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Fix post-processor TypeError, overhaul CI/CD release pipeline (#87)
* Support tuple input in PostArgMax post-processor Co-authored-by: gajarsky.tomas <gajarsky.tomas@gmail.com> * Remove paperswithcode badges from README - Remove non-working paperswithcode badges from all model sections - Clean up README formatting for better readability - Badges were causing display issues and not functioning properly * Fix flake8 linting: remove trailing whitespace - Remove trailing whitespace from blank line in PostArgMax.run() - Resolves W293 blank line contains whitespace error * Bump version to 0.6.1 and update CHANGELOG - Increment patch version from 0.6.0 to 0.6.1 - Add CHANGELOG entry for PostArgMax tuple handling fix - Document README badge removal and linting fixes * Update CHANGELOG.md * Add tuple input handling to PostSigmoidBinary and update changelog date PostSigmoidBinary was the only remaining post-processor not handling tuple model outputs, making it vulnerable to the same TypeError fixed in PostArgMax. Also fixes copy-paste docstring errors in the method. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add unit tests for post-processor tuple input handling Tests verify that all five post-processors (PostArgMax, PostSigmoidBinary, PostEmbedder, PostMultiLabel, PostLabelConfidencePairs) correctly handle both tensor and tuple inputs, preventing regression on issue #84. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Speed up conda CI by switching to libmamba solver The conda workflow was taking 50-80 minutes due to the classic solver struggling with PyTorch dependency resolution. Switch to libmamba solver, upgrade to setup-miniconda@v3 and actions/checkout@v4, and remove the now-unnecessary manual condarc file step. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fix conda CI: bump Python to 3.10 for conda 26.x compat conda 26.1.1 requires Python >=3.10, so 3.9 is no longer resolvable. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fix conda CI: remove defaults channel to avoid plugin conflict conda 26.x has a signature-verification plugin conflict when the defaults channel is used. Since facetorch is on conda-forge, drop defaults entirely and use --override-channels for the install step. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fix conda CI: resolve plugin conflict and fix deprecated params - Add conda-remove-defaults to prevent implicit defaults channel - Fix solver param: solver -> conda-solver (setup-miniconda@v3 API) - Remove conda-content-trust before install to fix PluginError with conflicting signature-verification plugins in conda 26.x Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fix conda CI: switch to miniforge to avoid defaults channel issues Miniconda's defaults channel brings in conda-anaconda-tos and conda-content-trust packages that conflict with conda 26.x and Python 3.10. Miniforge uses only conda-forge by default, avoiding all these issues cleanly. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Overhaul CI/CD: unified release workflow and action updates - Replace pypi-publish.yml and docker-push.yml with a single release.yml triggered by GitHub Release creation (not every push to main) - release.yml validates version file matches the release tag, then publishes to PyPI and pushes Docker images in parallel - Update all workflows to actions/checkout@v4 and actions/setup-python@v5 - Update CHANGELOG to reflect CI/CD changes Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add auto-release workflow for hands-free publishing When a PR that bumps the version file is merged to main, this workflow automatically creates a GitHub Release with the tag and changelog notes. The existing release.yml then picks up the release event and publishes to PyPI and Docker Hub. New release process: 1. Bump version + CHANGELOG in PR 2. Merge to main 3. Everything else is automatic Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Update CHANGELOG release date to April 14, 2026 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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‎.github/workflows/auto-release.yml‎

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name: auto-release
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on:
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push:
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branches: [ "main" ]
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paths:
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- "version"
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jobs:
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create-release:
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runs-on: ubuntu-latest
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permissions:
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contents: write
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steps:
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- uses: actions/checkout@v4
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with:
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fetch-depth: 0
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- name: Read version
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run: |
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VERSION=$(cat ./version | tr -d '[:space:]')
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echo "VERSION=$VERSION" >> $GITHUB_ENV
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echo "TAG=v$VERSION" >> $GITHUB_ENV
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- name: Check if tag already exists
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run: |
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if git rev-parse "${{ env.TAG }}" >/dev/null 2>&1; then
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echo "Tag ${{ env.TAG }} already exists, skipping release."
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echo "SKIP=true" >> $GITHUB_ENV
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else
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echo "SKIP=false" >> $GITHUB_ENV
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fi
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- name: Extract changelog for this version
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if: env.SKIP == 'false'
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run: |
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# Extract the section between "## $VERSION" and the next "## "
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CHANGELOG=$(awk -v ver="${VERSION}" '
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/^## / {
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if (found) exit
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if ($2 == ver) { found=1; next }
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}
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found { print }
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' CHANGELOG.md | sed '/^$/N;/^\n$/d')
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# Write to file for the release body (handles multiline safely)
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echo "$CHANGELOG" > /tmp/release_notes.md
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echo "Changelog extracted:"
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cat /tmp/release_notes.md
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- name: Create GitHub Release
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if: env.SKIP == 'false'
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env:
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GH_TOKEN: ${{ github.token }}
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run: |
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gh release create "${{ env.TAG }}" \
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--title "${{ env.TAG }}" \
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--notes-file /tmp/release_notes.md \
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--target main

‎.github/workflows/build.yml‎

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timeout-minutes: 30
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v2
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- uses: actions/checkout@v4
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- name: Docker compose build and run tests
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run: docker compose -f docker-compose.dev.yml run facetorch-tests

‎.github/workflows/conda-env.yml‎

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conda:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Create condarc file
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run: |
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echo "solver: classic" > condarc
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- name: Set up Miniconda
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uses: conda-incubator/setup-miniconda@v2
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- name: Set up Miniforge
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uses: conda-incubator/setup-miniconda@v3
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with:
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python-version: 3.9
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miniforge-version: latest
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python-version: "3.10"
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environment-file: environment.yml
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channels: conda-forge, defaults
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auto-activate-base: true
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activate-environment: base
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condarc-file: condarc
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auto-activate-base: true
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- name: Install facetorch from conda-forge
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run: |

‎.github/workflows/docker-push.yml‎

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‎.github/workflows/lint.yml‎

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python-version: ["3.9"]
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v3
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies

‎.github/workflows/pypi-publish.yml‎

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‎.github/workflows/python-package.yml‎

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python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v3
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install package

‎.github/workflows/release.yml‎

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name: release
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on:
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release:
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types: [published]
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jobs:
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validate-version:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Validate release tag matches version file
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run: |
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FILE_VERSION=$(cat ./version | tr -d '[:space:]')
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TAG_VERSION="${GITHUB_REF_NAME#v}"
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if [ "$FILE_VERSION" != "$TAG_VERSION" ]; then
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echo "::error::Version mismatch: version file says '$FILE_VERSION' but release tag is '$TAG_VERSION'"
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echo "Please ensure the version file matches the release tag (with or without 'v' prefix)."
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exit 1
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fi
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echo "Version validated: $FILE_VERSION"
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pypi-publish:
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needs: validate-version
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: "3.10"
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- name: Install build dependencies
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run: |
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python -m pip install --upgrade pip
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pip install build twine
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- name: Build package
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run: |
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python -m build
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- name: Publish package to PyPI
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env:
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TWINE_USERNAME: ${{ secrets.TWINE_USERNAME }}
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TWINE_PASSWORD: ${{ secrets.TWINE_PASSWORD }}
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run: |
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twine upload -u $TWINE_USERNAME -p $TWINE_PASSWORD dist/*
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docker-push:
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needs: validate-version
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Set VERSION variable
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run: echo "VERSION=$(cat ./version | tr -d '[:space:]')" >> $GITHUB_ENV
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- name: Login to Docker Hub
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env:
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DOCKER_USERNAME: ${{ secrets.DOCKER_USERNAME }}
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DOCKER_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
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run: docker login -u $DOCKER_USERNAME -p $DOCKER_PASSWORD docker.io
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- name: Build and push facetorch
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run: |
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docker compose build facetorch
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docker tag tomasgajarsky/facetorch:latest tomasgajarsky/facetorch:${{ env.VERSION }}
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docker push tomasgajarsky/facetorch:latest
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docker push tomasgajarsky/facetorch:${{ env.VERSION }}
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- name: Build and push facetorch-gpu
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run: |
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docker compose build facetorch-gpu-no-device
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docker tag tomasgajarsky/facetorch-gpu:latest tomasgajarsky/facetorch-gpu:${{ env.VERSION }}
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docker push tomasgajarsky/facetorch-gpu:latest
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docker push tomasgajarsky/facetorch-gpu:${{ env.VERSION }}

‎CHANGELOG.md‎

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# Change Log
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## 0.6.1
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Released on April 14, 2026.
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### Fixed
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* PostArgMax post-processor to handle tuple inputs (resolves TypeError: argmax(): argument 'input' must be Tensor, not tuple)
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* PostSigmoidBinary post-processor to handle tuple inputs for consistency with other post-processors
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### Changed
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* Replaced pypi-publish and docker-push workflows with unified release workflow triggered by GitHub Release
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* Switched conda CI from miniconda (classic solver) to miniforge (libmamba), reducing run time from ~60 min to ~1 min
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* Updated all GitHub Actions to latest versions (checkout@v4, setup-python@v5, setup-miniconda@v3)
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* Removed non-working paperswithcode badges from README for better readability
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### Added
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* Unit tests for all post-processor tuple input handling
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* Version tag validation in release workflow
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* Auto-release workflow that creates GitHub Releases when version file changes on main
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## 0.6.0
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Released on May 24, 2025.

‎README.md‎

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1. biubug6
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* code: [Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface)
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* paper: [Deng et al. - RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild](https://openaccess.thecvf.com/content_CVPR_2020/html/Deng_RetinaFace_Single-Shot_Multi-Level_Face_Localisation_in_the_Wild_CVPR_2020_paper.html)
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* [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/190500641/face-detection-on-wider-face-hard)](https://paperswithcode.com/sota/face-detection-on-wider-face-hard?p=190500641)
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1. Jung-Jun-Uk
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* code: [UNPG](https://github.com/jung-jun-uk/unpg)
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* paper: [Jung et al. - Unified Negative Pair Generation toward Well-discriminative Feature Space for Face Recognition](https://arxiv.org/abs/2203.11593)
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* [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/unified-negative-pair-generation-toward-well/face-verification-on-ijb-b)](https://paperswithcode.com/sota/face-verification-on-ijb-b?p=unified-negative-pair-generation-toward-well)(FAR=0.01)
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* Note: ```include_tensors``` needs to be True in order to include the model prediction in Prediction.logits
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2. mk-minchul
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* code: [AdaFace](https://github.com/mk-minchul/adaface)
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* paper: [Kim et al. - AdaFace: Quality Adaptive Margin for Face Recognition](https://arxiv.org/abs/2204.00964)
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* [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/adaface-quality-adaptive-margin-for-face/face-verification-on-ijb-b)](https://paperswithcode.com/sota/face-verification-on-ijb-b?p=adaface-quality-adaptive-margin-for-face) <
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* [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/adaface-quality-adaptive-margin-for-face/face-verification-on-ijb-c)](https://paperswithcode.com/sota/face-verification-on-ijb-c?p=adaface-quality-adaptive-margin-for-face) <
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* < badges represent models trained on smaller WebFace 4M dataset
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1. HSE-asavchenko
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* code: [face-emotion-recognition](https://github.com/HSE-asavchenko/face-emotion-recognition)
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* paper: [Savchenko - Facial expression and attributes recognition based on multi-task learning of lightweight neural networks](https://ieeexplore.ieee.org/abstract/document/9582508)
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* B2 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/classifying-emotions-and-engagement-in-online/facial-expression-recognition-on-affectnet)](https://paperswithcode.com/sota/facial-expression-recognition-on-affectnet?p=classifying-emotions-and-engagement-in-online)
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* B0 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/facial-expression-and-attributes-recognition/facial-expression-recognition-on-affectnet)](https://paperswithcode.com/sota/facial-expression-recognition-on-affectnet?p=facial-expression-and-attributes-recognition)
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* B0 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/facial-expression-and-attributes-recognition/facial-expression-recognition-on-acted-facial)](https://paperswithcode.com/sota/facial-expression-recognition-on-acted-facial?p=facial-expression-and-attributes-recognition)
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#### Facial Action Unit Detection (au)
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* code: [ME-GraphAU](https://github.com/CVI-SZU/ME-GraphAU)
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* paper: [Luo et al. - Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition](https://arxiv.org/abs/2205.01782)
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* [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/learning-multi-dimensional-edge-feature-based/facial-action-unit-detection-on-bp4d)](https://paperswithcode.com/sota/facial-action-unit-detection-on-bp4d?p=learning-multi-dimensional-edge-feature-based)
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* ! Does not work with CUDA > 12.0
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#### Facial Valence Arousal (va)
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* code: [SynergyNet](https://github.com/choyingw/SynergyNet)
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* challenge: [Wu et al. - Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry](https://arxiv.org/abs/2110.09772)
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* [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/synergy-between-3dmm-and-3d-landmarks-for/face-alignment-on-aflw)](https://paperswithcode.com/sota/face-alignment-on-aflw?p=synergy-between-3dmm-and-3d-landmarks-for)
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* [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/synergy-between-3dmm-and-3d-landmarks-for/head-pose-estimation-on-aflw2000)](https://paperswithcode.com/sota/head-pose-estimation-on-aflw2000?p=synergy-between-3dmm-and-3d-landmarks-for)
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* [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/synergy-between-3dmm-and-3d-landmarks-for/face-alignment-on-aflw2000-3d)](https://paperswithcode.com/sota/face-alignment-on-aflw2000-3d?p=synergy-between-3dmm-and-3d-landmarks-for)
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* Note: ```include_tensors``` needs to be True in order to include the model prediction in Prediction.logits
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