().parent
+            / "models"
+            / f"model_deimhgnetV2m_{device}_{version}.pt",
+            Path.cwd() / "services" / default_rel,
+            Path("/app") / "services" / default_rel,
+        ]
+        weights_path = next((p for p in candidate_paths if p and p.exists()), None)
+        if weights_path is None:
+            models_dir = Path(__file__).resolve().parent / "models"
+            alt_models_dir = repo_root / "app_service" / "models"
+            available = []
+            for d in [models_dir, alt_models_dir]:
+                try:
+                    if d.exists():
+                        available.extend(str(p.name) for p in d.glob("*.pt"))
+                except Exception:
+                    pass
+            searched = [str(p) for p in candidate_paths if p]
+            raise FileNotFoundError(
+                "Model weights not found. Looked in: "
+                + "; ".join(searched)
+                + ". Available .pt files: "
+                + (", ".join(sorted(set(available))) or "<none>")
+            )
+
+        self.device = device
+        cfg_path = weights_path.with_suffix(".json")
+        if not cfg_path.exists():
+            raise FileNotFoundError(
+                f"Config JSON not found next to weights: {cfg_path}"
+            )
+        version_tuple = _parse_version(torch.__version__)
+        if version_tuple < _TORCH_MIN_VERSION:
+            raise RuntimeError(
+                "PyTorch {} is too old for these weights. "
+                "Please upgrade to >= {}.{} (e.g. set torch==2.5.1 in Dockerfile).".format(
+                    torch.__version__, *_TORCH_MIN_VERSION
+                )
+            )
+
+        size_bytes = weights_path.stat().st_size
+        if size_bytes < 1_000_000:
+            raise RuntimeError(
+                f"Weights file at {weights_path} is only {size_bytes} bytes. "
+                "This usually means Git LFS pointers were copied instead of the binary file. "
+                "Run `git lfs pull` before building the container to fetch the real weights."
+            )
+
+        self.cfg = json.load(open(cfg_path, "r"))
+        self.model = torch.jit.load(weights_path, map_location=device).eval()
+
+    def _preprocess_image(self, image: Image):
+        transforms = T.Compose(
+            [
+                T.Resize((self.cfg["target_size"][0], self.cfg["target_size"][1])),
+                T.ToTensor(),
+            ]
+        )
+        return transforms(image).unsqueeze(0).to(self.device)
+
+    def _postprocess_detections(
+        self, scores, bboxes, min_confidence: float, wh: Tuple[int, int]
+    ):
+        w, h = wh
+        b_np = bboxes[0].cpu().numpy()
+        s_np = scores.sigmoid()[0].cpu().numpy()
+        mask = (s_np >= min_confidence).squeeze()
+        if not mask.any():
+            return np.zeros((0, 5), dtype=np.float32)
+        valid = b_np[mask]
+        cx, cy, box_w, box_h = valid[:, 0], valid[:, 1], valid[:, 2], valid[:, 3]
+        x1 = cx - box_w / 2
+        y1 = cy - box_h / 2
+        x2 = cx + box_w / 2
+        y2 = cy + box_h / 2
+        valid_xyxy = np.stack([x1, y1, x2, y2], axis=1) * [w, h, w, h]
+        return np.concatenate([valid_xyxy, s_np[mask]], axis=1)
+
+    def _nms(self, dets):
+        if dets.shape[0] == 0 or self.cfg["nms_iou_thr"] <= 0:
+            return dets
+        x1 = dets[:, 0]
+        y1 = dets[:, 1]
+        x2 = dets[:, 2]
+        y2 = dets[:, 3]
+        scores = dets[:, 4]
+        areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+        order = scores.argsort()[::-1]
+
+        keep = []
+        while order.size > 0:
+            i = order[0]
+            keep.append(i)
+            xx1 = np.maximum(x1[i], x1[order[1:]])
+            yy1 = np.maximum(y1[i], y1[order[1:]])
+            xx2 = np.minimum(x2[i], x2[order[1:]])
+            yy2 = np.minimum(y2[i], y2[order[1:]])
+            w = np.maximum(0.0, xx2 - xx1 + 1)
+            h = np.maximum(0.0, yy2 - yy1 + 1)
+            inter = w * h
+            iou = inter / (areas[i] + areas[order[1:]] - inter)
+            inds = np.where(iou <= self.cfg["nms_iou_thr"])[0]
+            order = order[inds + 1]
+        return dets[keep]
+
+    def _draw_detections_on_np(
+        self, image_np: np.ndarray, dets: np.ndarray
+    ) -> np.ndarray:
+        for bbox in dets:
+            x1, y1, x2, y2, confidence = bbox
+            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+            cv2.rectangle(image_np, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            label = f"{confidence:.2f}"
+            label_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)[0]
+            cv2.rectangle(
+                image_np,
+                (x1, y1 - label_size[1] - 10),
+                (x1 + label_size[0], y1),
+                (0, 255, 0),
+                -1,
+            )
+            cv2.putText(
+                image_np,
+                label,
+                (x1, y1 - 5),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.5,
+                (0, 0, 0),
+                1,
+            )
+        return image_np
+
+    def predict_image(self, image: Image, min_confidence: float) -> Image:
+        tensor = self._preprocess_image(image.copy())
+        with torch.no_grad():
+            labels, bboxes = self.model(tensor)
+        dets = self._postprocess_detections(labels, bboxes, min_confidence, image.size)
+        dets = self._nms(dets)
+        image_np: np.ndarray = np.array(image)
+        image_np = self._draw_detections_on_np(image_np, dets)
+        return Image.fromarray(image_np)
+
+    def predict_video(
+        self, video, min_confidence: float, target_dir_name="annotated_video"
+    ):
+        input_path = str(video)
+        cap = cv2.VideoCapture(input_path)
+        if not cap.isOpened():
+            raise ValueError(f"Cannot open video: {input_path}")
+
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+        input_p = Path(input_path)
+        out_dir = Path(target_dir_name)
+        out_dir.mkdir(parents=True, exist_ok=True)
+        out_path = out_dir / f"{input_p.stem}_annotated.mp4"
+
+        try:
+            while True:
+                ret, frame_bgr = cap.read()
+                if not ret:
+                    break
+                frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
+                pil_img = Image.fromarray(frame_rgb)
+
+                tensor = self._preprocess_image(pil_img.copy())
+                with torch.no_grad():
+                    labels, bboxes = self.model(tensor)
+                dets = self._postprocess_detections(
+                    labels, bboxes, min_confidence, (width, height)
+                )
+                dets = self._nms(dets)
+
+                frame_bgr = self._draw_detections_on_np(frame_bgr, dets)
+                cv2.imshow("img", frame_bgr)
+                cv2.waitKey(1)
+        finally:
+            cap.release()
+
+        return str(out_path)
+
+
+if __name__ == "__main__":
+    model = DeimHgnetV2MDrone(version="v2")
+    output_image = model.predict_video("./resources/videos/raw/sample1.mp4", 0.3)
+    output_image.show()

services/app_service/model.py

@@ -0,0 +1,90 @@
+from abc import ABC, abstractmethod
+from pathlib import Path
+
+from PIL import Image
+from ultralytics import YOLO
+
+
+class BaseModel(ABC):
+    @abstractmethod
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def predict_image(self, image):
+        pass
+
+    @abstractmethod
+    def predict_video(self, video):
+        pass
+
+
+class YOLOModelv1(BaseModel):
+    """Model: modelYOLOv8n_datasetDOTAv2_epochs5_batch1.pt"""
+
+    def __init__(self):
+        repo_root = Path(__file__).resolve().parents[1]
+        weights_path = (
+            repo_root / "models" / "modelYOLOv8n_datasetDOTAv2_epochs5_batch1.pt"
+        )
+        self.model = YOLO(str(weights_path), task="detect")
+
+    def predict_image(self, image, min_confidence):
+        results = self.model.predict(image, save=False, imgsz=640, conf=min_confidence)
+        annotated_image_filename = "annotated_image.png"
+        last_im = None
+        for result in results:
+            im_array = result.plot()
+            last_im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
+            last_im.save(annotated_image_filename)
+        # Return PIL Image for robust display in Streamlit
+        return last_im if last_im is not None else Image.open(annotated_image_filename)
+
+    def predict_video(self, video, min_confidence, target_dir_name="annotated_video"):
+        self.model.predict(
+            video,
+            save=True,
+            project=".",
+            name=target_dir_name,
+            exist_ok=True,
+            imgsz=640,
+            conf=min_confidence,
+        )
+
+
+class YOLOModelv2(BaseModel):
+    """Model: modelYOLOv8n_datasetDIOR_epochs50_batch16.pt"""
+
+    def __init__(self):
+        repo_root = Path(__file__).resolve().parents[1]
+        weights_path = (
+            repo_root / "models" / "modelYOLOv8n_datasetDIOR_epochs50_batch16.pt"
+        )
+        self.model = YOLO(str(weights_path), task="detect")
+
+    def predict_image(self, image, min_confidence, classes=None):
+        results = self.model.predict(
+            image, save=False, imgsz=800, conf=min_confidence, classes=classes
+        )
+        annotated_image_filename = "annotated_image.png"
+        last_im = None
+        for result in results:
+            im_array = result.plot()
+            last_im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
+            last_im.save(annotated_image_filename)
+        # Return PIL Image for robust display in Streamlit
+        return last_im if last_im is not None else Image.open(annotated_image_filename)
+
+    def predict_video(
+        self, video, min_confidence, target_dir_name="annotated_video", classes=None
+    ):
+        self.model.predict(
+            video,
+            save=True,
+            project=".",
+            name=target_dir_name,
+            exist_ok=True,
+            imgsz=800,
+            conf=min_confidence,
+            classes=classes,
+        )

services/app_service/models/model_deimhgnetV2m_cpu_v0.json

@@ -0,0 +1,4 @@
+{
+  "target_size": [960, 1280],
+  "nms_iou_thr": 0.8
+}

services/app_service/models/model_deimhgnetV2m_cpu_v0.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2d7cf84155a58af2e964b1818c7b648be5e4de12129fc92f78c2c222648d70db
+size 78217733

services/app_service/models/model_deimhgnetV2m_cpu_v0.pt.backup

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2d7cf84155a58af2e964b1818c7b648be5e4de12129fc92f78c2c222648d70db
+size 78217733

services/app_service/models/model_deimhgnetV2m_cpu_v2.json

@@ -0,0 +1,4 @@
+{
+  "target_size": [960, 1280],
+  "nms_iou_thr": 0.8
+}

services/app_service/models/model_deimhgnetV2m_cpu_v2.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:04c4f81c686928daee97697ace4545f833c8e02fb0c248e4133cfb7e20539b9a
+size 78311936

services/app_service/models/model_deimhgnetV2m_cuda_v2.json

@@ -0,0 +1,4 @@
+{
+  "target_size": [960, 1280],
+  "nms_iou_thr": 0.8
+}

services/app_service/models/model_deimhgnetV2m_cuda_v2.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d2812bfe5e796c8389b447d772bf0c1403556d1eca2d40d4cd5b0dbe8ee83401
+size 78254179

services/app_service/models/yolov8n.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f59b3d833e2ff32e194b5bb8e08d211dc7c5bdf144b90d2c8412c47ccfc83b36
+size 6549796

services/app_service/pages/bushland_beacon.py

@@ -0,0 +1,116 @@
+# pages/bushland_beacon.py
+import tempfile
+import time
+from pathlib import Path
+
+import streamlit as st
+from PIL import Image
+
+from deim_model import DeimHgnetV2MDrone
+
+# =============== Page setup ===============
+st.set_page_config(
+    page_title="Bushland Beacon ", layout="wide", initial_sidebar_state="expanded"
+)
+st.markdown(
+    "<h2 style='text-align:center;margin-top:0'>S A R - X ai\u200b</h2>"
+    "<h2 style='text-align:center;margin-top:0'>Bushland Beacon 🚨 </h2>",
+    unsafe_allow_html=True,
+)
+# =============== Sidebar: custom menu + cards ===============
+with st.sidebar:
+    st.header("Menu")
+    st.page_link("app.py", label="Home")
+    st.page_link("pages/lost_at_sea.py", label="Lost at Sea")
+    st.page_link("pages/bushland_beacon.py", label="Bushland Beacon")
+    st.page_link("pages/signal_watch.py", label="Signal Watch")
+
+    st.markdown("---")
+
+    # Simple "card" styling in the sidebar
+    st.markdown(
+        """
+    <style>
+    .sb-card {border:1px solid rgba(255,255,255,0.15); padding:14px; border-radius:8px; margin-bottom:16px;}
+    .sb-card h4 {margin:0 0 10px 0; font-weight:700;}
+    </style>
+    """,
+        unsafe_allow_html=True,
+    )
+
+    # Image Detection card
+    st.markdown("<h4>Image Detection</h4>", unsafe_allow_html=True)
+    img_file = st.file_uploader(
+        "Upload an image", type=["jpg", "jpeg", "png"], key="img_up"
+    )
+    run_img = st.button("🔍 Run Image Detection", use_container_width=True)
+
+    # Video Detection card
+    st.markdown("<h4>Video Detection</h4>", unsafe_allow_html=True)
+    vid_file = st.file_uploader(
+        "Upload a video", type=["mp4", "mov", "avi", "mkv"], key="vid_up"
+    )
+    run_vid = st.button("🎥 Run Video Detection", use_container_width=True)
+
+    # Parameters card (shared)
+    st.markdown("<h4>Parameters</h4>", unsafe_allow_html=True)
+    conf_thr = st.slider("Minimum confidence threshold", 0.05, 0.95, 0.50, 0.01)
+
+@st.cache_resource
+def load_model(device: str = "cpu"):
+    return DeimHgnetV2MDrone(device=device)
+
+
+# =============== Detection helpers ===============
+def run_image_detection(uploaded_file, conf_thr: float = 0.5):
+    img = Image.open(uploaded_file).convert("RGB")
+    st.image(img, caption="Uploaded Image", use_container_width=True)
+
+    model = load_model()
+    with st.spinner("Running detection..."):
+        annotated = model.predict_image(img, min_confidence=conf_thr)
+
+    st.subheader("🎯 Detection Results")
+    st.image(annotated, caption="Detections", use_container_width=True)
+
+
+def run_video_detection(vid_bytes, conf_thr: float = 0.5):
+    tmp_in = Path(tempfile.gettempdir()) / f"in_{int(time.time())}.mp4"
+    with open(tmp_in, "wb") as f:
+        f.write(vid_bytes)
+
+    model = load_model()
+    with st.spinner("Processing video…"):
+        try:
+            out_path = model.predict_video(
+                tmp_in,
+                min_confidence=conf_thr,
+                target_dir_name="annotated_video",
+            )
+        except Exception as exc:  # pragma: no cover - surface message to UI
+            st.error(f"Video detection failed: {exc}")
+            return
+
+    st.success("Done!")
+    st.video(str(out_path))
+    with open(out_path, "rb") as f:
+        st.download_button(
+            "Download processed video",
+            data=f.read(),
+            file_name=Path(out_path).name,
+            mime="video/mp4",
+        )
+
+
+# =============== Main: hook up actions ===============
+if run_img:
+    if img_file is None:
+        st.warning("Please upload an image first.")
+    else:
+        run_image_detection(img_file, conf_thr=conf_thr)
+
+if run_vid:
+    if vid_file is None:
+        st.warning("Please upload a video first.")
+    else:
+        run_video_detection(vid_bytes=vid_file.read(), conf_thr=conf_thr)

services/app_service/pages/lost_at_sea.py

@@ -0,0 +1,118 @@
+# pages/lost_at_sea.py
+import tempfile
+import time
+from pathlib import Path
+
+import streamlit as st
+from deim_model import DeimHgnetV2MDrone
+from PIL import Image
+
+# =============== Page setup ===============
+st.set_page_config(
+    page_title="Lost at Sea", layout="wide", initial_sidebar_state="expanded"
+)
+
+st.markdown(
+    "<h2 style='text-align:center;margin-top:0'>S A R - X ai\u200b</h2>"
+    "<h2 style='text-align:center;margin-top:0'>Lost at Sea 🌊</h2>",
+    unsafe_allow_html=True,
+)
+
+# =============== Sidebar: custom menu + cards ===============
+with st.sidebar:
+    st.header("Menu")
+    st.page_link("app.py", label="Home")
+    st.page_link("pages/lost_at_sea.py", label="Lost at Sea")
+    st.page_link("pages/bushland_beacon.py", label="Bushland Beacon")
+    st.page_link("pages/signal_watch.py", label="Signal Watch")
+
+    st.markdown("---")
+
+    # Simple "card" styling in the sidebar
+    st.markdown(
+        """
+    <style>
+    .sb-card {border:1px solid rgba(255,255,255,0.15); padding:14px; border-radius:8px; margin-bottom:16px;}
+    .sb-card h4 {margin:0 0 10px 0; font-weight:700;}
+    </style>
+    """,
+        unsafe_allow_html=True,
+    )
+
+    # Image Detection card
+    st.markdown("<h4>Image Detection</h4>", unsafe_allow_html=True)
+    img_file = st.file_uploader(
+        "Upload an image", type=["jpg", "jpeg", "png"], key="img_up"
+    )
+    run_img = st.button("🔍 Run Image Detection", use_container_width=True)
+
+    # Video Detection card
+    st.markdown("<h4>Video Detection</h4>", unsafe_allow_html=True)
+    vid_file = st.file_uploader(
+        "Upload a video", type=["mp4", "mov", "avi", "mkv"], key="vid_up"
+    )
+    run_vid = st.button("🎥 Run Video Detection", use_container_width=True)
+
+    # Parameters card (shared)
+    st.markdown("<h4>Parameters</h4>", unsafe_allow_html=True)
+    conf_thr = st.slider("Minimum confidence threshold", 0.05, 0.95, 0.50, 0.01)
+
+
+@st.cache_resource
+def load_model(device: str = "cpu"):
+    return DeimHgnetV2MDrone(device=device)
+
+
+# =============== Detection helpers ===============
+def run_image_detection(uploaded_file, conf_thr: float = 0.5):
+    img = Image.open(uploaded_file).convert("RGB")
+    st.image(img, caption="Uploaded Image", use_container_width=True)
+
+    model = load_model()
+    with st.spinner("Running detection..."):
+        annotated = model.predict_image(img, min_confidence=conf_thr)
+
+    st.subheader("🎯 Detection Results")
+    st.image(annotated, caption="Detections", use_container_width=True)
+
+
+def run_video_detection(vid_bytes, conf_thr: float = 0.5):
+    tmp_in = Path(tempfile.gettempdir()) / f"in_{int(time.time())}.mp4"
+    with open(tmp_in, "wb") as f:
+        f.write(vid_bytes)
+
+    model = load_model()
+    with st.spinner("Processing video…"):
+        try:
+            out_path = model.predict_video(
+                tmp_in,
+                min_confidence=conf_thr,
+                target_dir_name="annotated_video",
+            )
+        except Exception as exc:  # pragma: no cover - surface message to UI
+            st.error(f"Video detection failed: {exc}")
+            return
+
+    st.success("Done!")
+    st.video(str(out_path))
+    with open(out_path, "rb") as f:
+        st.download_button(
+            "Download processed video",
+            data=f.read(),
+            file_name=Path(out_path).name,
+            mime="video/mp4",
+        )
+
+
+# =============== Main: hook up actions ===============
+if run_img:
+    if img_file is None:
+        st.warning("Please upload an image first.")
+    else:
+        run_image_detection(img_file, conf_thr=conf_thr)
+
+if run_vid:
+    if vid_file is None:
+        st.warning("Please upload a video first.")
+    else:
+        run_video_detection(vid_bytes=vid_file.read(), conf_thr=conf_thr)

services/app_service/pages/signal_watch.py

@@ -0,0 +1,255 @@
+import tempfile
+import time
+from pathlib import Path
+
+import cv2
+import numpy as np
+import streamlit as st
+from deim_model import DeimHgnetV2MDrone
+from PIL import Image
+
+# --- Page setup ---
+st.set_page_config(page_title="signal watch", layout="wide")
+
+st.markdown(
+    "<h2 style='text-align:center;margin-top:0'>S A R - X ai\u200b</h2>"
+    "<h2 style='text-align:center;margin-top:0'>Signal Watch 👁️</h2>",
+    unsafe_allow_html=True,
+)
+
+# =============== Sidebar: custom menu + cards ===============
+with st.sidebar:
+    st.header("Menu")
+    st.page_link("app.py", label="Home")
+    st.page_link("pages/lost_at_sea.py", label="Lost at Sea")
+    st.page_link("pages/bushland_beacon.py", label="Bushland Beacon")
+    st.page_link("pages/signal_watch.py", label="Signal Watch")
+
+    st.markdown("---")
+# ---------- Model loading ----------
+try:
+    from ultralytics import YOLO
+
+    YOLO_AVAILABLE = True
+except Exception:
+    YOLO = None
+    YOLO_AVAILABLE = False
+
+
+MODELS_DIR = Path(__file__).resolve().parents[1] / "models"
+
+
+def _discover_model_entries() -> list[tuple[str, str]]:
+    entries: list[tuple[str, str]] = [("DEIM (NATSAR bespoke)", "deim")]
+
+    if MODELS_DIR.exists():
+        for pt_path in sorted(MODELS_DIR.glob("*.pt")):
+            name_lower = pt_path.name.lower()
+            if "deimhgnet" in name_lower:
+                continue
+            entries.append((pt_path.name, f"yolo:{pt_path.resolve()}"))
+
+    return entries
+
+
+MODEL_ENTRIES = _discover_model_entries()
+
+
+@st.cache_resource
+def load_model(model_key: str, device: str = "cpu"):
+    if model_key == "deim":
+        return DeimHgnetV2MDrone(device=device)
+    if not model_key.startswith("yolo:"):
+        raise ValueError(f"Unknown model key: {model_key}")
+    if not YOLO_AVAILABLE:
+        raise RuntimeError(
+            "Ultralytics YOLO weights requested but the package is not installed."
+        )
+
+    weight_path = Path(model_key.split(":", 1)[1])
+    if not weight_path.exists():
+        raise FileNotFoundError(
+            f"YOLO weights not found at {weight_path}. Place the .pt file there or "
+            "choose the DEIM model."
+        )
+    return YOLO(str(weight_path))
+
+
+def draw_yolo_dets(frame_bgr, result, show_score=True):
+    out = frame_bgr.copy()
+    boxes = getattr(result, "boxes", None)
+    if boxes is None:
+        return out
+    names = result.names
+    cls_ids = boxes.cls.cpu().numpy().astype(int)
+    confs = boxes.conf.cpu().numpy()
+    xyxy = boxes.xyxy.cpu().numpy()
+    for (x1, y1, x2, y2), cls, score in zip(xyxy, cls_ids, confs):
+        x1, y1, x2, y2 = map(int, (x1, y1, x2, y2))
+        label = names.get(int(cls), str(int(cls)))
+        if show_score:
+            label = f"{label} {score:.2f}"
+        cv2.rectangle(out, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        (tw, th), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)
+        cv2.rectangle(out, (x1, y1 - th - 8), (x1 + tw + 6, y1), (0, 255, 0), -1)
+        cv2.putText(
+            out,
+            label,
+            (x1 + 3, max(0, y1 - 6)),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.6,
+            (0, 0, 0),
+            2,
+            cv2.LINE_AA,
+        )
+    return out
+
+
+# ---------- Sidebar: Video feed dropdown ----------
+st.sidebar.header("Video feed")
+
+VIDEO_DIR = Path(__file__).resolve().parents[1] / "resources" / "videos" / "raw"
+
+video_map = {}
+if VIDEO_DIR.exists():
+    for p in sorted(VIDEO_DIR.glob("*")):
+        if p.suffix.lower() in {".mp4", ".mov", ".avi", ".mkv"}:
+            video_map[p.name] = str(p)
+
+source_options = ["Webcam (0)"] + list(video_map.keys()) + ["Custom URL…"]
+src_choice = st.sidebar.selectbox("Source", source_options, index=0)
+
+custom_url = None
+if src_choice == "Custom URL…":
+    custom_url = st.sidebar.text_input(
+        "RTSP/HTTP URL", placeholder="rtsp://... or https://..."
+    )
+
+st.sidebar.header("Parameters")
+label_to_key: dict[str, str] = {label: key for label, key in MODEL_ENTRIES}
+available_model_labels = []
+for label, key in MODEL_ENTRIES:
+    if key == "deim":
+        available_model_labels.append(label)
+        continue
+    if not key.startswith("yolo:"):
+        continue
+    weight_path = Path(key.split(":", 1)[1])
+    if YOLO_AVAILABLE and weight_path.exists():
+        available_model_labels.append(label)
+
+if not available_model_labels:
+    available_model_labels = ["DEIM (NATSAR bespoke)"]
+
+model_label = st.sidebar.selectbox("Model", available_model_labels, index=0)
+model_key = label_to_key.get(model_label, "deim")
+confidence_threshold = st.sidebar.slider("Minimum Confidence", 0.0, 1.0, 0.5, 0.01)
+frame_stride = st.sidebar.slider("Process every Nth frame", 1, 5, 2, 1)
+max_seconds = st.sidebar.slider("Max seconds (webcam/live)", 3, 60, 12, 1)
+run_detection = st.sidebar.button("🎥 Run Detection", use_container_width=True)
+
+
+# ---------- Main UI logic ----------
+
+
+def resolve_source():
+    if src_choice == "Webcam (0)":
+        return 0
+    if src_choice == "Custom URL…":
+        return custom_url.strip() if custom_url else None
+    return video_map.get(src_choice)
+
+
+def run_video_feed_detection(cap_source, conf_thr: float, stride: int, model_key: str):
+    try:
+        model = load_model(model_key)
+    except Exception as exc:
+        st.error(str(exc))
+        return
+
+    cap = cv2.VideoCapture(cap_source)
+    if not cap.isOpened():
+        st.error("Failed to open the selected source.")
+        return
+
+    is_file = isinstance(cap_source, str) and Path(str(cap_source)).exists()
+    fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
+    total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT) or 0)
+
+    frame_ph = st.empty()
+    prog = st.progress(0.0, text="Processing…")
+    start_t = time.time()
+    i = 0
+
+    writer = None
+    out_path = None
+    if is_file:
+        W = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        H = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        out_path = Path(tempfile.gettempdir()) / f"out_{int(time.time())}.mp4"
+        writer = cv2.VideoWriter(
+            str(out_path), cv2.VideoWriter_fourcc(*"mp4v"), fps, (W, H)
+        )
+
+    while True:
+        ok, frame = cap.read()
+        if not ok:
+            break
+
+        if i % max(1, stride) == 0:
+            if model_key == "deim":
+                frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                annotated_pil = model.predict_image(  # type: ignore[call-arg]
+                    Image.fromarray(frame_rgb), min_confidence=conf_thr
+                )
+                vis = cv2.cvtColor(np.array(annotated_pil), cv2.COLOR_RGB2BGR)
+            else:
+                results = model.predict(frame, conf=conf_thr, iou=0.5, verbose=False)
+                vis = draw_yolo_dets(frame, results[0], show_score=True)
+            frame_ph.image(
+                cv2.cvtColor(vis, cv2.COLOR_BGR2RGB), use_container_width=True
+            )
+            if writer is not None:
+                writer.write(vis)
+
+        i += 1
+
+        if is_file and total:
+            prog.progress(min(i / total, 1.0), text=f"Processing… {i}/{total}")
+        else:
+            elapsed = time.time() - start_t
+            prog.progress(
+                min(elapsed / max(1, max_seconds), 1.0), text=f"Live… {int(elapsed)}s"
+            )
+            if elapsed >= max_seconds:
+                break
+
+    cap.release()
+    if writer is not None:
+        writer.release()
+
+    st.success("Done!")
+    if out_path and out_path.exists():
+        st.video(str(out_path))
+        with open(out_path, "rb") as f:
+            st.download_button(
+                "Download processed video",
+                data=f.read(),
+                file_name="detections.mp4",
+                mime="video/mp4",
+            )
+
+
+if run_detection:
+    src = resolve_source()
+    if not src:
+        st.warning("Please select a valid source (pick a file or enter a URL).")
+    else:
+        run_video_feed_detection(
+            cap_source=src,
+            conf_thr=confidence_threshold,
+            stride=frame_stride,
+            model_key=model_key,
+        )
+else:
+    st.info("Pick a video source from the sidebar and click **Run Detection**.")

services/app_service/utils/cache.py

@@ -0,0 +1,6 @@
+import hashlib, json
+
+def hash_config(*args, **kwargs):
+    payload = {"args": args, "kwargs": kwargs}
+    s = json.dumps(payload, sort_keys=True, default=str).encode("utf-8")
+    return hashlib.md5(s).hexdigest()

services/app_service/utils/inference.py

@@ -0,0 +1,30 @@
+from ultralytics import YOLO
+import numpy as np
+
+# Simple in-memory cache keyed by (key, frame_idx)
+class CachedDetections:
+    def __init__(self): self._m = {}
+    def get(self, key, i): return self._m.get((key, i))
+    def put(self, key, i, dets): self._m[(key, i)] = dets
+
+class Detector:
+    def __init__(self, model_name, conf_thr=0.35, iou_thr=0.5):
+        self.model = YOLO(model_name)
+        self.conf_thr = conf_thr
+        self.iou_thr = iou_thr
+
+    def predict(self, frame_bgr):
+        # Ultralytics expects RGB
+        results = self.model.predict(frame_bgr[..., ::-1], conf=self.conf_thr, iou=self.iou_thr, verbose=False)
+        dets = []
+        if not results:
+            return dets
+        r = results[0]
+        if r.boxes is None: return dets
+        for b in r.boxes:
+            cls_id = int(b.cls[0])
+            conf = float(b.conf[0])
+            x1,y1,x2,y2 = map(float, b.xyxy[0].tolist())
+            label = r.names.get(cls_id, str(cls_id))
+            dets.append((label, conf, (x1,y1,x2,y2)))
+        return dets

services/app_service/utils/ir_sim.py

@@ -0,0 +1,7 @@
+import cv2
+
+def to_ir(frame_bgr):
+    gray = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2GRAY)
+    gray = cv2.equalizeHist(gray)
+    ir = cv2.applyColorMap(gray, cv2.COLORMAP_INFERNO)
+    return ir

services/app_service/utils/overlays.py

@@ -0,0 +1,20 @@
+import cv2
+import time
+
+COLOR = (0, 255, 0)
+
+def draw_boxes(img, detections, show_scores=True):
+    out = img.copy()
+    for cls, conf, (x1,y1,x2,y2) in detections:
+        cv2.rectangle(out, (int(x1),int(y1)), (int(x2),int(y2)), COLOR, 2)
+        if show_scores:
+            label = f"{cls} {conf:.2f}"
+            cv2.putText(out, label, (int(x1), int(y1)-6),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, COLOR, 2, cv2.LINE_AA)
+    return out
+
+def draw_hud(img, fps=0.0, model="yolov8n.pt"):
+    out = img.copy()
+    hud = f"{model} | {fps:.1f} FPS | {time.strftime('%H:%M:%S')}"
+    cv2.putText(out, hud, (12, 28), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,255), 2, cv2.LINE_AA)
+    return out

services/app_service/utils/video_io.py

@@ -0,0 +1,25 @@
+import cv2
+
+def frame_generator(video_path, stride=1):
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        raise FileNotFoundError(f"Cannot open video: {video_path}")
+    i = 0
+    while True:
+        ok, frame = cap.read()
+        if not ok:
+            break
+        if i % stride == 0:
+            yield frame
+        i += 1
+    cap.release()
+
+class VideoWriter:
+    def __init__(self, out_path, fps, width, height, fourcc="mp4v"):
+        self._writer = cv2.VideoWriter(
+            out_path, cv2.VideoWriter_fourcc(*fourcc), fps, (width, height)
+        )
+    def write(self, frame):
+        self._writer.write(frame)
+    def release(self):
+        self._writer.release()

services/training_service/LICENSE

@@ -0,0 +1,208 @@
+Copyright (C) INTELLINDUST INFORMATION TECHNOLOGY (SHENZHEN) CO., LTD. and all its affiliates.  
+
+DEIM is licensed under the Apache License. 
+
+A copy of the Apache License License is included in this file.
+
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services/training_service/README.md

@@ -0,0 +1,449 @@
+<h2 align="center">
+  DEIM: DETR with Improved Matching for Fast Convergence
+</h2>
+
+<p align="center">
+    <a href="https://github.com/ShihuaHuang95/DEIM/blob/master/LICENSE">
+        <img alt="license" src="https://img.shields.io/badge/LICENSE-Apache%202.0-blue">
+    </a>
+    <a href="https://arxiv.org/abs/2412.04234">
+        <img alt="arXiv" src="https://img.shields.io/badge/arXiv-2412.04234-red">
+    </a>
+   <a href="https://www.shihuahuang.cn/DEIM/">
+        <img alt="project webpage" src="https://img.shields.io/badge/Webpage-DEIM-purple">
+    </a>
+    <a href="https://github.com/ShihuaHuang95/DEIM/pulls">
+        <img alt="prs" src="https://img.shields.io/github/issues-pr/ShihuaHuang95/DEIM">
+    </a>
+    <a href="https://github.com/ShihuaHuang95/DEIM/issues">
+        <img alt="issues" src="https://img.shields.io/github/issues/ShihuaHuang95/DEIM?color=olive">
+    </a>
+    <a href="https://github.com/ShihuaHuang95/DEIM">
+        <img alt="stars" src="https://img.shields.io/github/stars/ShihuaHuang95/DEIM">
+    </a>
+    <a href="mailto:shihuahuang95@gmail.com">
+        <img alt="Contact Us" src="https://img.shields.io/badge/Contact-Email-yellow">
+    </a>
+</p>
+
+<p align="center">
+    DEIM is an advanced training framework designed to enhance the matching mechanism in DETRs, enabling faster convergence and improved accuracy. It serves as a robust foundation for future research and applications in the field of real-time object detection. 
+</p>
+
+---
+
+
+<div align="center">
+  <a href="http://www.shihuahuang.cn">Shihua Huang</a><sup>1</sup>,
+  <a href="https://scholar.google.com/citations?user=tIFWBcQAAAAJ&hl=en">Zhichao Lu</a><sup>2</sup>,
+  <a href="https://vinthony.github.io/academic/">Xiaodong Cun</a><sup>3</sup>,
+  Yongjun Yu<sup>1</sup>,
+  Xiao Zhou<sup>4</sup>, 
+  <a href="https://xishen0220.github.io">Xi Shen</a><sup>1*</sup>
+</div>
+
+  
+<p align="center">
+<i>
+1. Intellindust AI Lab &nbsp; 2. City University of Hong Kong &nbsp; 3. Great Bay University &nbsp; 4. Hefei Normal University
+</i>
+</p>
+
+<p align="center">
+  **📧 Corresponding author:** <a href="mailto:shenxiluc@gmail.com">shenxiluc@gmail.com</a>
+</p>
+
+<p align="center">
+    <a href="https://paperswithcode.com/sota/real-time-object-detection-on-coco?p=deim-detr-with-improved-matching-for-fast">
+    <img alt="sota" src="https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/deim-detr-with-improved-matching-for-fast/real-time-object-detection-on-coco">
+    </a>
+</p>
+
+<p align="center">
+<strong>If you like our work, please give us a ⭐!</strong>
+</p>
+
+
+<p align="center">
+  <img src="./figures/teaser_a.png" alt="Image 1" width="49%">
+  <img src="./figures/teaser_b.png" alt="Image 2" width="49%">
+</p>
+
+</details>
+
+ 
+  
+## 🚀 Updates
+- [x] **\[2025.06.24\]** DEIMv2 is coming soon: our next-gen detection series, along with three ultra-light variants: Pico (1.5M), Femto (0.96M), and Atto (0.49M), all delivering SoTA performance. Atto, in particular, is tailored for mobile devices, achieving 23.8 AP on COCO at 320×320 resolution.
+- [x] **\[2025.03.12\]** The Object365 Pretrained [DEIM-D-FINE-X](https://drive.google.com/file/d/1RMNrHh3bYN0FfT5ZlWhXtQxkG23xb2xj/view?usp=drive_link) model is released, which achieves 59.5% AP after fine-tuning 24 COCO epochs.
+- [x] **\[2025.03.05\]** The Nano DEIM model is released.
+- [x] **\[2025.02.27\]** The DEIM paper is accepted to CVPR 2025. Thanks to all co-authors.
+- [x] **\[2024.12.26\]** A more efficient implementation of Dense O2O, achieving nearly a 30% improvement in loading speed (See [the pull request](https://github.com/ShihuaHuang95/DEIM/pull/13) for more details). Huge thanks to my colleague [Longfei Liu](https://github.com/capsule2077).
+- [x] **\[2024.12.03\]** Release DEIM series. Besides, this repo also supports the re-implmentations of [D-FINE](https://arxiv.org/abs/2410.13842) and [RT-DETR](https://arxiv.org/abs/2407.17140).
+
+## Table of Content
+* [1. Model Zoo](https://github.com/ShihuaHuang95/DEIM?tab=readme-ov-file#1-model-zoo)
+* [2. Quick start](https://github.com/ShihuaHuang95/DEIM?tab=readme-ov-file#2-quick-start)
+* [3. Usage](https://github.com/ShihuaHuang95/DEIM?tab=readme-ov-file#3-usage)
+* [4. Tools](https://github.com/ShihuaHuang95/DEIM?tab=readme-ov-file#4-tools)
+* [5. Citation](https://github.com/ShihuaHuang95/DEIM?tab=readme-ov-file#5-citation)
+* [6. Acknowledgement](https://github.com/ShihuaHuang95/DEIM?tab=readme-ov-file#6-acknowledgement)
+  
+  
+## 1. Model Zoo
+
+### DEIM-D-FINE
+| Model | Dataset | AP<sup>D-FINE</sup> | AP<sup>DEIM</sup> | #Params | Latency | GFLOPs | config | checkpoint
+| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: 
+**N** | COCO | **42.8** | **43.0** | 4M | 2.12ms | 7 | [yml](./configs/deim_dfine/deim_hgnetv2_n_coco.yml) | [ckpt](https://drive.google.com/file/d/1ZPEhiU9nhW4M5jLnYOFwTSLQC1Ugf62e/view?usp=sharing) |
+**S** | COCO | **48.7** | **49.0** | 10M | 3.49ms | 25 | [yml](./configs/deim_dfine/deim_hgnetv2_s_coco.yml) | [ckpt](https://drive.google.com/file/d/1tB8gVJNrfb6dhFvoHJECKOF5VpkthhfC/view?usp=drive_link) |
+**M** | COCO | **52.3** | **52.7** | 19M | 5.62ms | 57 | [yml](./configs/deim_dfine/deim_hgnetv2_m_coco.yml) | [ckpt](https://drive.google.com/file/d/18Lj2a6UN6k_n_UzqnJyiaiLGpDzQQit8/view?usp=drive_link) |
+**L** | COCO | **54.0** | **54.7** | 31M | 8.07ms | 91 | [yml](./configs/deim_dfine/deim_hgnetv2_l_coco.yml) | [ckpt](https://drive.google.com/file/d/1PIRf02XkrA2xAD3wEiKE2FaamZgSGTAr/view?usp=drive_link) | 
+**X** | COCO | **55.8** | **56.5** | 62M | 12.89ms | 202 | [yml](./configs/deim_dfine/deim_hgnetv2_x_coco.yml) | [ckpt](https://drive.google.com/file/d/1dPtbgtGgq1Oa7k_LgH1GXPelg1IVeu0j/view?usp=drive_link) | 
+
+
+### DEIM-RT-DETRv2
+| Model | Dataset | AP<sup>RT-DETRv2</sup> | AP<sup>DEIM</sup> | #Params | Latency | GFLOPs | config | checkpoint
+| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: 
+**S** | COCO | **47.9** | **49.0** | 20M | 4.59ms | 60 | [yml](./configs/deim_rtdetrv2/deim_r18vd_120e_coco.yml) | [ckpt](https://drive.google.com/file/d/153_JKff6EpFgiLKaqkJsoDcLal_0ux_F/view?usp=drive_link) | 
+**M** | COCO | **49.9** | **50.9** | 31M | 6.40ms | 92 | [yml](./configs/deim_rtdetrv2/deim_r34vd_120e_coco.yml) | [ckpt](https://drive.google.com/file/d/1O9RjZF6kdFWGv1Etn1Toml4r-YfdMDMM/view?usp=drive_link) | 
+**M*** | COCO | **51.9** | **53.2** | 33M | 6.90ms | 100 | [yml](./configs/deim_rtdetrv2/deim_r50vd_m_60e_coco.yml) | [ckpt](https://drive.google.com/file/d/10dLuqdBZ6H5ip9BbBiE6S7ZcmHkRbD0E/view?usp=drive_link) | 
+**L** | COCO | **53.4** | **54.3** | 42M | 9.15ms | 136 | [yml](./configs/deim_rtdetrv2/deim_r50vd_60e_coco.yml) | [ckpt](https://drive.google.com/file/d/1mWknAXD5JYknUQ94WCEvPfXz13jcNOTI/view?usp=drive_link) | 
+**X** | COCO | **54.3** | **55.5** | 76M | 13.66ms | 259 | [yml](./configs/deim_rtdetrv2/deim_r101vd_60e_coco.yml) | [ckpt](https://drive.google.com/file/d/1BIevZijOcBO17llTyDX32F_pYppBfnzu/view?usp=drive_link) | 
+
+
+## 2. Quick start
+
+### Setup
+
+```shell
+conda create -n deim python=3.11.9
+conda activate deim
+pip install -r requirements.txt
+```
+
+
+### Data Preparation
+
+<details>
+<summary> COCO2017 Dataset </summary>
+
+1. Download COCO2017 from [OpenDataLab](https://opendatalab.com/OpenDataLab/COCO_2017) or [COCO](https://cocodataset.org/#download).
+1. Modify paths in [coco_detection.yml](./configs/dataset/coco_detection.yml)
+
+    ```yaml
+    train_dataloader:
+        img_folder: /data/COCO2017/train2017/
+        ann_file: /data/COCO2017/annotations/instances_train2017.json
+    val_dataloader:
+        img_folder: /data/COCO2017/val2017/
+        ann_file: /data/COCO2017/annotations/instances_val2017.json
+    ```
+
+</details>
+
+<details>
+<summary>Custom Dataset</summary>
+
+To train on your custom dataset, you need to organize it in the COCO format. Follow the steps below to prepare your dataset:
+
+1. **Set `remap_mscoco_category` to `False`:**
+
+    This prevents the automatic remapping of category IDs to match the MSCOCO categories.
+
+    ```yaml
+    remap_mscoco_category: False
+    ```
+
+2. **Organize Images:**
+
+    Structure your dataset directories as follows:
+
+    ```shell
+    dataset/
+    ├── images/
+    │   ├── train/
+    │   │   ├── image1.jpg
+    │   │   ├── image2.jpg
+    │   │   └── ...
+    │   ├── val/
+    │   │   ├── image1.jpg
+    │   │   ├── image2.jpg
+    │   │   └── ...
+    └── annotations/
+        ├── instances_train.json
+        ├── instances_val.json
+        └── ...
+    ```
+
+    - **`images/train/`**: Contains all training images.
+    - **`images/val/`**: Contains all validation images.
+    - **`annotations/`**: Contains COCO-formatted annotation files.
+
+3. **Convert Annotations to COCO Format:**
+
+    If your annotations are not already in COCO format, you'll need to convert them. You can use the following Python script as a reference or utilize existing tools:
+
+    ```python
+    import json
+
+    def convert_to_coco(input_annotations, output_annotations):
+        # Implement conversion logic here
+        pass
+
+    if __name__ == "__main__":
+        convert_to_coco('path/to/your_annotations.json', 'dataset/annotations/instances_train.json')
+    ```
+
+4. **Update Configuration Files:**
+
+    Modify your [custom_detection.yml](./configs/dataset/custom_detection.yml).
+
+    ```yaml
+    task: detection
+
+    evaluator:
+      type: CocoEvaluator
+      iou_types: ['bbox', ]
+
+    num_classes: 777 # your dataset classes
+    remap_mscoco_category: False
+
+    train_dataloader:
+      type: DataLoader
+      dataset:
+        type: CocoDetection
+        img_folder: /data/yourdataset/train
+        ann_file: /data/yourdataset/train/train.json
+        return_masks: False
+        transforms:
+          type: Compose
+          ops: ~
+      shuffle: True
+      num_workers: 4
+      drop_last: True
+      collate_fn:
+        type: BatchImageCollateFunction
+
+    val_dataloader:
+      type: DataLoader
+      dataset:
+        type: CocoDetection
+        img_folder: /data/yourdataset/val
+        ann_file: /data/yourdataset/val/ann.json
+        return_masks: False
+        transforms:
+          type: Compose
+          ops: ~
+      shuffle: False
+      num_workers: 4
+      drop_last: False
+      collate_fn:
+        type: BatchImageCollateFunction
+    ```
+
+</details>
+
+
+## 3. Usage
+<details open>
+<summary> COCO2017 </summary>
+
+1. Training
+```shell
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/deim_dfine/deim_hgnetv2_${model}_coco.yml --use-amp --seed=0
+```
+
+<!-- <summary>2. Testing </summary> -->
+2. Testing
+```shell
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/deim_dfine/deim_hgnetv2_${model}_coco.yml --test-only -r model.pth
+```
+
+<!-- <summary>3. Tuning </summary> -->
+3. Tuning
+```shell
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/deim_dfine/deim_hgnetv2_${model}_coco.yml --use-amp --seed=0 -t model.pth
+```
+</details>
+
+<details>
+<summary> Customizing Batch Size </summary>
+
+For example, if you want to double the total batch size when training D-FINE-L on COCO2017, here are the steps you should follow:
+
+1. **Modify your [dataloader.yml](./configs/base/dataloader.yml)** to increase the `total_batch_size`:
+
+    ```yaml
+    train_dataloader:
+        total_batch_size: 64  # Previously it was 32, now doubled
+    ```
+
+2. **Modify your [deim_hgnetv2_l_coco.yml](./configs/deim_dfine/deim_hgnetv2_l_coco.yml)**. Here’s how the key parameters should be adjusted:
+
+    ```yaml
+    optimizer:
+    type: AdamW
+    params:
+        -
+        params: '^(?=.*backbone)(?!.*norm|bn).*$'
+        lr: 0.000025  # doubled, linear scaling law
+        -
+        params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$'
+        weight_decay: 0.
+
+    lr: 0.0005  # doubled, linear scaling law
+    betas: [0.9, 0.999]
+    weight_decay: 0.0001  # need a grid search
+
+    ema:  # added EMA settings
+        decay: 0.9998  # adjusted by 1 - (1 - decay) * 2
+        warmups: 500  # halved
+
+    lr_warmup_scheduler:
+        warmup_duration: 250  # halved
+    ```
+
+</details>
+
+
+<details>
+<summary> Customizing Input Size </summary>
+
+If you'd like to train **DEIM** on COCO2017 with an input size of 320x320, follow these steps:
+
+1. **Modify your [dataloader.yml](./configs/base/dataloader.yml)**:
+
+    ```yaml
+
+    train_dataloader:
+    dataset:
+        transforms:
+            ops:
+                - {type: Resize, size: [320, 320], }
+    collate_fn:
+        base_size: 320
+    dataset:
+        transforms:
+            ops:
+                - {type: Resize, size: [320, 320], }
+    ```
+
+2. **Modify your [dfine_hgnetv2.yml](./configs/base/dfine_hgnetv2.yml)**:
+
+    ```yaml
+    eval_spatial_size: [320, 320]
+    ```
+
+</details>
+
+## 4. Tools
+<details>
+<summary> Deployment </summary>
+
+<!-- <summary>4. Export onnx </summary> -->
+1. Setup
+```shell
+pip install onnx onnxsim
+```
+
+2. Export onnx
+```shell
+python tools/deployment/export_onnx.py --check -c configs/deim_dfine/deim_hgnetv2_${model}_coco.yml -r model.pth
+```
+
+3. Export [tensorrt](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html)
+```shell
+trtexec --onnx="model.onnx" --saveEngine="model.engine" --fp16
+```
+
+</details>
+
+<details>
+<summary> Inference (Visualization) </summary>
+
+
+1. Setup
+```shell
+pip install -r tools/inference/requirements.txt
+```
+
+
+<!-- <summary>5. Inference </summary> -->
+2. Inference (onnxruntime / tensorrt / torch)
+
+Inference on images and videos is now supported.
+```shell
+python tools/inference/onnx_inf.py --onnx model.onnx --input image.jpg  # video.mp4
+python tools/inference/trt_inf.py --trt model.engine --input image.jpg
+python tools/inference/torch_inf.py -c configs/deim_dfine/deim_hgnetv2_${model}_coco.yml -r model.pth --input image.jpg --device cuda:0
+```
+</details>
+
+<details>
+<summary> Benchmark </summary>
+
+1. Setup
+```shell
+pip install -r tools/benchmark/requirements.txt
+```
+
+<!-- <summary>6. Benchmark </summary> -->
+2. Model FLOPs, MACs, and Params
+```shell
+python tools/benchmark/get_info.py -c configs/deim_dfine/deim_hgnetv2_${model}_coco.yml
+```
+
+2. TensorRT Latency
+```shell
+python tools/benchmark/trt_benchmark.py --COCO_dir path/to/COCO2017 --engine_dir model.engine
+```
+</details>
+
+<details>
+<summary> Fiftyone Visualization  </summary>
+
+1. Setup
+```shell
+pip install fiftyone
+```
+4. Voxel51 Fiftyone Visualization ([fiftyone](https://github.com/voxel51/fiftyone))
+```shell
+python tools/visualization/fiftyone_vis.py -c configs/deim_dfine/deim_hgnetv2_${model}_coco.yml -r model.pth
+```
+</details>
+
+<details>
+<summary> Others </summary>
+
+1. Auto Resume Training
+```shell
+bash reference/safe_training.sh
+```
+
+2. Converting Model Weights
+```shell
+python reference/convert_weight.py model.pth
+```
+</details>
+
+
+## 5. Citation
+If you use `DEIM` or its methods in your work, please cite the following BibTeX entries:
+<details open>
+<summary> bibtex </summary>
+
+```latex
+@misc{huang2024deim,
+      title={DEIM: DETR with Improved Matching for Fast Convergence},
+      author={Shihua Huang, Zhichao Lu, Xiaodong Cun, Yongjun Yu, Xiao Zhou, and Xi Shen},
+      booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+      year={2025},
+}
+```
+</details>
+
+## 6. Acknowledgement
+Our work is built upon [D-FINE](https://github.com/Peterande/D-FINE) and [RT-DETR](https://github.com/lyuwenyu/RT-DETR).
+
+✨ Feel free to contribute and reach out if you have any questions! ✨

services/training_service/configs/base/dataloader.yml

@@ -0,0 +1,40 @@
+
+train_dataloader:
+  dataset:
+    transforms:
+      ops:
+        - {type: RandomPhotometricDistort, p: 0.5}
+        - {type: RandomZoomOut, fill: 0}
+        - {type: RandomIoUCrop, p: 0.8}
+        - {type: SanitizeBoundingBoxes, min_size: 1}
+        - {type: RandomHorizontalFlip}
+        - {type: RandomVerticalFlip}
+        - {type: Resize, size: [1024, 800], }
+        - {type: SanitizeBoundingBoxes, min_size: 1}
+        - {type: ConvertPILImage, dtype: 'float32', scale: True}
+        - {type: ConvertBoxes, fmt: 'cxcywh', normalize: True}
+      policy:
+        name: stop_epoch
+        epoch: 72 # epoch in [71, ~) stop `ops`
+        ops: ['Mosaic', 'RandomPhotometricDistort', 'RandomZoomOut', 'RandomIoUCrop']
+
+  collate_fn:
+    type: BatchImageCollateFunction
+    base_size: 1024
+    base_size_repeat: 3
+    stop_epoch: 72 # epoch in [72, ~) stop `multiscales`
+
+  shuffle: True
+  total_batch_size: 32 # total batch size equals to 32 (4 * 8)
+  num_workers: 16
+
+
+val_dataloader:
+  dataset:
+    transforms:
+      ops:
+        - {type: Resize, size: [1024, 800], }
+        - {type: ConvertPILImage, dtype: 'float32', scale: True}
+  shuffle: False
+  total_batch_size: 64
+  num_workers: 16

services/training_service/configs/base/deim.yml

@@ -0,0 +1,49 @@
+# Dense O2O
+train_dataloader: 
+  dataset: 
+    transforms:
+      ops:
+        - {type: Mosaic, output_size: 320, rotation_range: 10, translation_range: [0.1, 0.1], scaling_range: [0.5, 1.5],
+           probability: 1.0, fill_value: 0, use_cache: False, max_cached_images: 50, random_pop: True}
+        - {type: RandomPhotometricDistort, p: 0.5}
+        - {type: RandomZoomOut, fill: 0}
+        - {type: RandomIoUCrop, p: 0.8}
+        - {type: SanitizeBoundingBoxes, min_size: 1}
+        - {type: RandomHorizontalFlip}
+        - {type: RandomVerticalFlip}
+        - {type: Resize, size: [1024, 800], }
+        - {type: SanitizeBoundingBoxes, min_size: 1}
+        - {type: ConvertPILImage, dtype: 'float32', scale: True}
+        - {type: ConvertBoxes, fmt: 'cxcywh', normalize: True}
+      policy:
+        epoch: [4, 29, 50]   # list 
+        ops: ['Mosaic', 'RandomPhotometricDistort', 'RandomZoomOut', 'RandomIoUCrop']
+      mosaic_prob: 0.5
+
+  collate_fn:
+    mixup_prob: 0.5
+    mixup_epochs: [4, 29]
+    stop_epoch: 50    # epoch in [72, ~) stop `multiscales`
+
+# Unfreezing BN
+HGNetv2:
+  freeze_at: -1         # 0 default
+  freeze_norm: False    # True default
+
+# Activation
+DFINETransformer:
+  activation: silu
+  mlp_act: silu
+
+## Our LR-Scheduler
+lrsheduler: flatcosine
+lr_gamma: 0.5
+warmup_iter: 2000
+flat_epoch: 29    # 4 + epoch // 2, e.g., 40 = 4 + 72 / 2
+no_aug_epoch: 8
+
+## Our Loss
+DEIMCriterion:
+  weight_dict: {loss_mal: 1, loss_bbox: 5, loss_giou: 2, loss_fgl: 0.15, loss_ddf: 1.5}
+  losses: ['mal', 'boxes', 'local']
+  gamma: 1.5

services/training_service/configs/base/dfine_hgnetv2.yml

@@ -0,0 +1,90 @@
+task: detection
+
+model: DEIM
+criterion: DEIMCriterion
+postprocessor: PostProcessor
+
+use_focal_loss: True
+eval_spatial_size: [640, 640] # h w
+checkpoint_freq: 4    # save freq
+
+DEIM:
+  backbone: HGNetv2
+  encoder: HybridEncoder
+  decoder: DFINETransformer
+
+# Add, default for step lr scheduler 
+lrsheduler: flatcosine
+lr_gamma: 1
+warmup_iter: 500
+flat_epoch: 4000000
+no_aug_epoch: 0
+
+HGNetv2:
+  pretrained: True
+  local_model_dir: ../RT-DETR-main/D-FINE/weight/hgnetv2/
+
+HybridEncoder:
+  in_channels: [512, 1024, 2048]
+  feat_strides: [8, 16, 32]
+
+  # intra
+  hidden_dim: 256
+  use_encoder_idx: [2]
+  num_encoder_layers: 1
+  nhead: 8
+  dim_feedforward: 1024
+  dropout: 0.
+  enc_act: 'gelu'
+
+  # cross
+  expansion: 1.0
+  depth_mult: 1
+  act: 'silu'
+
+
+DFINETransformer:
+  feat_channels: [256, 256, 256]
+  feat_strides: [8, 16, 32]
+  hidden_dim: 256
+  num_levels: 3
+
+  num_layers: 6
+  eval_idx: -1
+  num_queries: 300
+
+  num_denoising: 100
+  label_noise_ratio: 0.5
+  box_noise_scale: 1.0
+
+  # NEW
+  reg_max: 32
+  reg_scale: 4
+
+  # Auxiliary decoder layers dimension scaling
+  # "eg. If num_layers: 6 eval_idx: -4,
+  # then layer 3, 4, 5 are auxiliary decoder layers."
+  layer_scale: 1  # 2
+
+
+  num_points: [3, 6, 3] # [4, 4, 4] [3, 6, 3]
+  cross_attn_method: default # default, discrete
+  query_select_method: default # default, agnostic
+
+
+PostProcessor:
+  num_top_queries: 300
+
+
+DEIMCriterion:
+  weight_dict: {loss_vfl: 1, loss_bbox: 5, loss_giou: 2, loss_fgl: 0.15, loss_ddf: 1.5}
+  losses: ['vfl', 'boxes', 'local']
+  alpha: 0.75
+  gamma: 2.0
+  reg_max: 32
+
+  matcher:
+    type: HungarianMatcher
+    weight_dict: {cost_class: 2, cost_bbox: 5, cost_giou: 2}
+    alpha: 0.25
+    gamma: 2.0

services/training_service/configs/base/optimizer.yml

@@ -0,0 +1,39 @@
+use_amp: True
+use_ema: True
+ema:
+  type: ModelEMA
+  decay: 0.9999
+  warmups: 1000
+  start: 0
+
+epoches: 72
+clip_max_norm: 0.1
+
+# Gradient Accumulation - set to 1 to disable, >1 to enable accumulation
+# Effective batch size = batch_size * gradient_accumulation_steps
+gradient_accumulation_steps: 1
+
+
+optimizer:
+  type: AdamW
+  params:
+    -
+      params: '^(?=.*backbone)(?!.*norm).*$'
+      lr: 0.0000125
+    -
+      params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$'
+      weight_decay: 0.
+
+  lr: 0.00025
+  betas: [0.9, 0.999]
+  weight_decay: 0.000125
+
+
+lr_scheduler:
+  type: MultiStepLR
+  milestones: [500]
+  gamma: 0.1
+
+lr_warmup_scheduler:
+  type: LinearWarmup
+  warmup_duration: 500

services/training_service/configs/base/rt_deim.yml

@@ -0,0 +1,49 @@
+# Dense O2O
+train_dataloader: 
+  dataset: 
+    transforms:
+      ops:
+        - {type: Mosaic, output_size: 320, rotation_range: 10, translation_range: [0.1, 0.1], scaling_range: [0.5, 1.5],
+           probability: 1.0, fill_value: 0, use_cache: False, max_cached_images: 50, random_pop: True}
+        - {type: RandomPhotometricDistort, p: 0.5}
+        - {type: RandomZoomOut, fill: 0}
+        - {type: RandomIoUCrop, p: 0.8}
+        - {type: SanitizeBoundingBoxes, min_size: 1}
+        - {type: RandomHorizontalFlip}
+        - {type: Resize, size: [640, 640], }
+        - {type: SanitizeBoundingBoxes, min_size: 1}
+        - {type: ConvertPILImage, dtype: 'float32', scale: True}
+        - {type: ConvertBoxes, fmt: 'cxcywh', normalize: True}
+      policy:
+        epoch: [4, 29, 50]   # list 
+        ops: ['Mosaic', 'RandomPhotometricDistort', 'RandomZoomOut', 'RandomIoUCrop']
+      mosaic_prob: 0.5
+
+  collate_fn:
+    mixup_prob: 0.5
+    mixup_epochs: [4, 29]
+    stop_epoch: 50    # epoch in [72, ~) stop `multiscales`
+
+# Unfreezing BN
+PResNet:
+  freeze_at: -1     # default 0
+  freeze_norm: False   # default True
+
+# Activation
+RTDETRTransformerv2:
+  query_pos_method: as_reg
+  activation: silu
+  mlp_act: silu
+
+## Our LR-Scheduler
+lrsheduler: flatcosine
+lr_gamma: 0.5
+warmup_iter: 2000
+flat_epoch: 29    # 4 + epoch // 2, e.g., 40 = 4 + 72 / 2
+no_aug_epoch: 8
+
+## Our Loss
+DEIMCriterion:
+  weight_dict: {loss_mal: 1, loss_bbox: 5, loss_giou: 2}
+  losses: ['mal', 'boxes', ]
+  gamma: 1.5

services/training_service/configs/base/rt_optimizer.yml

@@ -0,0 +1,37 @@
+use_amp: True
+use_ema: True 
+ema:
+  type: ModelEMA
+  decay: 0.9999
+  warmups: 2000
+  start: 0
+
+epoches: 72
+clip_max_norm: 0.1
+
+train_dataloader:
+  total_batch_size: 16
+
+optimizer:
+  type: AdamW
+  params: 
+    - 
+      params: '^(?=.*backbone)(?!.*norm).*$'
+      lr: 0.00001
+    - 
+      params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$'
+      weight_decay: 0.
+
+  lr: 0.0001
+  betas: [0.9, 0.999]
+  weight_decay: 0.0001
+  
+lr_scheduler:
+  type: MultiStepLR
+  milestones: [1000]
+  gamma: 0.1
+
+
+lr_warmup_scheduler:
+  type: LinearWarmup
+  warmup_duration: 2000

services/training_service/configs/base/rtdetrv2_r50vd.yml

@@ -0,0 +1,90 @@
+task: detection
+
+model: DEIM
+criterion: DEIMCriterion
+postprocessor: PostProcessor
+
+use_focal_loss: True
+eval_spatial_size: [640, 640] # h w
+checkpoint_freq: 4    # save freq
+
+DEIM: 
+  backbone: PResNet
+  encoder: HybridEncoder
+  decoder: RTDETRTransformerv2
+  
+
+# Add, default for step lr scheduler 
+lrsheduler: flatcosine
+lr_gamma: 1
+warmup_iter: 2000
+flat_epoch: 4000000
+no_aug_epoch: 0
+
+PResNet:
+  depth: 50
+  variant: d
+  freeze_at: 0
+  return_idx: [1, 2, 3]
+  num_stages: 4
+  freeze_norm: True
+  pretrained: True 
+  local_model_dir: ../RT-DETR-main/rtdetrv2_pytorch/INK1k/
+
+
+HybridEncoder:
+  in_channels: [512, 1024, 2048]
+  feat_strides: [8, 16, 32]
+
+  # intra
+  hidden_dim: 256
+  use_encoder_idx: [2]
+  num_encoder_layers: 1
+  nhead: 8
+  dim_feedforward: 1024
+  dropout: 0.
+  enc_act: 'gelu'
+  
+  # cross
+  expansion: 1.0
+  depth_mult: 1
+  act: 'silu'
+  version: rt_detrv2    # pay attention to this
+
+
+RTDETRTransformerv2:
+  feat_channels: [256, 256, 256]
+  feat_strides: [8, 16, 32]
+  hidden_dim: 256
+  num_levels: 3
+
+  num_layers: 6
+  num_queries: 300
+
+  num_denoising: 100
+  label_noise_ratio: 0.5
+  box_noise_scale: 1.0    # 1.0 0.4
+
+  eval_idx: -1
+
+  # NEW, can be chosen 
+  num_points: [4, 4, 4]     # [3,3,3] [2,2,2]
+  cross_attn_method: default  # default, discrete
+  query_select_method: default  # default, agnostic 
+
+
+PostProcessor:
+  num_top_queries: 300
+
+DEIMCriterion:
+  weight_dict: {loss_vfl: 1, loss_bbox: 5, loss_giou: 2,}
+  losses: ['vfl', 'boxes', ]
+  alpha: 0.75
+  gamma: 2.0
+  use_uni_set: False
+
+  matcher:
+    type: HungarianMatcher
+    weight_dict: {cost_class: 2, cost_bbox: 5, cost_giou: 2}
+    alpha: 0.25
+    gamma: 2.0

services/training_service/configs/base/wandb.yml

@@ -0,0 +1,22 @@
+# Weights & Biases Configuration
+wandb:
+  enabled: False  # can be overridden by --use-wandb
+  project: "DEIM-detection"
+  entity: null  # your wandb team/username
+  name: null    # auto-generated if null
+  id: null      # for resuming runs
+  tags: ["deim", "object-detection"]
+  notes: "DEIM training run"
+  group: null   # for grouping related runs
+  job_type: "training"
+  
+  # Logging settings
+  watch_model: True
+  log_gradients: False
+  log_frequency: 10  # log every N steps
+  save_artifacts: True  # save best model as artifact
+  
+  # Advanced settings
+  save_code: True  # save code snapshot
+  anonymous: null  # "allow", "never", "must"
+  mode: "online"   # "online", "offline", "disabled"

services/training_service/configs/dataset/coco_detection.yml

@@ -0,0 +1,41 @@
+task: detection
+
+evaluator:
+  type: CocoEvaluator
+  iou_types: ['bbox', ]
+
+num_classes: 80
+remap_mscoco_category: True
+
+train_dataloader: 
+  type: DataLoader
+  dataset: 
+    type: CocoDetection
+    img_folder: /datassd/COCO/train2017/
+    ann_file: /datassd/COCO/annotations/instances_train2017.json
+    return_masks: False
+    transforms:
+      type: Compose
+      ops: ~
+  shuffle: True
+  num_workers: 4
+  drop_last: True 
+  collate_fn:
+    type: BatchImageCollateFunction
+
+
+val_dataloader:
+  type: DataLoader
+  dataset: 
+    type: CocoDetection
+    img_folder: /datassd/COCO/val2017/
+    ann_file: /datassd/COCO/annotations/instances_val2017.json
+    return_masks: False
+    transforms:
+      type: Compose
+      ops: ~ 
+  shuffle: False
+  num_workers: 4
+  drop_last: False
+  collate_fn:
+    type: BatchImageCollateFunction

services/training_service/configs/dataset/crowdhuman_detection.yml

@@ -0,0 +1,41 @@
+task: detection
+
+evaluator:
+  type: CocoEvaluator
+  iou_types: ['bbox', ]
+
+num_classes: 2 # your dataset classes
+remap_mscoco_category: False
+
+train_dataloader:
+  type: DataLoader
+  dataset:
+    type: CocoDetection
+    img_folder: /datassd/coco/crowd_human_coco/CrowdHuman_train
+    ann_file: /datassd/coco/crowd_human_coco/Chuman-train.json
+    return_masks: False
+    transforms:
+      type: Compose
+      ops: ~
+  shuffle: True
+  num_workers: 4
+  drop_last: True
+  collate_fn:
+    type: BatchImageCollateFunction
+
+
+val_dataloader:
+  type: DataLoader
+  dataset:
+    type: CocoDetection
+    img_folder: /datassd/coco/crowd_human_coco/CrowdHuman_val
+    ann_file: /datassd/coco/crowd_human_coco/Chuman-val.json
+    return_masks: False
+    transforms:
+      type: Compose
+      ops: ~
+  shuffle: False
+  num_workers: 4
+  drop_last: False
+  collate_fn:
+    type: BatchImageCollateFunction

services/training_service/configs/dataset/custom_detection.yml

@@ -0,0 +1,41 @@
+task: detection
+
+evaluator:
+  type: CocoEvaluator
+  iou_types: ['bbox', ]
+
+num_classes: 777 # your dataset classes
+remap_mscoco_category: False
+
+train_dataloader:
+  type: DataLoader
+  dataset:
+    type: CocoDetection
+    img_folder: /data/yourdataset/train
+    ann_file: /data/yourdataset/train/train.json
+    return_masks: False
+    transforms:
+      type: Compose
+      ops: ~
+  shuffle: True
+  num_workers: 4
+  drop_last: True
+  collate_fn:
+    type: BatchImageCollateFunction
+
+
+val_dataloader:
+  type: DataLoader
+  dataset:
+    type: CocoDetection
+    img_folder: /data/yourdataset/val
+    ann_file: /data/yourdataset/val/val.json
+    return_masks: False
+    transforms:
+      type: Compose
+      ops: ~
+  shuffle: False
+  num_workers: 4
+  drop_last: False
+  collate_fn:
+    type: BatchImageCollateFunction

services/training_service/configs/dataset/drone_detection.yml

@@ -0,0 +1,41 @@
+task: detection
+
+evaluator:
+  type: CocoEvaluator
+  iou_types: ['bbox', ]
+
+num_classes: 1
+remap_mscoco_category: False
+
+train_dataloader:
+  type: DataLoader
+  dataset:
+    type: CocoDetection
+    img_folder: /media/fast/drone_train/drone_ds/images
+    ann_file: /media/fast/drone_train/drone_ds/train.json
+    return_masks: False
+    transforms:
+      type: Compose
+      ops: ~
+  shuffle: True
+  num_workers: 8
+  drop_last: True
+  collate_fn:
+    type: BatchImageCollateFunction
+
+
+val_dataloader:
+  type: DataLoader
+  dataset:
+    type: CocoDetection
+    img_folder: /media/fast/drone_train/drone_ds/images
+    ann_file: /media/fast/drone_train/drone_ds/val.json
+    return_masks: False
+    transforms:
+      type: Compose
+      ops: ~
+  shuffle: False
+  num_workers: 4
+  drop_last: False
+  collate_fn:
+    type: BatchImageCollateFunction

services/training_service/configs/dataset/obj365_detection.yml

@@ -0,0 +1,41 @@
+task: detection
+
+evaluator:
+  type: CocoEvaluator
+  iou_types: ['bbox', ]
+
+num_classes: 366
+remap_mscoco_category: False
+
+train_dataloader:
+  type: DataLoader
+  dataset:
+    type: CocoDetection
+    img_folder: /home/Dataset/objects365/train
+    ann_file: /home/Dataset/objects365/train/new_zhiyuan_objv2_train_resized640.json
+    return_masks: False
+    transforms:
+      type: Compose
+      ops: ~
+  shuffle: True
+  num_workers: 4
+  drop_last: True
+  collate_fn:
+    type: BatchImageCollateFunction
+
+
+val_dataloader:
+  type: DataLoader
+  dataset:
+    type: CocoDetection
+    img_folder: /home/Dataset/objects365/val
+    ann_file: /home/Dataset/objects365/val/new_zhiyuan_objv2_val_resized640.json
+    return_masks: False
+    transforms:
+      type: Compose
+      ops: ~
+  shuffle: False
+  num_workers: 4
+  drop_last: False
+  collate_fn:
+    type: BatchImageCollateFunction

services/training_service/configs/dataset/voc_detection.yml

@@ -0,0 +1,40 @@
+task: detection
+
+evaluator:
+  type: CocoEvaluator
+  iou_types: ['bbox', ]
+
+num_classes: 20
+
+train_dataloader:
+  type: DataLoader
+  dataset:
+    type: VOCDetection
+    root: ./dataset/voc/
+    ann_file: trainval.txt
+    label_file: label_list.txt
+    transforms:
+      type: Compose
+      ops: ~
+  shuffle: True
+  num_workers: 4
+  drop_last: True
+  collate_fn:
+    type: BatchImageCollateFunction
+
+
+val_dataloader:
+  type: DataLoader
+  dataset:
+    type: VOCDetection
+    root: ./dataset/voc/
+    ann_file: test.txt
+    label_file: label_list.txt
+    transforms:
+      type: Compose
+      ops: ~
+  shuffle: False
+  num_workers: 4
+  drop_last: False
+  collate_fn:
+    type: BatchImageCollateFunction