bump version from 0.27.0rc3 to 0.27.0rc4

initial version of `filter_segments_by_distance`

.github/CODEOWNERS

@@ -0,0 +1,4 @@
+# These owners will be the default owners for everything in
+# the repo. They will be requested for review when someone
+# opens a pull request.
+*   @SkalskiP @soumik12345

.github/dependabot.yml

@@ -4,13 +4,15 @@ updates:
   - package-ecosystem: "github-actions"
     directory: "/"
     schedule:
-      interval: "daily"
+      interval: "weekly"
     commit-message:
       prefix: ⬆️
+    target-branch: "develop"
   # Python
   - package-ecosystem: "pip"
     directory: "/"
     schedule:
-      interval: "daily"
+      interval: "weekly"
     commit-message:
       prefix: ⬆️
+    target-branch: "develop"

.github/workflows/clear-cache.yml

@@ -1,33 +1,42 @@
-
 name: Clear cache
 
 on:
-    schedule:
-      - cron: '0 0 1 * *'
-    workflow_dispatch:
+  schedule:
+    - cron: "0 0 1 * *" # Run at midnight on the first day of every month
+  workflow_dispatch:
 
+# Restrict permissions by default
 permissions:
-  actions: write
+  actions: write # Required for cache management
 
 jobs:
   clear-cache:
+    name: Clear cache
     runs-on: ubuntu-latest
+    timeout-minutes: 10
     steps:
       - name: Clear cache
-        uses: actions/github-script@v7
+        uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
         with:
           script: |
-            console.log("About to clear")
+            console.log("Starting cache cleanup...")
             const caches = await github.rest.actions.getActionsCacheList({
               owner: context.repo.owner,
               repo: context.repo.repo,
             })
+
+            let deletedCount = 0
             for (const cache of caches.data.actions_caches) {
-              console.log(cache)
-              github.rest.actions.deleteActionsCacheById({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                cache_id: cache.id,
-              })
+              console.log(`Deleting cache: ${cache.key} (${cache.size_in_bytes} bytes)`)
+              try {
+                await github.rest.actions.deleteActionsCacheById({
+                  owner: context.repo.owner,
+                  repo: context.repo.repo,
+                  cache_id: cache.id,
+                })
+                deletedCount++
+              } catch (error) {
+                console.error(`Failed to delete cache ${cache.key}: ${error.message}`)
+              }
             }
-            console.log("Clear completed")
+            console.log(`Cache cleanup completed. Deleted ${deletedCount} caches.`)

.github/workflows/combine-dependabot-prs.yml

@@ -0,0 +1,22 @@
+name: Combine Dependabot PRs
+
+on:
+  schedule:
+    - cron: "0 1 * * 3" # Wednesday at 01:00
+  workflow_dispatch: # allows you to manually trigger the workflow
+
+permissions:
+  contents: write
+  pull-requests: write
+  checks: read
+
+jobs:
+  combine-prs:
+    name: Combine
+    runs-on: ubuntu-latest
+    steps:
+      - name: combine-prs
+        id: combine-prs
+        uses: github/combine-prs@2909f404763c3177a456e052bdb7f2e85d3a7cb3 # v5.2.0
+        with:
+          labels: combined-pr

.github/workflows/notebook-bot.yml

@@ -1,64 +0,0 @@
-name: Notebook Check Pull Request
-
-on:
-  pull_request_target:
-    types: [opened, reopened]
-
-permissions:
-  contents: read
-
-jobs:
-  comment-welcome:
-    permissions:
-      contents: read
-      pull-requests: write
-
-    runs-on: ubuntu-latest
-    steps:
-    - name: Fetch pull request branch
-      uses: actions/checkout@v4
-      with:
-        repository: ${{ github.event.pull_request.head.repo.full_name }}
-        ref: ${{ github.event.pull_request.head.sha }}
-    - name: Fetch base develop branch
-      run: git fetch -u "$GITHUB_SERVER_URL/$GITHUB_REPOSITORY" develop:develop
-    - name: Create message
-      env:
-        HEAD_REPOSITORY: ${{ github.event.pull_request.head.repo.full_name }}
-        HEAD_REF: ${{ github.event.pull_request.head.ref }}
-        PR_NUM: ${{ github.event.pull_request.number }}
-      run: |
-        # Preview links and tool usage only needed for notebook changes.
-        readarray -t changed_notebooks < <(git diff --name-only develop | grep '\.ipynb$' || true)
-        if [[ ${#changed_notebooks[@]} == 0 ]]; then
-          echo "No notebooks modified in this pull request."
-        else
-          msg="<h4>Preview</h4>\n"
-          msg+="Preview and run these notebook edits with Google Colab:\n<ul>\n"
-          # Link to PR branch in user's fork that is always current.
-          for fp in "${changed_notebooks[@]}"; do
-            gh_path="${HEAD_REPOSITORY}/blob/${HEAD_REF}/${fp}"
-            colab_url="https://colab.research.google.com/github/${gh_path}"
-            msg+="<li><a href='${colab_url}'>${fp}</a></li>\n"
-          done
-          msg+="</ul>\n"
-
-          reviewnb_url="https://app.reviewnb.com/${GITHUB_REPOSITORY}/pull/${PR_NUM}/files/"
-          msg+="Rendered <a href='${reviewnb_url}'>notebook diffs</a> available on ReviewNB.com.\n"
-
-          msg+="If commits are added to the pull request, synchronize your local branch: <code>git pull origin $HEAD_REF</code>\n"
-        fi
-        echo "MESSAGE=$msg" >> $GITHUB_ENV
-    - name: Post comment
-      env:
-        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        ISSUE_URL: ${{ github.event.pull_request.issue_url }}
-      run: |
-        # Env var defined in previous step. Escape string for JSON.
-        body="$(echo -n -e $MESSAGE | python3 -c 'import json,sys; print(json.dumps(sys.stdin.read()))')"
-        # Add comment to pull request.
-        curl -X POST \
-          -H "Accept: application/vnd.github.v3+json" \
-          -H "Authorization: token $GITHUB_TOKEN" \
-          "${ISSUE_URL}/comments" \
-          --data "{\"body\": $body}"

.github/workflows/publish-dev-docs.yml

@@ -1,53 +0,0 @@
-name: Docs WorkFlow - Develop Tag 📚
-
-on:
-  push:
-    branches:
-      - develop
-  workflow_dispatch:
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event_name == 'push' && github.ref}}
-  cancel-in-progress: true
-
-permissions:
-  contents: write
-  pages: write
-  pull-requests: write
-
-
-jobs:
-  deploy:
-    runs-on: ubuntu-latest
-    steps:
-      - name: 🔄 Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-      - name: 🐍 Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.10'
-      - name: 📦 Install mkdocs-material
-        run: pip install "mkdocs-material"
-      - name: 📦 Install mkdocstrings[python]
-        run: pip install "mkdocstrings[python]"
-      - name: 📦 Install mkdocs-material[imaging]
-        run: pip install "mkdocs-material[imaging]"
-      - name: 📦 Install mike
-        run: pip install "mike"
-      - name: 📦 Install mkdocs-git-revision-date-localized-plugin
-        run: pip install "mkdocs-git-revision-date-localized-plugin"
-      - name: 📦 Install JupyterLab
-        run: pip install jupyterlab
-      - name: 📦 Install mkdocs-jupyter
-        run: pip install mkdocs-jupyter
-      - name: 📦 Install mkdocs-git-committers-plugin-2
-        run: pip install mkdocs-git-committers-plugin-2
-      - name: ⚙️ Configure git for github-actions
-        run: |
-          git config --global user.name "github-actions[bot]"
-          git config --global user.email "41898282+github-actions[bot]@users.noreply.github.com"
-      - name: 🚀 Deploy MkDoc-Material with mike
-        run: |
-          MKDOCS_GIT_COMMITTERS_APIKEY=${{ secrets.GITHUB_TOKEN }} mike deploy --push develop

.github/workflows/publish-docs.yml

@@ -0,0 +1,72 @@
+name: Build and Publish Docs
+
+on:
+  push:
+    branches:
+      - develop
+  workflow_dispatch:
+  release:
+    types: [published]
+
+# Ensure only one concurrent deployment
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event_name == 'push' && github.ref}}
+  cancel-in-progress: true
+
+# Restrict permissions by default
+permissions:
+  contents: write # Required for committing to gh-pages
+  pages: write # Required for deploying to Pages
+  pull-requests: write # Required for PR comments
+
+jobs:
+  deploy:
+    name: Publish Docs
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+    strategy:
+      matrix:
+        python-version: ["3.10"]
+    steps:
+      - name: 📥 Checkout the repository
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+          fetch-depth: 0
+
+      - name: 🐍 Install uv and set Python ${{ matrix.python-version }}
+        uses: astral-sh/setup-uv@e92bafb6253dcd438e0484186d7669ea7a8ca1cc # v6.4.3
+        with:
+          python-version: ${{ matrix.python-version }}
+          activate-environment: true
+
+
+      - name: 🔑 Create GitHub App token (mkdocs)
+        id: mkdocs_token
+        uses: actions/create-github-app-token@df432ceedc7162793a195dd1713ff69aefc7379e # v2.0.6
+        with:
+          app-id: ${{ secrets.MKDOCS_APP_ID }}
+          private-key: ${{ secrets.MKDOCS_PEM }}
+          owner: roboflow
+          repositories: mkdocs-material-insiders
+
+      - name: 🏗️ Install dependencies
+        run: |
+          uv pip install -r pyproject.toml --group docs
+          # Install mkdocs-material-insiders using the GitHub App token
+          uv pip install "git+https://roboflow:${{ steps.mkdocs_token.outputs.token }}@github.com/roboflow/mkdocs-material-insiders.git@9.5.49-insiders-4.53.14#egg=mkdocs-material[imaging]"
+
+      - name: ⚙️ Configure git for github-actions
+        run: |
+          git config --global user.name "github-actions[bot]"
+          git config --global user.email "41898282+github-actions[bot]@users.noreply.github.com"
+
+      - name: 🚀 Deploy Development Docs
+        if: (github.event_name == 'push' && github.ref == 'refs/heads/develop') || github.event_name == 'workflow_dispatch'
+        run: |
+          MKDOCS_GIT_COMMITTERS_APIKEY=${{ secrets.GITHUB_TOKEN }} uv run mike deploy --push develop
+
+      - name: 🚀 Deploy Release Docs
+        if: github.event_name == 'release' && github.event.action == 'published'
+        run: |
+          latest_tag=$(git describe --tags `git rev-list --tags --max-count=1`)
+          MKDOCS_GIT_COMMITTERS_APIKEY=${{ secrets.GITHUB_TOKEN }} uv run mike deploy --push --update-aliases $latest_tag latest

.github/workflows/publish-pre-release.yml

@@ -0,0 +1,47 @@
+name: Publish Supervision Pre-Releases to PyPI
+
+on:
+  push:
+    tags:
+      - "[0-9]+.[0-9]+[0-9]+.[0-9]+a[0-9]"
+      - "[0-9]+.[0-9]+[0-9]+.[0-9]+b[0-9]"
+      - "[0-9]+.[0-9]+[0-9]+.[0-9]+rc[0-9]"
+  workflow_dispatch:
+
+permissions: {} # Explicitly remove all permissions by default
+
+jobs:
+  publish-pre-release:
+    name: Publish Pre-release Package
+    runs-on: ubuntu-latest
+    environment:
+      name: test
+      url: https://pypi.org/project/supervision/
+    timeout-minutes: 10
+    permissions:
+      id-token: write # Required for PyPI publishing
+      contents: read # Required for checkout
+    strategy:
+      matrix:
+        python-version: ["3.10"]
+    steps:
+      - name: 📥 Checkout the repository
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+
+      - name: 🐍 Install uv and set Python version ${{ matrix.python-version }}
+        uses: astral-sh/setup-uv@e92bafb6253dcd438e0484186d7669ea7a8ca1cc # v6.4.3
+        with:
+          python-version: ${{ matrix.python-version }}
+          activate-environment: true
+
+
+      - name: 🏗️ Build source and wheel distributions
+        run: |
+          uv pip install -r pyproject.toml --group build
+          uv build
+          uv run twine check --strict dist/*
+
+      - name: 🚀 Publish to PyPi
+        uses: pypa/gh-action-pypi-publish@76f52bc884231f62b9a034ebfe128415bbaabdfc # v1.12.4
+        with:
+          attestations: true

.github/workflows/publish-release-docs.yml

@@ -1,55 +0,0 @@
-name: Supervision Release Documentation Workflow 📚
-on:
-  workflow_dispatch:
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event_name == 'push' && github.ref}}
-  cancel-in-progress: true
-
-permissions:
-  contents: write
-  pages: write
-  pull-requests: write
-
-
-jobs:
-  doc-build-deploy:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: ["3.10"]
-    steps:
-      - name: 🛎️ Checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.head_ref }}
-
-      - name: 🐍 Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.10'
-      - name: 📦 Install mkdocs-material
-        run: pip install "mkdocs-material"
-      - name: 📦 Install mkdocstrings[python]
-        run: pip install "mkdocstrings[python]"
-      - name: 📦 Install mkdocs-material[imaging]
-        run: pip install "mkdocs-material[imaging]"
-      - name: 📦 Install mike
-        run: pip install "mike"
-      - name: 📦 Install mkdocs-git-revision-date-localized-plugin
-        run: pip install "mkdocs-git-revision-date-localized-plugin"
-      - name: 📦 Install JupyterLab
-        run: pip install jupyterlab
-      - name: 📦 Install mkdocs-jupyter
-        run: pip install mkdocs-jupyter
-      - name: 📦 Install mkdocs-git-committers-plugin-2
-        run: pip install mkdocs-git-committers-plugin-2
-      - name: ⚙️ Configure git for github-actions 👷
-        run: |
-          git config --global user.name "github-actions[bot]"
-          git config --global user.email "41898282+github-actions[bot]@users.noreply.github.com"
-      - name: 🚀 Deploy MkDoc-Material 📚
-        run: |
-            latest_tag=$(git describe --tags `git rev-list --tags --max-count=1`)
-            MKDOCS_GIT_COMMITTERS_APIKEY=${{ secrets.GITHUB_TOKEN }} mike deploy --push --update-aliases $latest_tag latest

.github/workflows/publish-release.yml

@@ -0,0 +1,45 @@
+name: Publish Supervision Releases to PyPI
+
+on:
+  push:
+    tags:
+      - "[0-9]+.[0-9]+[0-9]+.[0-9]"
+  workflow_dispatch:
+
+permissions: {} # Explicitly remove all permissions by default
+
+jobs:
+  publish-release:
+    name: Publish Release Package
+    runs-on: ubuntu-latest
+    environment:
+      name: release
+      url: https://pypi.org/project/supervision/
+    timeout-minutes: 10
+    permissions:
+      id-token: write # Required for PyPI publishing
+      contents: read # Required for checkout
+    strategy:
+      matrix:
+        python-version: ["3.10"]
+    steps:
+      - name: 📥 Checkout the repository
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+
+      - name: 🐍 Install uv and set Python version ${{ matrix.python-version }}
+        uses: astral-sh/setup-uv@e92bafb6253dcd438e0484186d7669ea7a8ca1cc # v6.4.3
+        with:
+          python-version: ${{ matrix.python-version }}
+          activate-environment: true
+
+
+      - name: 🏗️ Build source and wheel distributions
+        run: |
+          uv pip install -r pyproject.toml --group build
+          uv build
+          uv run twine check --strict dist/*
+
+      - name: 🚀 Publish to PyPi
+        uses: pypa/gh-action-pypi-publish@76f52bc884231f62b9a034ebfe128415bbaabdfc # v1.12.4
+        with:
+          attestations: true

.github/workflows/publish-test.yml

@@ -1,42 +0,0 @@
-name: Publish Supervision Pre-Releases to PyPI and TestPyPI
-on:
-  push:
-    tags:
-      - '[0-9]+.[0-9]+[0-9]+.[0-9]+a[0-9]'
-      - '[0-9]+.[0-9]+[0-9]+.[0-9]+b[0-9]'
-      - '[0-9]+.[0-9]+[0-9]+.[0-9]+rc[0-9]'
-
-  workflow_dispatch:
-
-jobs:
-  build-and-publish-pre-release-pypi:
-    name: Build and publish to PyPI
-    runs-on: ubuntu-latest
-    permissions:
-      id-token: write
-    strategy:
-      matrix:
-        python-version: ["3.10"]
-    steps:
-      - name: 🛎️ Checkout
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ github.head_ref }}
-      - name: 🐍 Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - name: 🏗️ Build source and wheel distributions
-        run: |
-          python -m pip install --upgrade build twine
-          python -m build
-          twine check --strict dist/*
-
-      - name: 🚀 Publish to PyPi
-        uses: pypa/gh-action-pypi-publish@release/v1.10
-
-      - name: 🚀 Publish to Test-PyPi
-        uses: pypa/gh-action-pypi-publish@release/v1.10
-        with:
-          repository-url: https://test.pypi.org/legacy/

.github/workflows/publish-testpypi.yml

@@ -0,0 +1,43 @@
+name: Publish Supervision Releases to TestPyPI
+
+on:
+  workflow_dispatch:
+
+permissions: {} # Explicitly remove all permissions by default
+
+jobs:
+  publish-testpypi:
+    name: Publish Release Package
+    runs-on: ubuntu-latest
+    environment:
+      name: release
+      url: https://pypi.org/project/supervision/
+    timeout-minutes: 10
+    permissions:
+      id-token: write # Required for PyPI publishing
+      contents: read # Required for checkout
+    strategy:
+      matrix:
+        python-version: ["3.10"]
+    steps:
+      - name: 📥 Checkout the repository
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+
+      - name: 🐍 Install uv and set Python version ${{ matrix.python-version }}
+        uses: astral-sh/setup-uv@e92bafb6253dcd438e0484186d7669ea7a8ca1cc # v6.4.3
+        with:
+          python-version: ${{ matrix.python-version }}
+          activate-environment: true
+
+
+      - name: 🏗️ Build source and wheel distributions
+        run: |
+          uv pip install -r pyproject.toml --group build
+          uv build
+          uv run twine check --strict dist/*
+
+      - name: 🚀 Publish to Test-PyPi
+        uses: pypa/gh-action-pypi-publish@76f52bc884231f62b9a034ebfe128415bbaabdfc # v1.12.4
+        with:
+          repository-url: https://test.pypi.org/legacy/
+          attestations: true

.github/workflows/publish.yml

@@ -1,39 +0,0 @@
-name: Publish Supervision Releases to PyPI and TestPyPI
-on:
-  push:
-    tags:
-      - '[0-9]+.[0-9]+[0-9]+.[0-9]'
-
-  workflow_dispatch:
-
-jobs:
-  build-and-publish-pre-release:
-    runs-on: ubuntu-latest
-    permissions:
-      id-token: write
-    strategy:
-      matrix:
-        python-version: ["3.10"]
-    steps:
-      - name: 🛎️ Checkout
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ github.head_ref }}
-      - name: 🐍 Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - name:  🏗️ Build source and wheel distributions
-        run: |
-          python -m pip install --upgrade build twine
-          python -m build
-          twine check --strict dist/*
-
-      - name: 🚀 Publish to PyPi
-        uses: pypa/gh-action-pypi-publish@release/v1.10
-
-      - name: 🚀 Publish to Test-PyPi
-        uses: pypa/gh-action-pypi-publish@release/v1.10
-        with:
-          repository-url: https://test.pypi.org/legacy/

.github/workflows/test-doc.yml

@@ -4,18 +4,34 @@ on:
   pull_request:
     branches: [main, develop]
 
+# Restrict permissions by default
+permissions:
+  contents: read # Required for checkout
+  checks: write # Required for test reporting
+
 jobs:
   docs-build-test:
+    name: Test docs build
     runs-on: ubuntu-latest
+    timeout-minutes: 10
+    strategy:
+      matrix:
+        python-version: ["3.10"]
     steps:
-      - name: 🔄 Checkout code
-        uses: actions/checkout@v4
-      - name: 🐍 Set up Python
-        uses: actions/setup-python@v5
+      - name: 📥 Checkout the repository
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
         with:
-          python-version: '3.10'
-      - name:  🏗️ Install dependencies and Test Docs Build
+          fetch-depth: 0
+
+      - name: 🐍 Install uv and set Python ${{ matrix.python-version }}
+        uses: astral-sh/setup-uv@e92bafb6253dcd438e0484186d7669ea7a8ca1cc # v6.4.3
+        with:
+          python-version: ${{ matrix.python-version }}
+          activate-environment: true
+
+      - name: 🏗️ Install dependencies
         run: |
-          python -m pip install --upgrade pip
-          pip install "mkdocs-material" "mkdocstrings[python]" "mkdocs-material[imaging]" mike "mkdocs-git-revision-date-localized-plugin" jupyterlab mkdocs-jupyter mkdocs-git-committers-plugin-2
-          mkdocs build --verbose
+          uv pip install -r pyproject.toml --group docs
+
+      - name: 🧪 Test Docs Build
+        run: uv run mkdocs build --verbose

.github/workflows/test-min.yml

@@ -1,58 +0,0 @@
-name: Python 3.8 - Min Dep Test WorkFlow
-
-on:
-  pull_request:
-    branches: [main, develop]
-
-jobs:
-  build-min-dep-test:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: ["3.8"]
-    steps:
-      - name: 🛎️ Checkout
-        uses: actions/checkout@v4
-      - name: 🐍 Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
-        # id based on python version
-        id: python-setup
-        with:
-          python-version: ${{ matrix.python-version }}
-          check-latest: true
-
-      - name: 📦 Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install \
-              attrs==23.1.0 \
-              certifi==2023.7.22 \
-              charset-normalizer==2.0.12 \
-              cycler==0.12.1 \
-              exceptiongroup==1.1.3 \
-              fonttools==4.43.1 \
-              idna==3.4 \
-              iniconfig==2.0.0 \
-              kiwisolver==1.4.5 \
-              matplotlib==3.5.0 \
-              numpy==1.21.2 \
-              opencv-python==4.5.5.64 \
-              Pillow==10.1.0 \
-              packaging==23.2 \
-              pluggy==1.3.0 \
-              pyparsing==3.1.1 \
-              pytest==7.2.0 \
-              python-dateutil==2.8.2 \
-              PyYAML==5.3 \
-              requests==2.26.0 \
-              scipy==1.10.0 \
-              setuptools-scm==8.0.4 \
-              six==1.16.0 \
-              tomli==2.0.1 \
-              tqdm==4.62.3 \
-              typing_extensions==4.8.0 \
-              urllib3==1.26.18 \
-              defusedxml==0.7.1
-
-      - name: 🧪 Test
-        run: "python -m pytest ./test"

.github/workflows/test.yml

@@ -1,29 +0,0 @@
-name: Test WorkFlow
-
-on:
-  pull_request:
-    branches: [main, develop]
-
-jobs:
-  build-dev-test:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
-    steps:
-      - name: 🛎️ Checkout
-        uses: actions/checkout@v4
-      - name: 🐍 Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python-version }}
-          check-latest: true
-
-      - name: 📦 Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install .
-          pip install pytest
-
-      - name: 🧪 Test
-        run: "python -m pytest ./test"

.github/workflows/uv-test.yml

@@ -0,0 +1,35 @@
+name: 🔧 Pytest/Test Workflow
+
+on:
+  pull_request:
+    branches: [main, develop]
+
+jobs:
+  run-tests:
+    name: Import Test and Pytest Run
+    timeout-minutes: 10
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, windows-latest, macos-latest]
+        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+    runs-on: ${{ matrix.os }}
+    steps:
+      - name: 📥 Checkout the repository
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+
+      - name: 🐍 Install uv and set Python version ${{ matrix.python-version }}
+        uses: astral-sh/setup-uv@e92bafb6253dcd438e0484186d7669ea7a8ca1cc # v6.4.3
+        with:
+          python-version: ${{ matrix.python-version }}
+          activate-environment: true
+
+
+      - name: 🚀 Install Packages
+        run: uv pip install -r pyproject.toml --group dev --group docs --extra metrics
+
+      - name: 🧪 Run the Import test
+        run: uv run python -c "import supervision; from supervision import assets; from supervision import metrics; print(supervision.__version__)"
+
+      - name: 🧪 Run the Test
+        run: uv run pytest

.github/workflows/welcome.yml

@@ -1,18 +0,0 @@
-name: Welcome WorkFlow
-
-on:
-  issues:
-    types: [opened]
-  pull_request_target:
-    types: [opened]
-
-jobs:
-  build:
-    name: 👋 Welcome
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/first-interaction@v1.3.0
-        with:
-          repo-token: ${{ secrets.GITHUB_TOKEN }}
-          issue-message: "Hello there, thank you for opening an Issue ! 🙏🏻 The team was notified and they will get back to you asap."
-          pr-message:  "Hello there, thank you for opening an PR ! 🙏🏻 The team was notified and they will get back to you asap."

.pre-commit-config.yaml

@@ -7,7 +7,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v5.0.0
     hooks:
       - id: trailing-whitespace
         exclude: test/.*\.py
@@ -25,14 +25,14 @@ repos:
       - id: mixed-line-ending
 
   -   repo: https://github.com/PyCQA/bandit
-      rev: '1.7.10'
+      rev: '1.8.6'
       hooks:
       -   id: bandit
           args: ["-c", "pyproject.toml"]
           additional_dependencies: ["bandit[toml]"]
 
   -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.6.8
+      rev: v0.12.5
       hooks:
       -   id: ruff
           args: [--fix, --exit-non-zero-on-fix]
@@ -48,8 +48,16 @@ repos:
   #       args: ["--number"]
 
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.3.0
+    rev: v2.4.1
     hooks:
       - id: codespell
         additional_dependencies:
           - tomli
+
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v3.20.0
+    hooks:
+    -   id: pyupgrade
+        args: ["--py310-plus"]
+        additional_dependencies:
+          - tomli

CONTRIBUTING.md

@@ -128,15 +128,26 @@ PRs must pass all tests and linting requirements before they can be merged.
 
 Before starting your work on the project, set up your development environment:
 
-1. Clone your fork of the project:
+1. Clone your fork of the project (recommended to use shallow clone of develop branch):
+
+    **Option A: Recommended for most contributors (shallow clone of develop branch):**
+
+    ```bash
+    git clone --depth 1 -b develop https://github.com/YOUR_USERNAME/supervision.git
+    cd supervision
+    ```
+
+    Replace `YOUR_USERNAME` with your GitHub username.
+
+    > Note: Using `--depth 1` creates a shallow clone with minimal history and `-b develop` ensures you start with the development branch. This significantly reduces download size while providing everything needed to contribute.
+
+    **Option B: Full repository clone (if you need complete history):**
 
     ```bash
     git clone https://github.com/YOUR_USERNAME/supervision.git
     cd supervision
     ```
 
-    Replace `YOUR_USERNAME` with your GitHub username.
-
 2. Create and activate a virtual environment:
 
     ```bash
@@ -144,31 +155,20 @@ Before starting your work on the project, set up your development environment:
     source .venv/bin/activate
     ```
 
-3. Install Poetry:
+3. Install `uv`:
 
-    Using pip:
-
-    ```bash
-    pip install -U pip setuptools
-    pip install poetry
-    ```
-
-    Or using pipx (recommended for global installation):
-
-    ```bash
-    pipx install poetry
-    ```
+    Follow the instructions on the [uv installation page](https://docs.astral.sh/uv/getting-started/installation/).
 
 4. Install project dependencies:
 
     ```bash
-    poetry install
+    uv pip install -r pyproject.toml --extra dev --extra docs --extra metrics
     ```
 
 5. Run pytest to verify the setup:
 
     ```bash
-    poetry run pytest
+    uv run pytest
     ```
 
 ## 🎨 Code Style and Quality
@@ -181,7 +181,7 @@ Furthermore, we have integrated a pre-commit GitHub Action into our workflow. Th
 
 To run the pre-commit tool, follow these steps:
 
-1. Install pre-commit by running the following command: `poetry install --with dev`. It will not only install pre-commit but also install all the deps and dev-deps of project
+1. Install pre-commit by running the following command: `uv pip install -r pyproject.toml --extra dev`. It will not only install pre-commit but also install all the deps and dev-deps of project
 
 2. Once pre-commit is installed, navigate to the project's root directory.
 
@@ -203,7 +203,7 @@ So far, **there is no type checking with mypy**. See [issue](https://github.com/
 
 The `supervision` documentation is stored in a folder called `docs`. The project documentation is built using `mkdocs`.
 
-To run the documentation, install the project requirements with `poetry install --with dev`. Then, run `mkdocs serve` to start the documentation server.
+To run the documentation, install the project requirements with `uv pip install -r pyproject.toml --extra dev --extra docs`. Then, run `mkdocs serve` to start the documentation server.
 
 You can learn more about mkdocs on the [mkdocs website](https://www.mkdocs.org/).
 

README.md

@@ -21,7 +21,7 @@
 [![python-version](https://img.shields.io/pypi/pyversions/supervision)](https://badge.fury.io/py/supervision)
 [![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb)
 [![gradio](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Roboflow/Annotators)
-[![discord](https://img.shields.io/discord/1159501506232451173)](https://discord.gg/GbfgXGJ8Bk)
+[![discord](https://img.shields.io/discord/1159501506232451173?logo=discord&label=discord&labelColor=fff&color=5865f2&link=https%3A%2F%2Fdiscord.gg%2FGbfgXGJ8Bk)](https://discord.gg/GbfgXGJ8Bk)
 [![built-with-material-for-mkdocs](https://img.shields.io/badge/Material_for_MkDocs-526CFE?logo=MaterialForMkDocs&logoColor=white)](https://squidfunk.github.io/mkdocs-material/)
 
   <div align="center">
@@ -34,12 +34,10 @@
 
 **We write your reusable computer vision tools.** Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us! 🤝
 
-[![supervision-hackfest](https://media.roboflow.com/supervision/supervision-hacktoberfest-banner-2024.png)](https://github.com/roboflow/supervision/issues?q=is%3Aissue+is%3Aopen+label%3Ahacktoberfest)
-
 ## 💻 install
 
 Pip install the supervision package in a
-[**Python>=3.8**](https://www.python.org/) environment.
+[**Python>=3.9**](https://www.python.org/) environment.
 
 ```bash
 pip install supervision
@@ -137,88 +135,88 @@ for path, image, annotation in ds:
 
 - load
 
-    ```python
-    dataset = sv.DetectionDataset.from_yolo(
-        images_directory_path=...,
-        annotations_directory_path=...,
-        data_yaml_path=...
-    )
+  ```python
+  dataset = sv.DetectionDataset.from_yolo(
+      images_directory_path=...,
+      annotations_directory_path=...,
+      data_yaml_path=...
+  )
 
-    dataset = sv.DetectionDataset.from_pascal_voc(
-        images_directory_path=...,
-        annotations_directory_path=...
-    )
+  dataset = sv.DetectionDataset.from_pascal_voc(
+      images_directory_path=...,
+      annotations_directory_path=...
+  )
 
-    dataset = sv.DetectionDataset.from_coco(
-        images_directory_path=...,
-        annotations_path=...
-    )
-    ```
+  dataset = sv.DetectionDataset.from_coco(
+      images_directory_path=...,
+      annotations_path=...
+  )
+  ```
 
 - split
 
-    ```python
-    train_dataset, test_dataset = dataset.split(split_ratio=0.7)
-    test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)
+  ```python
+  train_dataset, test_dataset = dataset.split(split_ratio=0.7)
+  test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)
 
-    len(train_dataset), len(test_dataset), len(valid_dataset)
-    # (700, 150, 150)
-    ```
+  len(train_dataset), len(test_dataset), len(valid_dataset)
+  # (700, 150, 150)
+  ```
 
 - merge
 
-    ```python
-    ds_1 = sv.DetectionDataset(...)
-    len(ds_1)
-    # 100
-    ds_1.classes
-    # ['dog', 'person']
+  ```python
+  ds_1 = sv.DetectionDataset(...)
+  len(ds_1)
+  # 100
+  ds_1.classes
+  # ['dog', 'person']
 
-    ds_2 = sv.DetectionDataset(...)
-    len(ds_2)
-    # 200
-    ds_2.classes
-    # ['cat']
+  ds_2 = sv.DetectionDataset(...)
+  len(ds_2)
+  # 200
+  ds_2.classes
+  # ['cat']
 
-    ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
-    len(ds_merged)
-    # 300
-    ds_merged.classes
-    # ['cat', 'dog', 'person']
-    ```
+  ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
+  len(ds_merged)
+  # 300
+  ds_merged.classes
+  # ['cat', 'dog', 'person']
+  ```
 
 - save
 
-    ```python
-    dataset.as_yolo(
-        images_directory_path=...,
-        annotations_directory_path=...,
-        data_yaml_path=...
-    )
+  ```python
+  dataset.as_yolo(
+      images_directory_path=...,
+      annotations_directory_path=...,
+      data_yaml_path=...
+  )
 
-    dataset.as_pascal_voc(
-        images_directory_path=...,
-        annotations_directory_path=...
-    )
+  dataset.as_pascal_voc(
+      images_directory_path=...,
+      annotations_directory_path=...
+  )
 
-    dataset.as_coco(
-        images_directory_path=...,
-        annotations_path=...
-    )
-    ```
+  dataset.as_coco(
+      images_directory_path=...,
+      annotations_path=...
+  )
+  ```
 
 - convert
 
-    ```python
-    sv.DetectionDataset.from_yolo(
-        images_directory_path=...,
-        annotations_directory_path=...,
-        data_yaml_path=...
-    ).as_pascal_voc(
-        images_directory_path=...,
-        annotations_directory_path=...
-    )
-    ```
+  ```python
+  sv.DetectionDataset.from_yolo(
+      images_directory_path=...,
+      annotations_directory_path=...,
+      data_yaml_path=...
+  ).as_pascal_voc(
+      images_directory_path=...,
+      annotations_directory_path=...
+  )
+  ```
 
 </details>
 

demo.ipynb

@@ -353,7 +353,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install -q ultralytics"
+    "!pip install -q \"ultralytics<=8.3.40\""
    ]
   },
   {

docs/assets.md

@@ -7,16 +7,6 @@ comments: true
 Supervision offers an assets download utility that allows you to download video files
 that you can use in your demos.
 
-## Install extra
-
-To install the Supervision assets utility, you can use `pip`. This utility is available
-as an extra within the Supervision package.
-
-!!! example "pip install"
-    ```bash
-    pip install "supervision[assets]"
-    ```
-
 <div class="md-typeset">
     <h2><a href="#supervision.assets.downloader.download_assets.download_assets">download_assets</a></h2>
 </div>

docs/changelog.md

@@ -1,4 +1,335 @@
-# CHANGELOG
+# Changelog
+
+### 0.26.1 <small>Jul 22, 2025</small>
+
+- Fixed [1894](https://github.com/roboflow/supervision/pull/1894): Error in [`sv.MeanAveragePrecision`](https://supervision.roboflow.com/0.26.1/metrics/mean_average_precision/#supervision.metrics.mean_average_precision.MeanAveragePrecision) where the area used for size-specific evaluation (small / medium / large) was always zero unless explicitly provided in `sv.Detections.data`.
+
+- Fixed [1895](https://github.com/roboflow/supervision/pull/1895): `ID=0` bug in [`sv.MeanAveragePrecision`](https://supervision.roboflow.com/0.26.1/metrics/mean_average_precision/#supervision.metrics.mean_average_precision.MeanAveragePrecision) where objects were getting `0.0` mAP despite perfect IoU matches due to a bug in annotation ID assignment.
+
+- Fixed [1898](https://github.com/roboflow/supervision/pull/1898): Issue where [`sv.MeanAveragePrecision`](https://supervision.roboflow.com/0.26.1/metrics/mean_average_precision/#supervision.metrics.mean_average_precision.MeanAveragePrecision) could return negative values when certain object size categories have no data.
+
+- Fixed [1901](https://github.com/roboflow/supervision/pull/1901): `match_metric` support for [`sv.Detections.with_nms`](https://supervision.roboflow.com/0.26.1/metrics/mean_average_precision/#supervision.detection.core.Detections.with_nms).
+
+- Fixed [1906](https://github.com/roboflow/supervision/pull/1906): `border_thickness` parameter usage for [`sv.PercentageBarAnnotator`](https://supervision.roboflow.com/0.26.1/metrics/mean_average_precision/#supervision.annotators.core.PercentageBarAnnotator).
+
+### 0.26.0 <small>Jul 16, 2025</small>
+
+!!! failure "Removed"
+    `supervision-0.26.0` drops `python3.8` support and upgrade all codes to `python3.9` syntax style.
+
+!!! info "Tip"
+    Supervision’s documentation theme now has a fresh look that is consistent with the documentations of all Roboflow open-source projects. ([#1858](https://github.com/roboflow/supervision/pull/1858))
+
+- Added [#1774](https://github.com/roboflow/supervision/pull/1774): Support for the IOS (Intersection over Smallest) overlap metric that measures how much of the smaller object is covered by the larger one in [`sv.Detections.with_nms`](https://supervision.roboflow.com/0.26.0/detection/core/#supervision.detection.core.Detections.with_nms), [`sv.Detections.with_nmm`](https://supervision.roboflow.com/0.26.0/detection/core/#supervision.detection.core.Detections.with_nmm), [`sv.box_iou_batch`](https://supervision.roboflow.com/0.26.0/detection/utils/iou_and_nms/#supervision.detection.utils.iou_and_nms.box_iou_batch), and [`sv.mask_iou_batch`](https://supervision.roboflow.com/0.26.0/detection/utils/iou_and_nms/#supervision.detection.utils.iou_and_nms.mask_iou_batch).
+
+    ```python
+    import numpy as np
+    import supervision as sv
+
+    boxes_true = np.array([
+        [100, 100, 200, 200],
+        [300, 300, 400, 400]
+    ])
+    boxes_detection = np.array([
+        [150, 150, 250, 250],
+        [320, 320, 420, 420]
+    ])
+
+    sv.box_iou_batch(
+        boxes_true=boxes_true,
+        boxes_detection=boxes_detection,
+        overlap_metric=sv.OverlapMetric.IOU
+    )
+
+    # array([[0.14285714, 0.        ],
+    #        [0.        , 0.47058824]])
+
+    sv.box_iou_batch(
+        boxes_true=boxes_true,
+        boxes_detection=boxes_detection,
+        overlap_metric=sv.OverlapMetric.IOS
+    )
+
+    # array([[0.25, 0.  ],
+    #        [0.  , 0.64]])
+    ```
+
+- Added [#1874](https://github.com/roboflow/supervision/pull/1874): [`sv.box_iou`](https://supervision.roboflow.com/0.26.0/detection/utils/iou_and_nms/#supervision.detection.utils.iou_and_nms.box_iou) that efficiently computes the Intersection over Union (IoU) between two individual bounding boxes.
+
+- Added [#1816](https://github.com/roboflow/supervision/pull/1816): Support for frame limitations and progress bar in [`sv.process_video`](https://supervision.roboflow.com/0.26.0/utils/video/#supervision.utils.video.process_video).
+
+- Added [#1788](https://github.com/roboflow/supervision/pull/1788): Support for creating [`sv.KeyPoints`](https://supervision.roboflow.com/0.26.0/keypoint/core/#supervision.keypoint.core.KeyPoints) objects from [ViTPose](https://huggingface.co/docs/transformers/en/model_doc/vitpose) and [ViTPose++](https://huggingface.co/docs/transformers/en/model_doc/vitpose#vitpose-models) inference results via [`sv.KeyPoints.from_transformers`](https://supervision.roboflow.com/0.26.0/keypoint/core/#supervision.keypoint.core.KeyPoints.from_transformers).
+
+- Added [#1823](https://github.com/roboflow/supervision/pull/1823): [`sv.xyxy_to_xcycarh`](https://supervision.roboflow.com/0.26.0/detection/utils/converters/#supervision.detection.utils.converters.xyxy_to_xcycarh) function to convert bounding box coordinates from `(x_min, y_min, x_max, y_max)` into measurement space to format `(center x, center y, aspect ratio, height)`, where the aspect ratio is `width / height`.
+
+- Added [#1788](https://github.com/roboflow/supervision/pull/1788): [`sv.xyxy_to_xywh`](https://supervision.roboflow.com/0.26.0/detection/utils/converters/#supervision.detection.utils.converters.xyxy_to_xywh) function to convert bounding box coordinates from `(x_min, y_min, x_max, y_max)` format to `(x, y, width, height)` format.
+
+- Changed [#1820](https://github.com/roboflow/supervision/pull/1820): [`sv.LabelAnnotator`](https://supervision.roboflow.com/0.26.0/detection/annotators/#supervision.annotators.core.LabelAnnotator) now supports the `smart_position` parameter to automatically keep labels within frame boundaries, and the `max_line_length` parameter to control text wrapping for long or multi-line labels.
+
+- Changed [#1825](https://github.com/roboflow/supervision/pull/1825): [`sv.LabelAnnotator`](https://supervision.roboflow.com/0.26.0/detection/annotators/#supervision.annotators.core.LabelAnnotator) now supports non-string labels.
+
+- Changed [#1792](https://github.com/roboflow/supervision/pull/1792): [`sv.Detections.from_vlm`](https://supervision.roboflow.com/0.26.0/detection/core/#supervision.detection.core.Detections.from_vlm) now supports parsing bounding boxes and segmentation masks from responses generated by [Google Gemini models](https://ai.google.dev/gemini-api/docs/vision).
+
+    ```python
+    import supervision as sv
+
+    gemini_response_text = """```json
+        [
+            {"box_2d": [543, 40, 728, 200], "label": "cat", "id": 1},
+            {"box_2d": [653, 352, 820, 522], "label": "dog", "id": 2}
+        ]
+    ```"""
+
+    detections = sv.Detections.from_vlm(
+        sv.VLM.GOOGLE_GEMINI_2_5,
+        gemini_response_text,
+        resolution_wh=(1000, 1000),
+        classes=['cat', 'dog'],
+    )
+
+    detections.xyxy
+    # array([[543., 40., 728., 200.], [653., 352., 820., 522.]])
+
+    detections.data
+    # {'class_name': array(['cat', 'dog'], dtype='<U26')}
+
+    detections.class_id
+    # array([0, 1])
+    ```
+
+- Changed [#1878](https://github.com/roboflow/supervision/pull/1878): [`sv.Detections.from_vlm`](https://supervision.roboflow.com/0.26.0/detection/core/#supervision.detection.core.Detections.from_vlm) now supports parsing bounding boxes from responses generated by [Moondream](https://github.com/vikhyat/moondream).
+
+    ```python
+    import supervision as sv
+
+    moondream_result = {
+        'objects': [
+            {
+                'x_min': 0.5704046934843063,
+                'y_min': 0.20069346576929092,
+                'x_max': 0.7049859315156937,
+                'y_max': 0.3012596592307091
+            },
+            {
+                'x_min': 0.6210969910025597,
+                'y_min': 0.3300672620534897,
+                'x_max': 0.8417936339974403,
+                'y_max': 0.4961046129465103
+            }
+        ]
+    }
+
+    detections = sv.Detections.from_vlm(
+        sv.VLM.MOONDREAM,
+        moondream_result,
+        resolution_wh=(1000, 1000),
+    )
+
+    detections.xyxy
+    # array([[1752.28,  818.82, 2165.72, 1229.14],
+    #        [1908.01, 1346.67, 2585.99, 2024.11]])
+    ```
+
+- Changed [#1709](https://github.com/roboflow/supervision/pull/1790): [`sv.Detections.from_vlm`](https://supervision.roboflow.com/0.26.0/detection/core/#supervision.detection.core.Detections.from_vlm) now supports parsing bounding boxes from responses generated by [Qwen-2.5 VL](https://github.com/QwenLM/Qwen2.5-VL).
+
+    ```python
+    import supervision as sv
+
+    qwen_2_5_vl_result = """```json
+    [
+        {"bbox_2d": [139, 768, 315, 954], "label": "cat"},
+        {"bbox_2d": [366, 679, 536, 849], "label": "dog"}
+    ]
+    ```"""
+
+    detections = sv.Detections.from_vlm(
+        sv.VLM.QWEN_2_5_VL,
+        qwen_2_5_vl_result,
+        input_wh=(1000, 1000),
+        resolution_wh=(1000, 1000),
+        classes=['cat', 'dog'],
+    )
+
+    detections.xyxy
+    # array([[139., 768., 315., 954.], [366., 679., 536., 849.]])
+
+    detections.class_id
+    # array([0, 1])
+
+    detections.data
+    # {'class_name': array(['cat', 'dog'], dtype='<U10')}
+
+    detections.class_id
+    # array([0, 1])
+    ```
+
+- Changed [#1786](https://github.com/roboflow/supervision/pull/1786): Significantly improved the speed of HSV color mapping in [`sv.HeatMapAnnotator`](https://supervision.roboflow.com/0.26.0/detection/annotators/#supervision.annotators.core.HeatMapAnnotator), achieving approximately 28x faster performance on 1920x1080 frames.
+
+- Fixed [#1834](https://github.com/roboflow/supervision/pull/1834): Supervision’s [`sv.MeanAveragePrecision`](https://supervision.roboflow.com/0.26.0/metrics/mean_average_precision/#supervision.metrics.mean_average_precision.MeanAveragePrecision) is now fully aligned with [pycocotools](https://github.com/ppwwyyxx/cocoapi), the official COCO evaluation tool, ensuring accurate and standardized metrics. This update enabled us to launch a new version of the [Computer Vision Model Leaderboard](https://leaderboard.roboflow.com/).
+
+    ```python
+    import supervision as sv
+    from supervision.metrics import MeanAveragePrecision
+
+    predictions = sv.Detections(...)
+    targets = sv.Detections(...)
+
+    map_metric = MeanAveragePrecision()
+    map_metric.update(predictions, targets).compute()
+
+    # Average Precision (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.464
+    # Average Precision (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.637
+    # Average Precision (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.203
+    # Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.284
+    # Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.497
+    # Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.629
+    ```
+
+- Fixed [#1767](https://github.com/roboflow/supervision/pull/1767): Fixed losing `sv.Detections.data` when detections filtering.
+
+### 0.25.0 <small>Nov 12, 2024</small>
+
+- No removals or deprecations in this release!
+
+- Essential update to the [`LineZone`](https://supervision.roboflow.com/0.25.0/detection/tools/line_zone/): when computing line crossings, detections that jitter might be counted twice (or more). This can now be solved with the `minimum_crossing_threshold` argument. If you set it to `2` or more, extra frames will be used to confirm the crossing, improving the accuracy significantly. ([#1540](https://github.com/roboflow/supervision/pull/1540))
+
+- It is now possible to track objects detected as [`KeyPoints`](https://supervision.roboflow.com/0.25.0/keypoint/core/#supervision.keypoint.core.KeyPoints). See the complete step-by-step guide in the [Object Tracking Guide](https://supervision.roboflow.com/latest/how_to/track_objects/#keypoints). ([#1658](https://github.com/roboflow/supervision/pull/1658))
+
+```python
+import numpy as np
+import supervision as sv
+from ultralytics import YOLO
+
+model = YOLO("yolov8m-pose.pt")
+tracker = sv.ByteTrack()
+trace_annotator = sv.TraceAnnotator()
+
+def callback(frame: np.ndarray, _: int) -> np.ndarray:
+    results = model(frame)[0]
+    key_points = sv.KeyPoints.from_ultralytics(results)
+
+    detections = key_points.as_detections()
+    detections = tracker.update_with_detections(detections)
+
+    annotated_image = trace_annotator.annotate(frame.copy(), detections)
+    return annotated_image
+
+sv.process_video(
+    source_path="input_video.mp4",
+    target_path="output_video.mp4",
+    callback=callback
+)
+```
+
+- Added `is_empty` method to [`KeyPoints`](https://supervision.roboflow.com/0.25.0/keypoint/core/#supervision.keypoint.core.KeyPoints) to check if there are any keypoints in the object. ([#1658](https://github.com/roboflow/supervision/pull/1658))
+
+- Added `as_detections` method to [`KeyPoints`](https://supervision.roboflow.com/0.25.0/keypoint/core/#supervision.keypoint.core.KeyPoints) that converts `KeyPoints` to `Detections`. ([#1658](https://github.com/roboflow/supervision/pull/1658))
+
+- Added a new video to `supervision[assets]`. ([#1657](https://github.com/roboflow/supervision/pull/1657))
+
+```python
+from supervision.assets import download_assets, VideoAssets
+
+path_to_video = download_assets(VideoAssets.SKIING)
+```
+
+- Supervision can now be used with [`Python 3.13`](https://docs.python.org/3/whatsnew/3.13.html). The most renowned update is the ability to run Python [without Global Interpreter Lock (GIL)](https://docs.python.org/3/whatsnew/3.13.html#whatsnew313-free-threaded-cpython). We expect support for this among our dependencies to be inconsistent, but if you do attempt it - let us know the results! ([#1595](https://github.com/roboflow/supervision/pull/1595))
+
+- Added [`Mean Average Recall`](https://supervision.roboflow.com/latest/metrics/mean_average_recall/) mAR metric, which returns a recall score, averaged over IoU thresholds, detected object classes, and limits imposed on maximum considered detections. ([#1661](https://github.com/roboflow/supervision/pull/1661))
+
+```python
+import supervision as sv
+from supervision.metrics import MeanAverageRecall
+
+predictions = sv.Detections(...)
+targets = sv.Detections(...)
+
+map_metric = MeanAverageRecall()
+map_result = map_metric.update(predictions, targets).compute()
+
+map_result.plot()
+```
+
+- Added [`Precision`](https://supervision.roboflow.com/latest/metrics/precision/) and [`Recall`](https://supervision.roboflow.com/latest/metrics/recall/) metrics, providing a baseline for comparing model outputs to ground truth or another model ([#1609](https://github.com/roboflow/supervision/pull/1609))
+
+```python
+import supervision as sv
+from supervision.metrics import Recall
+
+predictions = sv.Detections(...)
+targets = sv.Detections(...)
+
+recall_metric = Recall()
+recall_result = recall_metric.update(predictions, targets).compute()
+
+recall_result.plot()
+```
+
+- All Metrics now support Oriented Bounding Boxes (OBB) ([#1593](https://github.com/roboflow/supervision/pull/1593))
+
+```python
+import supervision as sv
+from supervision.metrics import F1_Score
+
+predictions = sv.Detections(...)
+targets = sv.Detections(...)
+
+f1_metric = MeanAverageRecall(metric_target=sv.MetricTarget.ORIENTED_BOUNDING_BOXES)
+f1_result = f1_metric.update(predictions, targets).compute()
+```
+
+- Introducing Smart Labels! When `smart_position` is set for [`LabelAnnotator`](https://supervision.roboflow.com/0.25.0/detection/annotators/#supervision.annotators.core.LabelAnnotator), [`RichLabelAnnotator`](https://supervision.roboflow.com/0.25.0/detection/annotators/#supervision.annotators.core.RichLabelAnnotator) or [`VertexLabelAnnotator`](https://supervision.roboflow.com/0.25.0/detection/annotators/#supervision.annotators.core.RichLabelAnnotator), the labels will move around to avoid overlapping others. ([#1625](https://github.com/roboflow/supervision/pull/1625))
+
+```python
+import supervision as sv
+from ultralytics import YOLO
+
+image = cv2.imread("image.jpg")
+
+label_annotator = sv.LabelAnnotator(smart_position=True)
+
+model = YOLO("yolo11m.pt")
+results = model(image)[0]
+detections = sv.Detections.from_ultralytics(results)
+
+annotated_frame = label_annotator.annotate(first_frame.copy(), detections)
+sv.plot_image(annotated_frame)
+```
+
+- Added the `metadata` variable to [`Detections`](https://supervision.roboflow.com/0.25.0/detection/core/#supervision.detection.core.Detections). It allows you to store custom data per-image, rather than per-detected-object as was possible with `data` variable. For example, `metadata` could be used to store the source video path, camera model or camera parameters. ([#1589](https://github.com/roboflow/supervision/pull/1589))
+
+```python
+import supervision as sv
+from ultralytics import YOLO
+
+model = YOLO("yolov8m")
+
+result = model("image.png")[0]
+detections = sv.Detections.from_ultralytics(result)
+
+# Items in `data` must match length of detections
+object_ids = [num for num in range(len(detections))]
+detections.data["object_number"] = object_ids
+
+# Items in `metadata` can be of any length.
+detections.metadata["camera_model"] = "Luxonis OAK-D"
+```
+
+- Added a `py.typed` type hints metafile. It should provide a stronger signal to type annotators and IDEs that type support is available. ([#1586](https://github.com/roboflow/supervision/pull/1586))
+
+- `ByteTrack` no longer requires `detections` to have a `class_id` ([#1637](https://github.com/roboflow/supervision/pull/1637))
+- `draw_line`, `draw_rectangle`, `draw_filled_rectangle`, `draw_polygon`, `draw_filled_polygon` and `PolygonZoneAnnotator` now comes with a default color ([#1591](https://github.com/roboflow/supervision/pull/1591))
+- Dataset classes are treated as case-sensitive when merging multiple datasets. ([#1643](https://github.com/roboflow/supervision/pull/1643))
+- Expanded [metrics documentation](https://supervision.roboflow.com/0.25.0/metrics/f1_score/) with example plots and printed results ([#1660](https://github.com/roboflow/supervision/pull/1660))
+- Added usage example for polygon zone ([#1608](https://github.com/roboflow/supervision/pull/1608))
+- Small improvements to error handling in polygons: ([#1602](https://github.com/roboflow/supervision/pull/1602))
+
+- Updated [`ByteTrack`](https://supervision.roboflow.com/0.25.0/trackers/#supervision.tracker.byte_tracker.core.ByteTrack), removing shared variables. Previously, multiple instances of `ByteTrack` would share some date, requiring liberal use of `tracker.reset()`. ([#1603](https://github.com/roboflow/supervision/pull/1603)), ([#1528](https://github.com/roboflow/supervision/pull/1528))
+- Fixed a bug where `class_agnostic` setting in `MeanAveragePrecision` would not work. ([#1577](https://github.com/roboflow/supervision/pull/1577)) hacktoberfest
+- Removed welcome workflow from our CI system. ([#1596](https://github.com/roboflow/supervision/pull/1596))
+
+- Large refactor of `ByteTrack`: STrack moved to separate class, removed superfluous `BaseTrack` class, removed unused variables ([#1603](https://github.com/roboflow/supervision/pull/1603))
+- Large refactor of `RichLabelAnnotator`, matching its contents with `LabelAnnotator`. ([#1625](https://github.com/roboflow/supervision/pull/1625))
 
 ### 0.24.0 <small>Oct 4, 2024</small>
 
@@ -238,9 +569,9 @@ detections = sv.Detections.from_sam(sam_result=sam_result)
 
 - Changed [#1434](https://github.com/roboflow/supervision/pull/1434): [`InferenceSlicer`](https://supervision.roboflow.com/0.23.0/detection/tools/inference_slicer/) now features an `overlap_wh` parameter, making it easier to compute slice sizes when handling overlapping slices.
 
-- Fix [#1448](https://github.com/roboflow/supervision/pull/1448): Various annotator type issues have been resolved, supporting expanded error handling.
+- Fixed [#1448](https://github.com/roboflow/supervision/pull/1448): Various annotator type issues have been resolved, supporting expanded error handling.
 
-- Fix [#1348](https://github.com/roboflow/supervision/pull/1348): Introduced a new method for [seeking to a specific video frame](https://supervision.roboflow.com/0.23.0/utils/video/#supervision.utils.video.get_video_frames_generator), addressing cases where traditional seek methods were failing. It can be enabled with `iterative_seek=True`.
+- Fixed [#1348](https://github.com/roboflow/supervision/pull/1348): Introduced a new method for [seeking to a specific video frame](https://supervision.roboflow.com/0.23.0/utils/video/#supervision.utils.video.get_video_frames_generator), addressing cases where traditional seek methods were failing. It can be enabled with `iterative_seek=True`.
 
 ```python
 import supervision as sv
@@ -253,7 +584,7 @@ for frame in sv.get_video_frames_generator(
     ...
 ```
 
-- Fix [#1424](https://github.com/roboflow/supervision/pull/1424): `plot_image` function now clearly indicates that the size is in inches.
+- Fixed [#1424](https://github.com/roboflow/supervision/pull/1424): `plot_image` function now clearly indicates that the size is in inches.
 
 !!! failure "Removed"
 
@@ -966,7 +1297,7 @@ array([
 
 ### 0.11.1 <small>June 29, 2023</small>
 
-- Fix [#165](https://github.com/roboflow/supervision/pull/165): [`as_folder_structure`](/0.11.1/dataset/core/#supervision.dataset.core.ClassificationDataset.as_folder_structure) fails to save [`sv.ClassificationDataset`](/0.11.1/dataset/core/#classificationdataset) when it is result of inference.
+- Fixed [#165](https://github.com/roboflow/supervision/pull/165): [`as_folder_structure`](/0.11.1/dataset/core/#supervision.dataset.core.ClassificationDataset.as_folder_structure) fails to save [`sv.ClassificationDataset`](/0.11.1/dataset/core/#classificationdataset) when it is result of inference.
 
 ### 0.11.0 <small>June 28, 2023</small>
 
@@ -1012,7 +1343,7 @@ array([
 
 - Added [#162](https://github.com/roboflow/supervision/pull/162): additional `start` and `end` arguments to [`sv.get_video_frames_generator`](/0.11.0/utils/video/#get_video_frames_generator) allowing to generate frames only for a selected part of the video.
 
-- Fix [#157](https://github.com/roboflow/supervision/pull/157): incorrect loading of YOLO dataset class names from `data.yaml`.
+- Fixed [#157](https://github.com/roboflow/supervision/pull/157): incorrect loading of YOLO dataset class names from `data.yaml`.
 
 ### 0.10.0 <small>June 14, 2023</small>
 

docs/deprecated.md

@@ -7,18 +7,20 @@ status: deprecated
 
 These features are phased out due to better alternatives or potential issues in future versions. Deprecated functionalities are supported for **five subsequent releases**, providing time for users to transition to updated methods.
 
-- Constructing [`DetectionDataset`](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.DetectionDataset) and [`ClassificationDataset`](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.ClassificationDataset) with parameter `images` as `Dict[str, np.ndarray]` will be removed in `supervision-0.26.0`. Please pass a list of paths `List[str]` instead.
-
-- The `DetectionDataset.images` property will be removed in `supervision-0.26.0`. Please loop over images with `for path, image, annotation in dataset:`, as that does not require loading all images into memory.
-
-- `BoundingBoxAnnotator` has been renamed to `BoxAnnotator` after the old implementation of [`BoxAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator) has been removed. `BoundingBoxAnnotator` will be removed in `supervision-0.26.0`.
 
 - `overlap_filter_strategy` in [`InferenceSlicer.__init__`](https://supervision.roboflow.com/latest/detection/tools/inference_slicer/) is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_strategy` instead.
-
 - `overlap_ratio_wh` in [`InferenceSlicer.__init__`](https://supervision.roboflow.com/latest/detection/tools/inference_slicer/) is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_wh` instead.
+- `sv.LMM` enum is deprecated and will be removed in `supervision-0.31.0`. Use `sv.VLM` instead.
+- [`sv.Detections.from_lmm`](https://supervision.roboflow.com/0.26.0/detection/core/#supervision.detection.core.Detections.from_lmm) property is deprecated and will be removed in `supervision-0.31.0`. Use [`sv.Detections.from_vlm`](https://supervision.roboflow.com/0.26.0/detection/core/#supervision.detection.core.Detections.from_vlm) instead.
 
 # Removed
 
+### 0.26.0
+
+- The `sv.DetectionDataset.images` property has been removed in `supervision-0.26.0`. Please loop over images with `for path, image, annotation in dataset:`, as that does not require loading all images into memory. Also, constructing `sv.DetectionDataset` with parameter `images` as `Dict[str, np.ndarray]` is deprecated and has been removed in `supervision-0.26.0`. Please pass a list of paths `List[str]` instead.
+- The name `sv.BoundingBoxAnnotator` is deprecated and has been removed in `supervision-0.26.0`. It has been renamed to [`sv.BoxAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.BoxAnnotator).
+
+
 ### 0.24.0
 
 - The `frame_resolution_wh ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) has been removed.
@@ -31,12 +33,12 @@ These features are phased out due to better alternatives or potential issues in
 
 ### 0.22.0
 
-- `Detections.from_roboflow` is removed as of `supervision-0.22.0`. Use [`Detections.from_inference`](detection/core.md/#supervision.detection.core.Detections.from_inference) instead.
-- The method `Color.white()` was removed as of `supervision-0.22.0`. Use the constant `Color.WHITE` instead.
-- The method `Color.black()` was removed as of `supervision-0.22.0`. Use the constant `Color.BLACK` instead.
-- The method `Color.red()` was removed as of `supervision-0.22.0`. Use the constant `Color.RED` instead.
-- The method `Color.green()` was removed as of `supervision-0.22.0`. Use the constant `Color.GREEN` instead.
-- The method `Color.blue()` was removed as of `supervision-0.22.0`. Use the constant `Color.BLUE` instead.
-- The method `ColorPalette.default()` was removed as of `supervision-0.22.0`. Use the constant [`ColorPalette.DEFAULT`](/utils/draw/#supervision.draw.color.ColorPalette.DEFAULT) instead.
-- `BoxAnnotator` was removed as of `supervision-0.22.0`, however `BoundingBoxAnnotator` was immediately renamed to `BoxAnnotator`. Use [`BoxAnnotator`](detection/annotators.md/#supervision.annotators.core.BoxAnnotator) and [`LabelAnnotator`](detection/annotators.md/#supervision.annotators.core.LabelAnnotator) instead of the old `BoxAnnotator`.
-- The method `FPSMonitor.__call__` was removed as of `supervision-0.22.0`. Use the attribute [`FPSMonitor.fps`](utils/video.md/#supervision.utils.video.FPSMonitor.fps) instead.
+- `sv.Detections.from_roboflow` is removed as of `supervision-0.22.0`. Use [`Detections.from_inference`](detection/core.md/#supervision.detection.core.Detections.from_inference) instead.
+- The method `sv.Color.white()` was removed as of `supervision-0.22.0`. Use the constant `sv.Color.WHITE` instead.
+- The method `sv.Color.black()` was removed as of `supervision-0.22.0`. Use the constant `sv.Color.BLACK` instead.
+- The method `sv.Color.red()` was removed as of `supervision-0.22.0`. Use the constant `sv.Color.RED` instead.
+- The method `sv.Color.green()` was removed as of `supervision-0.22.0`. Use the constant `sv.Color.GREEN` instead.
+- The method `sv.Color.blue()` was removed as of `supervision-0.22.0`. Use the constant `sv.Color.BLUE` instead.
+- The method `sv.ColorPalette.default()` was removed as of `supervision-0.22.0`. Use the constant [`ColorPalette.DEFAULT`](/utils/draw/#supervision.draw.color.ColorPalette.DEFAULT) instead.
+- `sv.BoxAnnotator` was removed as of `supervision-0.22.0`, however `sv.BoundingBoxAnnotator` was immediately renamed to `sv.BoxAnnotator`. Use [`BoxAnnotator`](detection/annotators.md/#supervision.annotators.core.BoxAnnotator) and [`LabelAnnotator`](detection/annotators.md/#supervision.annotators.core.LabelAnnotator) instead of the old `sv.BoxAnnotator`.
+- The method `sv.FPSMonitor.__call__` was removed as of `supervision-0.22.0`. Use the attribute [`sv.FPSMonitor.fps`](utils/video.md/#supervision.utils.video.FPSMonitor.fps) instead.

docs/detection/annotators.md

@@ -234,7 +234,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
 
     <div class="result" markdown>
 
-    ![mask-annotator-example](https://media.roboflow.com/supervision-annotator-examples/mask-annotator-example-purple.png){ align=center width="800" }
+    ![mask-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
+    mask-annotator-example-purple.png){ align=center width="800" }
 
     </div>
 
@@ -255,7 +256,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
 
     <div class="result" markdown>
 
-    ![polygon-annotator-example](https://media.roboflow.com/supervision-annotator-examples/polygon-annotator-example-purple.png){ align=center width="800" }
+    ![polygon-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
+    polygon-annotator-example-purple.png){ align=center width="800" }
 
     </div>
 
@@ -283,7 +285,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
 
     <div class="result" markdown>
 
-    ![label-annotator-example](https://media.roboflow.com/supervision-annotator-examples/label-annotator-example-purple.png){ align=center width="800" }
+    ![label-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
+    label-annotator-example-purple.png){ align=center width="800" }
 
     </div>
 
@@ -314,7 +317,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
 
     <div class="result" markdown>
 
-    ![label-annotator-example](https://media.roboflow.com/supervision-annotator-examples/label-annotator-example-purple.png){ align=center width="800" }
+    ![label-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
+    label-annotator-example-purple.png){ align=center width="800" }
 
     </div>
 
@@ -341,24 +345,32 @@ Annotators accept detections and apply box or mask visualizations to the detecti
 
     <div class="result" markdown>
 
-    ![icon-annotator-example](https://media.roboflow.com/supervision-annotator-examples/icon-annotator-example.png){ align=center width="800" }
+    ![icon-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
+    icon-annotator-example.png){ align=center width="800" }
 
     </div>
 
-=== "Crop"
+    <!-- === "Crop"
 
-    ```python
-    import supervision as sv
+        ```python
+        import supervision as sv
 
-    image = ...
-    detections = sv.Detections(...)
+        image = ...
+        detections = sv.Detections(...)
 
-    crop_annotator = sv.CropAnnotator()
-    annotated_frame = crop_annotator.annotate(
-        scene=image.copy(),
-        detections=detections
-    )
-    ```
+        crop_annotator = sv.CropAnnotator()
+        annotated_frame = crop_annotator.annotate(
+            scene=image.copy(),
+            detections=detections
+        )
+        ```
+        <div class="result" markdown>
+
+        ![crop-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
+        crop-annotator-example.png){ align=center width="800" }
+
+        </div>
+    -->
 
 === "Blur"
 
@@ -377,7 +389,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
 
     <div class="result" markdown>
 
-    ![blur-annotator-example](https://media.roboflow.com/supervision-annotator-examples/blur-annotator-example-purple.png){ align=center width="800" }
+    ![blur-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
+    blur-annotator-example-purple.png){ align=center width="800" }
 
     </div>
 
@@ -398,7 +411,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
 
     <div class="result" markdown>
 
-    ![pixelate-annotator-example](https://media.roboflow.com/supervision-annotator-examples/pixelate-annotator-example-10.png){ align=center width="800" }
+    ![pixelate-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
+    pixelate-annotator-example-10.png){ align=center width="800" }
 
     </div>
 
@@ -429,7 +443,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
 
     <div class="result" markdown>
 
-    ![trace-annotator-example](https://media.roboflow.com/supervision-annotator-examples/trace-annotator-example-purple.png){ align=center width="800" }
+    ![trace-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
+    trace-annotator-example-purple.png){ align=center width="800" }
 
     </div>
 
@@ -458,7 +473,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
 
     <div class="result" markdown>
 
-    ![heat-map-annotator-example](https://media.roboflow.com/supervision-annotator-examples/heat-map-annotator-example-purple.png){ align=center width="800" }
+    ![heat-map-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
+    heat-map-annotator-example-purple.png){ align=center width="800" }
 
     </div>
 
@@ -479,7 +495,31 @@ Annotators accept detections and apply box or mask visualizations to the detecti
 
     <div class="result" markdown>
 
-    ![background-overlay-annotator-example](https://media.roboflow.com/supervision-annotator-examples/background-color-annotator-example-purple.png)
+    ![background-overlay-annotator-example](https://media.roboflow.com/supervision-annotator-examples/background-color-annotator-example-purple.png){ align=center width="800" }
+
+    </div>
+
+=== "Comparison"
+
+    ```python
+    import supervision as sv
+
+    image = ...
+    detections_1 = sv.Detections(...)
+    detections_2 = sv.Detections(...)
+
+    comparison_annotator = sv.ComparisonAnnotator()
+    annotated_frame = comparison_annotator.annotate(
+        scene=image.copy(),
+        detections_1=detections_1,
+        detections_2=detections_2
+    )
+    ```
+
+    <div class="result" markdown>
+
+    ![comparison-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
+    comparison-annotator-example.png){ align=center width="800" }
 
     </div>
 
@@ -622,6 +662,12 @@ Annotators accept detections and apply box or mask visualizations to the detecti
 
 :::supervision.annotators.core.BackgroundOverlayAnnotator
 
+<div class="md-typeset">
+    <h2><a href="#supervision.annotators.core.ComparisonAnnotator">ComparisonAnnotator</a></h2>
+</div>
+
+:::supervision.annotators.core.ComparisonAnnotator
+
 <div class="md-typeset">
     <h2><a href="#supervision.annotators.core.ColorLookup">ColorLookup</a></h2>
 </div>

docs/detection/double_detection_filter.md

@@ -1,29 +0,0 @@
----
-comments: true
----
-
-# Double Detection Filter
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.overlap_filter.OverlapFilter">OverlapFilter</a></h2>
-</div>
-
-:::supervision.detection.overlap_filter.OverlapFilter
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.overlap_filter.box_non_max_suppression">box_non_max_suppression</a></h2>
-</div>
-
-:::supervision.detection.overlap_filter.box_non_max_suppression
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.overlap_filter.mask_non_max_suppression">mask_non_max_suppression</a></h2>
-</div>
-
-:::supervision.detection.overlap_filter.mask_non_max_suppression
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.overlap_filter.box_non_max_merge">box_non_max_merge</a></h2>
-</div>
-
-:::supervision.detection.overlap_filter.box_non_max_merge

docs/detection/tools/inference_slicer.md

@@ -1,6 +1,5 @@
 ---
 comments: true
-status: new
 ---
 
 # InferenceSlicer

docs/detection/tools/line_zone.md

@@ -1,6 +1,5 @@
 ---
 comments: true
-status: new
 ---
 
 <div class="md-typeset">

docs/detection/tools/polygon_zone.md

@@ -1,6 +1,5 @@
 ---
 comments: true
-status: new
 ---
 
 <div class="md-typeset">

docs/detection/utils.md

@@ -1,108 +0,0 @@
----
-comments: true
-status: new
----
-
-# Detection Utils
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.utils.box_iou_batch">box_iou_batch</a></h2>
-</div>
-
-:::supervision.detection.utils.box_iou_batch
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.utils.mask_iou_batch">mask_iou_batch</a></h2>
-</div>
-
-:::supervision.detection.utils.mask_iou_batch
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.utils.oriented_box_iou_batch">oriented_box_iou_batch</a></h2>
-</div>
-
-:::supervision.detection.utils.oriented_box_iou_batch
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.utils.polygon_to_mask">polygon_to_mask</a></h2>
-</div>
-
-:::supervision.detection.utils.polygon_to_mask
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.utils.mask_to_xyxy">mask_to_xyxy</a></h2>
-</div>
-
-:::supervision.detection.utils.mask_to_xyxy
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.utils.mask_to_polygons">mask_to_polygons</a></h2>
-</div>
-
-:::supervision.detection.utils.mask_to_polygons
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.utils.polygon_to_xyxy">polygon_to_xyxy</a></h2>
-</div>
-
-:::supervision.detection.utils.polygon_to_xyxy
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.utils.filter_polygons_by_area">filter_polygons_by_area</a></h2>
-</div>
-
-:::supervision.detection.utils.filter_polygons_by_area
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.utils.move_boxes">move_boxes</a></h2>
-</div>
-
-:::supervision.detection.utils.move_boxes
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.utils.move_masks">move_masks</a></h2>
-</div>
-
-:::supervision.detection.utils.move_masks
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.utils.scale_boxes">scale_boxes</a></h2>
-</div>
-
-:::supervision.detection.utils.scale_boxes
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.utils.clip_boxes">clip_boxes</a></h2>
-</div>
-
-:::supervision.detection.utils.clip_boxes
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.utils.pad_boxes">pad_boxes</a></h2>
-</div>
-
-:::supervision.detection.utils.pad_boxes
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.utils.xywh_to_xyxy">xywh_to_xyxy</a></h2>
-</div>
-
-:::supervision.detection.utils.xywh_to_xyxy
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.utils.xcycwh_to_xyxy">xcycwh_to_xyxy</a></h2>
-</div>
-
-:::supervision.detection.utils.xcycwh_to_xyxy
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.utils.contains_holes">contains_holes</a></h2>
-</div>
-
-:::supervision.detection.utils.contains_holes
-
-<div class="md-typeset">
-  <h2><a href="#supervision.detection.utils.contains_multiple_segments">contains_multiple_segments</a></h2>
-</div>
-
-:::supervision.detection.utils.contains_multiple_segments

docs/detection/utils/boxes.md

@@ -0,0 +1,36 @@
+---
+comments: true
+status: new
+---
+
+# Boxes Utils
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.boxes.move_boxes">move_boxes</a></h2>
+</div>
+
+:::supervision.detection.utils.boxes.move_boxes
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.boxes.scale_boxes">scale_boxes</a></h2>
+</div>
+
+:::supervision.detection.utils.boxes.scale_boxes
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.boxes.utils.clip_boxes">clip_boxes</a></h2>
+</div>
+
+:::supervision.detection.utils.boxes.clip_boxes
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.boxes.pad_boxes">pad_boxes</a></h2>
+</div>
+
+:::supervision.detection.utils.boxes.pad_boxes
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.boxes.denormalize_boxes">denormalize_boxes</a></h2>
+</div>
+
+:::supervision.detection.utils.boxes.denormalize_boxes

docs/detection/utils/converters.md

@@ -0,0 +1,66 @@
+---
+comments: true
+status: new
+---
+
+# Converters Utils
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.converters.xyxy_to_xywh">xyxy_to_xywh</a></h2>
+</div>
+
+:::supervision.detection.utils.converters.xyxy_to_xywh
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.converters.xywh_to_xyxy">xywh_to_xyxy</a></h2>
+</div>
+
+:::supervision.detection.utils.converters.xywh_to_xyxy
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.converters.xyxy_to_xcycarh">xyxy_to_xcycarh</a></h2>
+</div>
+
+:::supervision.detection.utils.converters.xyxy_to_xcycarh
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.converters.xcycwh_to_xyxy">xcycwh_to_xyxy</a></h2>
+</div>
+
+:::supervision.detection.utils.converters.xcycwh_to_xyxy
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.converters.xyxy_to_polygons">xyxy_to_polygons</a></h2>
+</div>
+
+:::supervision.detection.utils.converters.xyxy_to_polygons
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.converters.mask_to_xyxy">mask_to_xyxy</a></h2>
+</div>
+
+:::supervision.detection.utils.converters.mask_to_xyxy
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.converters.mask_to_polygons">mask_to_polygons</a></h2>
+</div>
+
+:::supervision.detection.utils.converters.mask_to_polygons
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.converters.polygon_to_mask">polygon_to_mask</a></h2>
+</div>
+
+:::supervision.detection.utils.converters.polygon_to_mask
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.converters.polygon_to_xyxy">polygon_to_xyxy</a></h2>
+</div>
+
+:::supervision.detection.utils.converters.polygon_to_xyxy
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.converters.xyxy_to_mask">xyxy_to_mask</a></h2>
+</div>
+
+:::supervision.detection.utils.converters.xyxy_to_mask

docs/detection/utils/iou_and_nms.md

@@ -0,0 +1,72 @@
+---
+comments: true
+status: new
+---
+
+# IoU and NMS Utils
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.iou_and_nms.OverlapFilter">OverlapFilter</a></h2>
+</div>
+
+:::supervision.detection.utils.iou_and_nms.OverlapFilter
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.iou_and_nms.OverlapMetric">OverlapMetric</a></h2>
+</div>
+
+:::supervision.detection.utils.iou_and_nms.OverlapMetric
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.iou_and_nms.utils.box_iou">box_iou</a></h2>
+</div>
+
+:::supervision.detection.utils.iou_and_nms.box_iou
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.iou_and_nms.box_iou_batch">box_iou_batch</a></h2>
+</div>
+
+:::supervision.detection.utils.iou_and_nms.box_iou_batch
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.iou_and_nms.box_iou_batch_with_jaccard">box_iou_batch_with_jaccard</a></h2>
+</div>
+
+:::supervision.detection.utils.iou_and_nms.box_iou_batch_with_jaccard
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.iou_and_nms.mask_iou_batch">mask_iou_batch</a></h2>
+</div>
+
+:::supervision.detection.utils.iou_and_nms.mask_iou_batch
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.iou_and_nms.oriented_box_iou_batch">oriented_box_iou_batch</a></h2>
+</div>
+
+:::supervision.detection.utils.iou_and_nms.oriented_box_iou_batch
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.iou_and_nms.box_non_max_suppression">box_non_max_suppression</a></h2>
+</div>
+
+:::supervision.detection.utils.iou_and_nms.box_non_max_suppression
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.iou_and_nms.mask_non_max_suppression">mask_non_max_suppression</a></h2>
+</div>
+
+:::supervision.detection.utils.iou_and_nms.mask_non_max_suppression
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.iou_and_nms.box_non_max_merge">box_non_max_merge</a></h2>
+</div>
+
+:::supervision.detection.utils.iou_and_nms.box_non_max_merge
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.iou_and_nms.mask_non_max_merge">mask_non_max_merge</a></h2>
+</div>
+
+:::supervision.detection.utils.iou_and_nms.mask_non_max_merge

docs/detection/utils/masks.md

@@ -0,0 +1,30 @@
+---
+comments: true
+status: new
+---
+
+# Masks Utils
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.masks.move_masks">move_masks</a></h2>
+</div>
+
+:::supervision.detection.utils.masks.move_masks
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.masks.contains_holes">contains_holes</a></h2>
+</div>
+
+:::supervision.detection.utils.masks.contains_holes
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.masks.contains_multiple_segments">contains_multiple_segments</a></h2>
+</div>
+
+:::supervision.detection.utils.masks.contains_multiple_segments
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.masks.filter_segments_by_distance">filter_segments_by_distance</a></h2>
+</div>
+
+:::supervision.detection.utils.masks.filter_segments_by_distance

docs/detection/utils/polygons.md

@@ -0,0 +1,18 @@
+---
+comments: true
+status: new
+---
+
+# Polygons Utils
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.polygons.filter_polygons_by_area">filter_polygons_by_area</a></h2>
+</div>
+
+:::supervision.detection.utils.polygons.filter_polygons_by_area
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.polygons.approximate_polygon">approximate_polygon</a></h2>
+</div>
+
+:::supervision.detection.utils.polygons.approximate_polygon

docs/detection/utils/vlms.md

@@ -0,0 +1,18 @@
+---
+comments: true
+status: new
+---
+
+# VLMs Utils
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.vlms.edit_distance">edit_distance</a></h2>
+</div>
+
+:::supervision.detection.utils.vlms.edit_distance
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.vlms.fuzzy_match_index">fuzzy_match_index</a></h2>
+</div>
+
+:::supervision.detection.utils.vlms.fuzzy_match_index

docs/how_to/benchmark_a_model.md

@@ -0,0 +1,441 @@
+---
+comments: true
+status: new
+---
+
+![Corgi Example](https://media.roboflow.com/supervision/image-examples/how-to/benchmark-models/corgi-sorted-2.png)
+
+# Benchmark a Model
+
+Have you ever trained multiple detection models and wondered which one performs best on your specific use case? Or maybe you've downloaded a pre-trained model and want to verify its performance on your dataset? Model benchmarking is essential for making informed decisions about which model to deploy in production.
+
+This guide will show an easy way to benchmark your results using `supervision`. It will go over:
+
+1. [Loading a dataset](#loading-a-dataset)
+2. [Loading a model](#loading-a-model)
+3. [Benchmarking Basics](#benchmarking-basics)
+4. [Running a Model](#running-a-model)
+5. [Remapping Classes](#remapping-classes)
+6. [Visual Benchmarking](#visual-benchmarking)
+7. [Benchmarking Metrics](#benchmarking-metrics)
+8. [Mean Average Precision (mAP)](#mean-average-precision-map)
+9. [F1 Score](#f1-score)
+10. [Bonus: Model Leaderboard](#model-leaderboard)
+
+This guide will use an instance segmentation model, but it applies to object detection, instance segmentation, and oriented bounding box models (OBB) too.
+
+A condensed version of this guide is available as a [Colab Notebook](https://colab.research.google.com/drive/1HoOY9pZoVwGiRMmLHtir0qT6Uj45w6Ps?usp=sharing).
+
+## Loading a Dataset
+
+Suppose you start with a dataset. Perhaps you found it on [Universe](https://universe.roboflow.com/); perhaps you [labeled your own](https://roboflow.com/how-to-label/yolo11). In either case, this guide assumes you know of a labelled dataset at hand.
+
+We'll use the following libraries:
+
+- `roboflow` to manage the dataset and deploy models
+- `inference` to run the models
+- `supervision` to evaluate the model results
+
+```bash
+pip install roboflow supervision
+pip install git+https://github.com/roboflow/inference.git@linas/allow-latest-rc-supervision
+```
+
+!!! info
+
+    We're updating `inference` at the moment. Please install it as shown above.
+
+Here's how you can download a dataset:
+
+```python
+from roboflow import Roboflow
+
+rf = Roboflow(api_key="<YOUR_API_KEY>")
+project = rf.workspace("<WORKSPACE_NAME>").project("<PROJECT_NAME>")
+dataset = project.version(<DATASET_VERSION_NUMBER>).download("<FORMAT>")
+```
+
+If your dataset is from Universe, go to `Dataset` > `Download Dataset` > select the format (e.g. `YOLOv11`) > `Show download code`.
+
+If labeling your own data, go to the [dashboard](https://app.roboflow.com/) and check this [guide](https://docs.roboflow.com/api-reference/workspace-and-project-ids) to find your workspace and project IDs.
+
+In this guide, we shall use a small [Corgi v2](https://universe.roboflow.com/model-examples/segmented-animals-basic) dataset. It is well-labeled and comes with a test set.
+
+```python
+from roboflow import Roboflow
+
+rf = Roboflow(api_key="<YOUR_API_KEY>")
+project = rf.workspace("fbamse1-gm2os").project("corgi-v2")
+dataset = project.version(4).download("yolov11")
+```
+
+This will create a folder called `Corgi-v2-4` with the dataset in the current working directory, with `train`, `test`, and `valid` folders and a `data.yaml` file.
+
+## Loading a Model
+
+Let's load a model.
+
+=== "Inference, Local"
+
+    Roboflow supports a range of state-of-the-art [pre-trained models](https://inference.roboflow.com/quickstart/aliases/) for object detection, instance segmentation, and pose tracking. You don't even need an API key!
+
+    Let's load such a model with inference [`inference`](https://inference.roboflow.com/).
+
+    ```python
+    from inference import get_model
+
+    model = get_model(model_id="yolov11s-seg-640")
+    ```
+
+=== "Inference, Deployed"
+
+    You can train and deploy a model without leaving the Roboflow platform. See this [guide](https://docs.roboflow.com/train/train/train-from-scratch) for more details.
+
+    To load a model, you can use inference:
+
+    ```python
+    from inference import get_model
+
+    model_id = "<PROJECT_NAME>/<MODEL_VERSION>"
+    model = get_model(model_id=model_id)
+    ```
+
+=== "Ultralytics"
+
+    Similarly to Inference, Ultralytics allows you to run a variety of models.
+
+    ```bash
+    pip install "ultralytics<=8.3.40"
+    ```
+
+    ```python
+    from ultralytics import YOLO
+
+    model = YOLO("yolo11s-seg.pt")
+    ```
+
+## Benchmarking Basics
+
+Evaluating your model requires careful selection of the dataset. Which images should you use?Let's go over the different scenarios.
+
+- **Unrelated Dataset**: If you have a dataset that was not used to train the model, this is the best choice.
+- **Training Set**: This is the set of images used to train the model. This is fine if the model was not trained on this dataset. Otherwise, **never** use it for benchmarking - the results will seem unrealistically good.
+- **Validation Set**: This is the set of images used to validate the model during training. Every Nth training epoch, the model is evaluated on the validation set. Often the training is stopped once the validation loss stops improving. Therefore, even while the images aren't used to train the model, it still indirectly influences the training outcome.
+- **Test Set**: This is the set of images kept aside for model testing. It is exactly the set you should use for benchmarking. If the dataset was split correctly, none of these images would be shown to the model during training.
+
+Therefore, an unrelated dataset or the `test` set is the best choice for benchmarking.
+Several other problems may arise:
+
+- **Extra Classes**: An unrelated dataset may contain additional classes which you may need to [filter out](https://supervision.roboflow.com/how_to/filter_detections/#by-set-of-classes) before computing metrics.
+- **Class Mismatch**: In an unrelated dataset, the class names or IDs may be different to what your model produces, you'll need to remap them, which is [shown in this guide](#running-a-model).
+- **Data Contamination**: The `test` set may not be split correctly, with images from the test set also present in `training` or `validation` set and used during training. In this case, the results will be overly optimistic. This also applies when **very similar** images are used for training and testing - e.g. those taken in the same environment, same lighting conditions, similar angle, etc.
+- **Missing Test Set**: Some datasets do not come with a test set. In this case, you should collect and [label](https://roboflow.com/annotate) your own data. Alternatively, a validation set could be used, but the results could be overly optimistic. Make sure to test in the real world as soon as possible.
+
+## Running a Model
+
+At this stage, you should have:
+
+- A dataset of labeled images to evaluate the model.
+- A model prepared for benchmarking.
+
+With these ready, we can now run the model and obtain predictions.
+We'll use `supervision` to create a dataset iterator, and then run the model on each image.
+
+=== "Inference"
+
+    ```python
+    import supervision as sv
+
+    test_set = sv.DetectionDataset.from_yolo(
+        images_directory_path=f"{dataset.location}/test/images",
+        annotations_directory_path=f"{dataset.location}/test/labels",
+        data_yaml_path=f"{dataset.location}/data.yaml"
+    )
+
+    image_paths = []
+    predictions_list = []
+    targets_list = []
+
+    for image_path, image, label in test_set:
+        result = model.infer(image)[0]
+        predictions = sv.Detections.from_inference(result)
+
+        image_paths.append(image_path)
+        predictions_list.append(predictions)
+        targets_list.append(label)
+    ```
+
+=== "Ultralytics"
+
+    ```python
+    import supervision as sv
+
+    test_set = sv.DetectionDataset.from_yolo(
+        images_directory_path=f"{dataset.location}/test/images",
+        annotations_directory_path=f"{dataset.location}/test/labels",
+        data_yaml_path=f"{dataset.location}/data.yaml"
+    )
+
+    image_paths = []
+    predictions_list = []
+    targets_list = []
+
+    for image_path, image, label in test_set:
+        result = model(image)[0]
+        predictions = sv.Detections.from_ultralytics(result)
+
+        image_paths.append(image_path)
+        predictions_list.append(predictions)
+        targets_list.append(label)
+    ```
+
+## Remapping classes
+
+Did you notice an issue in the above logic?
+Since we're using an unrelated dataset, the class names and IDs may be different from what the model was trained on.
+
+We need to remap them to match the dataset classes. Here's how to do it:
+
+```python
+def remap_classes(
+    detections: sv.Detections,
+    class_ids_from_to: dict[int, int],
+    class_names_from_to: dict[str, str]
+) -> None:
+    new_class_ids = [
+        class_ids_from_to.get(class_id, class_id) for class_id in detections.class_id]
+    detections.class_id = np.array(new_class_ids)
+
+    new_class_names = [
+        class_names_from_to.get(name, name) for name in detections["class_name"]]
+    predictions["class_name"] = np.array(new_class_names)
+```
+
+Let's also remove the predictions that are not in the dataset classes.
+
+=== "Inference"
+
+    Dataset class names and IDs can be found in the `data.yaml` file, or by printing `dataset.classes`.
+
+    ```python
+    import supervision as sv
+
+    test_set = sv.DetectionDataset.from_yolo(
+        images_directory_path=f"{dataset.location}/test/images",
+        annotations_directory_path=f"{dataset.location}/test/labels",
+        data_yaml_path=f"{dataset.location}/data.yaml"
+    )
+
+    image_paths = []
+    predictions_list = []
+    targets_list = []
+
+    for image_path, image, label in test_set:
+        result = model.infer(image)[0]
+        predictions = sv.Detections.from_inference(result)
+
+        remap_classes(
+            detections=predictions,
+            class_ids_from_to={16: 0},
+            class_names_from_to={"dog": "Corgi"}
+        )
+        predictions = predictions[
+            np.isin(predictions["class_name"], test_set.classes)
+        ]
+
+        image_paths.append(image_path)
+        predictions_list.append(predictions)
+        targets_list.append(label)
+    ```
+
+=== "Ultralytics"
+
+    Dataset class names and IDs can be found in the `data.yaml` file, or by printing `dataset.classes`.
+
+    Each model will have a different class mapping, so make sure to check the model's documentation. In this case, the model was trained on the COCO dataset, with a class
+    configuration found [here](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8.yaml).
+
+    ```python
+    import supervision as sv
+
+    test_set = sv.DetectionDataset.from_yolo(
+        images_directory_path=f"{dataset.location}/test/images",
+        annotations_directory_path=f"{dataset.location}/test/labels",
+        data_yaml_path=f"{dataset.location}/data.yaml"
+    )
+
+    image_paths = []
+    predictions_list = []
+    targets_list = []
+
+    for image_path, image, label in test_set:
+        result = model(image)[0]
+        predictions = sv.Detections.from_ultralytics(result)
+
+        remap_classes(
+            detections=predictions,
+            class_ids_from_to={16: 0},
+            class_names_from_to={"dog": "Corgi"}
+        )
+        predictions = predictions[
+            np.isin(predictions["class_name"], test_set.classes)
+        ]
+
+        image_paths.append(image_path)
+        predictions_list.append(predictions)
+        targets_list.append(label)
+    ```
+
+## Visualizing Predictions
+
+The first step in evaluating your model’s performance is to visualize its predictions.
+This gives an intuitive sense of how well your model is detecting objects and where it might be failing.
+
+```python
+import supervision as sv
+
+N = 9
+GRID_SIZE = (3, 3)
+
+target_annotator = sv.PolygonAnnotator(color=sv.Color.from_hex("#8315f9"), thickness=8)
+prediction_annotator = sv.PolygonAnnotator(color=sv.Color.from_hex("#00cfc6"), thickness=6)
+
+
+annotated_images = []
+for image_path, predictions, targets in zip(
+  image_paths[:N], predictions_list[:N], targets_list[:N]
+):
+    annotated_image = cv2.imread(image_path)
+    annotated_image = target_annotator.annotate(scene=annotated_image, detections=targets)
+    annotated_image = prediction_annotator.annotate(scene=annotated_image, detections=prediction)
+    annotated_images.append(annotated_image)
+
+sv.plot_images_grid(images=annotated_images, grid_size=GRID_SIZE)
+```
+
+Here, predictions in purple are targets (ground truth), and predictions in teal are model predictions.
+
+![Basic Model Comparison](https://media.roboflow.com/supervision/image-examples/how-to/benchmark-models/basic-model-comparison-corgi.png)
+
+!!! tip
+
+    Use `sv.BoxAnnotator` for object detection and `sv.OrientedBoxAnnotator` for OBB.
+
+    See [annotator documentation](https://supervision.roboflow.com/latest/detection/annotators/) for even more options.
+
+## Benchmarking Metrics
+
+With multiple models, fine details matter. Visual inspection may not be enough. `supervision` provides a collection of metrics that help obtain precise numerical results of model performance.
+
+### Mean Average Precision (mAP)
+
+We'll start with [MeanAveragePrecision (mAP)](https://supervision.roboflow.com/latest/metrics/mean_average_precision/#supervision.metrics.mean_average_precision.MeanAveragePrecision), which is the most commonly used metric for object detection. It measures the average precision across all classes and IoU thresholds.
+
+For a thorough explanation, check out our [blog](https://blog.roboflow.com/mean-average-precision/) and [Youtube video](https://www.youtube.com/watch?v=oqXDdxF_Wuw).
+
+Here, the most popular value is `mAP 50:95`. It represents the average precision across all classes and IoU thresholds (`0.5` to `0.95`), whereas other values such as `mAP 50` or `mAP 75` only consider a single IoU threshold (`0.5` and `0.75` respectively).
+
+Let's compute the mAP:
+
+```python
+from supervision.metrics import MeanAveragePrecision, MetricTarget
+
+map_metric = MeanAveragePrecision(metric_target=MetricTarget.MASKS)
+map_result = map_metric.update(predictions_list, targets_list).compute()
+```
+
+Try printing the result to see it at a glance:
+
+```python
+print(map_result)
+```
+
+```
+MeanAveragePrecisionResult:
+Metric target: MetricTarget.MASKS
+Class agnostic: False
+mAP @ 50:95: 0.2409
+mAP @ 50:    0.3591
+mAP @ 75:    0.2915
+mAP scores: [0.35909 0.3468 0.34556 ...]
+IoU thresh: [0.5 0.55 0.6 ...]
+AP per class:
+  0: [0.35909 0.3468 0.34556 ...]
+...
+Small objects: ...
+Medium objects: ...
+Large objects: ...
+```
+
+You can also plot the results:
+
+```python
+map_result.plot()
+```
+
+![mAP Plot](https://media.roboflow.com/supervision/image-examples/how-to/benchmark-models/mAP-plot-corgi.png)
+
+The metric also breaks down the results by detected object area. Small, medium and large are simply those with area less than 32², between 32² and 96², and greater than 96² pixels respectively.
+
+### F1 Score
+
+The [F1 Score](https://supervision.roboflow.com/latest/metrics/f1_score/) is another useful metric, especially when dealing with an imbalance between false positives and false negatives. It’s the harmonic mean of **precision** (how many predictions are correct) and **recall** (how many actual instances were detected).
+
+Here's how you can compute the F1 score:
+
+```python
+from supervision.metrics import F1Score, MetricTarget
+
+f1_metric = F1Score(metric_target=MetricTarget.MASKS)
+f1_result = f1_metric.update(predictions_list, targets_list).compute()
+```
+
+As with mAP, you can also print the result:
+
+```python
+print(f1_result)
+```
+
+```
+F1ScoreResult:
+Metric target: MetricTarget.MASKS
+Averaging method: AveragingMethod.WEIGHTED
+F1 @ 50:     0.5341
+F1 @ 75:     0.4636
+F1 @ thresh: [0.53406 0.5278 0.52153 ...]
+IoU thresh: [0.5 0.55 0.6 ...]
+F1 per class:
+  0: [0.53406 0.5278 0.52153 ...]
+...
+Small objects: ...
+Medium objects: ...
+Large objects: ...
+```
+
+Similarly, you can plot the results:
+
+```python
+f1_result.plot()
+```
+
+![F1 Plot](https://media.roboflow.com/supervision/image-examples/how-to/benchmark-models/f1-score-corgi.png)
+
+As with mAP, the metric also breaks down the results by detected object area. Small, medium and large are simply those with area less than 32², between 32² and 96², and greater than 96² pixels respectively.
+
+## Model Leaderboard
+
+Here to compare the basic models? We've got you covered. Check out our [Model Leaderboard](https://leaderboard.roboflow.com/) to see how different models perform and to get a sense of the state-of-the-art results. It's a great place to understand what the leading models can achieve and to compare your own results.
+
+Even better, the repository is open source! You can see how the models were benchmarked, run the evaluation yourself, and even add your own models to the leaderboard. Check it out on [GitHub](https://github.com/roboflow/model-leaderboard)!
+
+![Model Leaderboard Example](https://media.roboflow.com/model-leaderboard/model-leaderboard-example.png)
+
+## Conclusion
+
+In this guide, you've learned how to set up your environment, train or use pre-trained models, visualize predictions, and evaluate model performance with metrics like [mAP](https://supervision.roboflow.com/latest/metrics/mean_average_precision/), [F1 score](https://supervision.roboflow.com/latest/metrics/f1_score/), and got to know our Model Leaderboard.
+
+A condensed version of this guide is also available as a [Colab Notebook](https://colab.research.google.com/drive/1HoOY9pZoVwGiRMmLHtir0qT6Uj45w6Ps?usp=sharing).
+
+For more details, be sure to check out our [documentation](https://supervision.roboflow.com/latest/) and join our community discussions. If you find any issues, please let us know on [GitHub](https://github.com/roboflow/supervision/issues).
+
+Best of luck with your benchmarking!

docs/how_to/process_datasets.md

@@ -331,12 +331,9 @@ for i in range(16):
     annotated_image = label_annotator.annotate(annotated_image, annotations, labels)
     annotated_images.append(annotated_image)
 
-grid = sv.create_tiles(
+sv.plot_images_grid(
     annotated_images,
     grid_size=(4, 4),
-    single_tile_size=(400, 400),
-    tile_padding_color=sv.Color.WHITE,
-    tile_margin_color=sv.Color.WHITE
 )
 ```
 

docs/how_to/track_objects.md

@@ -1,18 +1,21 @@
 ---
 comments: true
+status: new
 ---
 
 # Track Objects
 
 Leverage Supervision's advanced capabilities for enhancing your video analysis by
 seamlessly [tracking](/latest/trackers/) objects recognized by
-a multitude of object detection and segmentation models. This comprehensive guide will
+a multitude of object detection, segmentation and keypoint models. This comprehensive guide will
 take you through the steps to perform inference using the YOLOv8 model via either the
 [Inference](https://github.com/roboflow/inference) or
 [Ultralytics](https://github.com/ultralytics/ultralytics) packages. Following this,
 you'll discover how to track these objects efficiently and annotate your video content
 for a deeper analysis.
 
+## Object Detection & Segmentation
+
 To make it easier for you to follow our tutorial download the video we will use as an
 example. You can do this using
 [`supervision[assets]`](/latest/assets/) extension.
@@ -27,7 +30,7 @@ download_assets(VideoAssets.PEOPLE_WALKING)
     <source src="https://media.roboflow.com/supervision/video-examples/people-walking.mp4" type="video/mp4">
 </video>
 
-## Run Inference
+### Run Inference
 
 First, you'll need to obtain predictions from your object detection or segmentation
 model. In this tutorial, we are using the YOLOv8 model as an example. However,
@@ -40,14 +43,19 @@ by obtaining model predictions and then annotating the frame based on these pred
 This `callback` function will be essential in the subsequent steps of the tutorial, as
 it will be modified to include tracking, labeling, and trace annotations.
 
+!!! tip
+
+    Both object detection and segmentation models are supported. Try it with `yolov8n.pt` or `yolov8n-640-seg`!
+
 === "Ultralytics"
+
     ```{ .py }
     import numpy as np
     import supervision as sv
     from ultralytics import YOLO
 
     model = YOLO("yolov8n.pt")
-    box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
 
     def callback(frame: np.ndarray, _: int) -> np.ndarray:
         results = model(frame)[0]
@@ -62,13 +70,14 @@ it will be modified to include tracking, labeling, and trace annotations.
     ```
 
 === "Inference"
+
     ```{ .py }
     import numpy as np
     import supervision as sv
     from inference.models.utils import get_roboflow_model
 
     model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
-    box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
 
     def callback(frame: np.ndarray, _: int) -> np.ndarray:
         results = model.infer(frame)[0]
@@ -86,7 +95,7 @@ it will be modified to include tracking, labeling, and trace annotations.
     <source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/run-inference.mp4" type="video/mp4">
 </video>
 
-## Tracking
+### Tracking
 
 After running inference and obtaining predictions, the next step is to track the
 detected objects throughout the video. Utilizing Supervision’s
@@ -95,6 +104,7 @@ functionality, each detected object is assigned a unique tracker ID,
 enabling the continuous following of the object's motion path across different frames.
 
 === "Ultralytics"
+
     ```{ .py hl_lines="6 12" }
     import numpy as np
     import supervision as sv
@@ -102,7 +112,7 @@ enabling the continuous following of the object's motion path across different f
 
     model = YOLO("yolov8n.pt")
     tracker = sv.ByteTrack()
-    box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
 
     def callback(frame: np.ndarray, _: int) -> np.ndarray:
         results = model(frame)[0]
@@ -118,6 +128,7 @@ enabling the continuous following of the object's motion path across different f
     ```
 
 === "Inference"
+
     ```{ .py hl_lines="6 12" }
     import numpy as np
     import supervision as sv
@@ -125,7 +136,7 @@ enabling the continuous following of the object's motion path across different f
 
     model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
     tracker = sv.ByteTrack()
-    box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
 
     def callback(frame: np.ndarray, _: int) -> np.ndarray:
         results = model.infer(frame)[0]
@@ -140,7 +151,7 @@ enabling the continuous following of the object's motion path across different f
     )
     ```
 
-## Annotate Video with Tracking IDs
+### Annotate Video with Tracking IDs
 
 Annotating the video with tracking IDs helps in distinguishing and following each object
 distinctly. With the
@@ -149,6 +160,7 @@ in Supervision, we can overlay the tracker IDs and class labels on the detected
 offering a clear visual representation of each object's class and unique identifier.
 
 === "Ultralytics"
+
     ```{ .py hl_lines="8 15-19 23-24" }
     import numpy as np
     import supervision as sv
@@ -156,7 +168,7 @@ offering a clear visual representation of each object's class and unique identif
 
     model = YOLO("yolov8n.pt")
     tracker = sv.ByteTrack()
-    box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
     label_annotator = sv.LabelAnnotator()
 
     def callback(frame: np.ndarray, _: int) -> np.ndarray:
@@ -165,9 +177,9 @@ offering a clear visual representation of each object's class and unique identif
         detections = tracker.update_with_detections(detections)
 
         labels = [
-            f"#{tracker_id} {results.names[class_id]}"
-            for class_id, tracker_id
-            in zip(detections.class_id, detections.tracker_id)
+            f"#{tracker_id} {class_name}"
+            for class_name, tracker_id
+            in zip(detections.data["class_name"], detections.tracker_id)
         ]
 
         annotated_frame = box_annotator.annotate(
@@ -183,6 +195,7 @@ offering a clear visual representation of each object's class and unique identif
     ```
 
 === "Inference"
+
     ```{ .py hl_lines="8 15-19 23-24" }
     import numpy as np
     import supervision as sv
@@ -190,7 +203,7 @@ offering a clear visual representation of each object's class and unique identif
 
     model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
     tracker = sv.ByteTrack()
-    box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
     label_annotator = sv.LabelAnnotator()
 
     def callback(frame: np.ndarray, _: int) -> np.ndarray:
@@ -199,9 +212,9 @@ offering a clear visual representation of each object's class and unique identif
         detections = tracker.update_with_detections(detections)
 
         labels = [
-            f"#{tracker_id} {results.names[class_id]}"
-            for class_id, tracker_id
-            in zip(detections.class_id, detections.tracker_id)
+            f"#{tracker_id} {class_name}"
+            for class_name, tracker_id
+            in zip(detections.data["class_name"], detections.tracker_id)
         ]
 
         annotated_frame = box_annotator.annotate(
@@ -220,7 +233,7 @@ offering a clear visual representation of each object's class and unique identif
     <source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/annotate-video-with-tracking-ids.mp4" type="video/mp4">
 </video>
 
-## Annotate Video with Traces
+### Annotate Video with Traces
 
 Adding traces to the video involves overlaying the historical paths of the detected
 objects. This feature, powered by the
@@ -229,6 +242,7 @@ allows for visualizing the trajectories of objects, helping in understanding the
 movement patterns and interactions between objects in the video.
 
 === "Ultralytics"
+
     ```{ .py hl_lines="9 26-27" }
     import numpy as np
     import supervision as sv
@@ -236,7 +250,7 @@ movement patterns and interactions between objects in the video.
 
     model = YOLO("yolov8n.pt")
     tracker = sv.ByteTrack()
-    box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
     label_annotator = sv.LabelAnnotator()
     trace_annotator = sv.TraceAnnotator()
 
@@ -246,9 +260,9 @@ movement patterns and interactions between objects in the video.
         detections = tracker.update_with_detections(detections)
 
         labels = [
-            f"#{tracker_id} {results.names[class_id]}"
-            for class_id, tracker_id
-            in zip(detections.class_id, detections.tracker_id)
+            f"#{tracker_id} {class_name}"
+            for class_name, tracker_id
+            in zip(detections.data["class_name"], detections.tracker_id)
         ]
 
         annotated_frame = box_annotator.annotate(
@@ -266,6 +280,7 @@ movement patterns and interactions between objects in the video.
     ```
 
 === "Inference"
+
     ```{ .py hl_lines="9 26-27" }
     import numpy as np
     import supervision as sv
@@ -273,7 +288,7 @@ movement patterns and interactions between objects in the video.
 
     model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
     tracker = sv.ByteTrack()
-    box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
     label_annotator = sv.LabelAnnotator()
     trace_annotator = sv.TraceAnnotator()
 
@@ -283,9 +298,9 @@ movement patterns and interactions between objects in the video.
         detections = tracker.update_with_detections(detections)
 
         labels = [
-            f"#{tracker_id} {results.names[class_id]}"
-            for class_id, tracker_id
-            in zip(detections.class_id, detections.tracker_id)
+            f"#{tracker_id} {class_name}"
+            for class_name, tracker_id
+            in zip(detections.data["class_name"], detections.tracker_id)
         ]
 
         annotated_frame = box_annotator.annotate(
@@ -306,6 +321,336 @@ movement patterns and interactions between objects in the video.
     <source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/annotate-video-with-traces.mp4" type="video/mp4">
 </video>
 
-This structured walkthrough should give a detailed pathway to annotate videos
-effectively using Supervision’s various functionalities, including object tracking and
-trace annotations.
+## Keypoints
+
+Models aren't limited to object detection and segmentation. Keypoint detection allows for detailed analysis of body joints and connections, especially valuable for applications like human pose estimation. This section introduces keypoint tracking. We'll walk through the steps of annotating keypoints, converting them into bounding box detections compatible with `ByteTrack`, and applying detection smoothing for enhanced stability.
+
+To make it easier for you to follow our tutorial, let's download the video we will use as an
+example. You can do this using [`supervision[assets]`](/latest/assets/) extension.
+
+```python
+from supervision.assets import download_assets, VideoAssets
+
+download_assets(VideoAssets.SKIING)
+```
+
+<video controls muted>
+    <source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/skiing-hd.mp4" type="video/mp4">
+</video>
+
+### Keypoint Detection
+
+First, you'll need to obtain predictions from your keypoint detection model. In this tutorial, we are using the YOLOv8 model as an example. However,
+Supervision is versatile and compatible with various models. Check this [link](/latest/keypoint/core/) for guidance on how to plug in other models.
+
+We will define a `callback` function, which will process each frame of the video by obtaining model predictions and then annotating the frame based on these predictions.
+
+Let's immediately visualize the results with our [`EdgeAnnotator`](/latest/keypoint/annotators/#supervision.key_points.annotators.EdgeAnnotator) and [`VertexAnnotator`](https://supervision.roboflow.com/latest/keypoint/annotators/#supervision.key_points.annotators.VertexAnnotator).
+
+=== "Ultralytics"
+
+    ```{ .py hl_lines="5 10-11" }
+    import numpy as np
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8m-pose.pt")
+    edge_annotator = sv.EdgeAnnotator()
+    vertex_annotator = sv.VertexAnnotator()
+
+    def callback(frame: np.ndarray, _: int) -> np.ndarray:
+        results = model(frame)[0]
+        key_points = sv.KeyPoints.from_ultralytics(results)
+
+        annotated_frame = edge_annotator.annotate(
+            frame.copy(), key_points=key_points)
+        return vertex_annotator.annotate(
+            annotated_frame, key_points=key_points)
+
+    sv.process_video(
+        source_path="skiing.mp4",
+        target_path="result.mp4",
+        callback=callback
+    )
+    ```
+
+=== "Inference"
+
+    ```{ .py hl_lines="5-6 11-12" }
+    import numpy as np
+    import supervision as sv
+    from inference.models.utils import get_roboflow_model
+
+    model = get_roboflow_model(
+        model_id="yolov8m-pose-640", api_key=<ROBOFLOW API KEY>)
+    edge_annotator = sv.EdgeAnnotator()
+    vertex_annotator = sv.VertexAnnotator()
+
+    def callback(frame: np.ndarray, _: int) -> np.ndarray:
+        results = model.infer(frame)[0]
+        key_points = sv.KeyPoints.from_inference(results)
+
+        annotated_frame = edge_annotator.annotate(
+            frame.copy(), key_points=key_points)
+        return vertex_annotator.annotate(
+            annotated_frame, key_points=key_points)
+
+    sv.process_video(
+        source_path="skiing.mp4",
+        target_path="result.mp4",
+        callback=callback
+    )
+    ```
+
+<video controls>
+    <source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/track-keypoints-only-keypoints.mp4" type="video/mp4">
+</video>
+
+### Convert to Detections
+
+Keypoint tracking is currently supported via the conversion of `KeyPoints` to `Detections`. This is achieved with the [`KeyPoints.as_detections()`](/latest/keypoint/core/#supervision.key_points.core.KeyPoints.as_detections) function.
+
+Let's convert to detections and visualize the results with our [`BoxAnnotator`](/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator).
+
+!!! tip
+
+    You may use the `selected_keypoint_indices` argument to specify a subset of keypoints to convert. This is useful when some keypoints could be occluded. For example: a person might swing their arm, causing the elbow to be occluded by the torso sometimes.
+
+=== "Ultralytics"
+
+    ```{ .py hl_lines="8 13 19-20" }
+    import numpy as np
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8m-pose.pt")
+    edge_annotator = sv.EdgeAnnotator()
+    vertex_annotator = sv.VertexAnnotator()
+    box_annotator = sv.BoxAnnotator()
+
+    def callback(frame: np.ndarray, _: int) -> np.ndarray:
+        results = model(frame)[0]
+        key_points = sv.KeyPoints.from_ultralytics(results)
+        detections = key_points.as_detections()
+
+        annotated_frame = edge_annotator.annotate(
+            frame.copy(), key_points=key_points)
+        annotated_frame = vertex_annotator.annotate(
+            annotated_frame, key_points=key_points)
+        return box_annotator.annotate(
+            annotated_frame, detections=detections)
+
+    sv.process_video(
+        source_path="skiing.mp4",
+        target_path="result.mp4",
+        callback=callback
+    )
+    ```
+
+=== "Inference"
+
+    ```{ .py hl_lines="9 14 20-21" }
+    import numpy as np
+    import supervision as sv
+    from inference.models.utils import get_roboflow_model
+
+    model = get_roboflow_model(
+        model_id="yolov8m-pose-640", api_key=<ROBOFLOW API KEY>)
+    edge_annotator = sv.EdgeAnnotator()
+    vertex_annotator = sv.VertexAnnotator()
+    box_annotator = sv.BoxAnnotator()
+
+    def callback(frame: np.ndarray, _: int) -> np.ndarray:
+        results = model.infer(frame)[0]
+        key_points = sv.KeyPoints.from_inference(results)
+        detections = key_points.as_detections()
+
+        annotated_frame = edge_annotator.annotate(
+            frame.copy(), key_points=key_points)
+        annotated_frame = vertex_annotator.annotate(
+            annotated_frame, key_points=key_points)
+        return box_annotator.annotate(
+            annotated_frame, detections=detections)
+
+    sv.process_video(
+        source_path="skiing.mp4",
+        target_path="result.mp4",
+        callback=callback
+    )
+    ```
+
+<video controls>
+    <source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/track-keypoints-converted-to-detections.mp4" type="video/mp4">
+</video>
+
+### Keypoint Tracking
+
+Now that we have a `Detections` object, we can track it throughout the video. Utilizing Supervision’s [`sv.ByteTrack`](/latest/trackers/#supervision.tracker.byte_tracker.core.ByteTrack) functionality, each detected object is assigned a unique tracker ID, enabling the continuous following of the object's motion path across different frames. We shall visualize the result with `TraceAnnotator`.
+
+=== "Ultralytics"
+
+    ```{ .py hl_lines="10-11 17 25-26" }
+    import numpy as np
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8m-pose.pt")
+    edge_annotator = sv.EdgeAnnotator()
+    vertex_annotator = sv.VertexAnnotator()
+    box_annotator = sv.BoxAnnotator()
+
+    tracker = sv.ByteTrack()
+    trace_annotator = sv.TraceAnnotator()
+
+    def callback(frame: np.ndarray, _: int) -> np.ndarray:
+        results = model(frame)[0]
+        key_points = sv.KeyPoints.from_ultralytics(results)
+        detections = key_points.as_detections()
+        detections = tracker.update_with_detections(detections)
+
+        annotated_frame = edge_annotator.annotate(
+            frame.copy(), key_points=key_points)
+        annotated_frame = vertex_annotator.annotate(
+            annotated_frame, key_points=key_points)
+        annotated_frame = box_annotator.annotate(
+            annotated_frame, detections=detections)
+        return trace_annotator.annotate(
+            annotated_frame, detections=detections)
+
+    sv.process_video(
+        source_path="skiing.mp4",
+        target_path="result.mp4",
+        callback=callback
+    )
+    ```
+
+=== "Inference"
+
+    ```{ .py hl_lines="11-12 18 26-27" }
+    import numpy as np
+    import supervision as sv
+    from inference.models.utils import get_roboflow_model
+
+    model = get_roboflow_model(
+        model_id="yolov8m-pose-640", api_key=<ROBOFLOW API KEY>)
+    edge_annotator = sv.EdgeAnnotator()
+    vertex_annotator = sv.VertexAnnotator()
+    box_annotator = sv.BoxAnnotator()
+
+    tracker = sv.ByteTrack()
+    trace_annotator = sv.TraceAnnotator()
+
+    def callback(frame: np.ndarray, _: int) -> np.ndarray:
+        results = model.infer(frame)[0]
+        key_points = sv.KeyPoints.from_inference(results)
+        detections = key_points.as_detections()
+        detections = tracker.update_with_detections(detections)
+
+        annotated_frame = edge_annotator.annotate(
+            frame.copy(), key_points=key_points)
+        annotated_frame = vertex_annotator.annotate(
+            annotated_frame, key_points=key_points)
+        annotated_frame = box_annotator.annotate(
+            annotated_frame, detections=detections)
+        return trace_annotator.annotate(
+            annotated_frame, detections=detections)
+
+    sv.process_video(
+        source_path="skiing.mp4",
+        target_path="result.mp4",
+        callback=callback
+    )
+    ```
+
+<video controls>
+    <source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/track-keypoints-with-tracking.mp4" type="video/mp4">
+</video>
+
+### Bonus: Smoothing
+
+We could stop here as we have successfully tracked the object detected by the keypoint model. However, we can further enhance the stability of the boxes by applying [`DetectionsSmoother`](/latest/detection/tools/smoother/). This tool helps in stabilizing the boxes by smoothing the bounding box coordinates across frames. It is very simple to use:
+
+=== "Ultralytics"
+
+    ```{ .py hl_lines="11 19" }
+    import numpy as np
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8m-pose.pt")
+    edge_annotator = sv.EdgeAnnotator()
+    vertex_annotator = sv.VertexAnnotator()
+    box_annotator = sv.BoxAnnotator()
+
+    tracker = sv.ByteTrack()
+    smoother = sv.DetectionsSmoother()
+    trace_annotator = sv.TraceAnnotator()
+
+    def callback(frame: np.ndarray, _: int) -> np.ndarray:
+        results = model(frame)[0]
+        key_points = sv.KeyPoints.from_ultralytics(results)
+        detections = key_points.as_detections()
+        detections = tracker.update_with_detections(detections)
+        detections = smoother.update_with_detections(detections)
+
+        annotated_frame = edge_annotator.annotate(
+            frame.copy(), key_points=key_points)
+        annotated_frame = vertex_annotator.annotate(
+            annotated_frame, key_points=key_points)
+        annotated_frame = box_annotator.annotate(
+            annotated_frame, detections=detections)
+        return trace_annotator.annotate(
+            annotated_frame, detections=detections)
+
+    sv.process_video(
+        source_path="skiing.mp4",
+        target_path="result.mp4",
+        callback=callback
+    )
+    ```
+
+=== "Inference"
+
+    ```{ .py hl_lines="12 20" }
+    import numpy as np
+    import supervision as sv
+    from inference.models.utils import get_roboflow_model
+
+    model = get_roboflow_model(
+        model_id="yolov8m-pose-640", api_key=<ROBOFLOW API KEY>)
+    edge_annotator = sv.EdgeAnnotator()
+    vertex_annotator = sv.VertexAnnotator()
+    box_annotator = sv.BoxAnnotator()
+
+    tracker = sv.ByteTrack()
+    smoother = sv.DetectionsSmoother()
+    trace_annotator = sv.TraceAnnotator()
+
+    def callback(frame: np.ndarray, _: int) -> np.ndarray:
+        results = model.infer(frame)[0]
+        key_points = sv.KeyPoints.from_inference(results)
+        detections = key_points.as_detections()
+        detections = tracker.update_with_detections(detections)
+        detections = smoother.update_with_detections(detections)
+
+        annotated_frame = edge_annotator.annotate(
+            frame.copy(), key_points=key_points)
+        annotated_frame = vertex_annotator.annotate(
+            annotated_frame, key_points=key_points)
+        annotated_frame = box_annotator.annotate(
+            annotated_frame, detections=detections)
+        return trace_annotator.annotate(
+            annotated_frame, detections=detections)
+
+    sv.process_video(
+        source_path="skiing.mp4",
+        target_path="result.mp4",
+        callback=callback
+    )
+    ```
+
+<video controls>
+    <source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/track-keypoints-with-smoothing.mp4" type="video/mp4">
+</video>
+
+This structured walkthrough should give a detailed pathway to annotate videos effectively using Supervision’s various functionalities, including object tracking and trace annotations.

docs/index.md

@@ -10,13 +10,19 @@ hide:
   <h1></h1>
 </div>
 
-<div align="center" id="logo">
+<div align="center" id="logo" style="padding-top: 1rem;">
   <a align="center" href="" target="_blank">
       <img width="850"
           src="https://media.roboflow.com/open-source/supervision/rf-supervision-banner.png?updatedAt=1678995927529">
   </a>
 </div>
 
+<style>
+    #hello {
+        margin: 0;
+    }
+</style>
+
 ## 👋 Hello
 
 We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us!
@@ -31,10 +37,11 @@ We write your reusable computer vision tools. Whether you need to load your data
 ## 💻 Install
 
 You can install `supervision` in a
-[**Python>=3.8**](https://www.python.org/) environment.
+[**Python>=3.9**](https://www.python.org/) environment.
 
-!!! example "pip install (recommended)"
-    === "pip"
+!!! example "Installation"
+
+    === "pip (recommended)"
         [![version](https://badge.fury.io/py/supervision.svg)](https://badge.fury.io/py/supervision)
         [![downloads](https://img.shields.io/pypi/dm/supervision)](https://pypistats.org/packages/supervision)
         [![license](https://img.shields.io/pypi/l/supervision)](https://github.com/roboflow/supervision/blob/main/LICENSE.md)
@@ -44,6 +51,43 @@ You can install `supervision` in a
         pip install supervision
         ```
 
+    === "poetry"
+        [![version](https://badge.fury.io/py/supervision.svg)](https://badge.fury.io/py/supervision)
+        [![downloads](https://img.shields.io/pypi/dm/supervision)](https://pypistats.org/packages/supervision)
+        [![license](https://img.shields.io/pypi/l/supervision)](https://github.com/roboflow/supervision/blob/main/LICENSE.md)
+        [![python-version](https://img.shields.io/pypi/pyversions/supervision)](https://badge.fury.io/py/supervision)
+
+        ```bash
+        poetry add supervision
+        ```
+
+    === "uv"
+        [![version](https://badge.fury.io/py/supervision.svg)](https://badge.fury.io/py/supervision)
+        [![downloads](https://img.shields.io/pypi/dm/supervision)](https://pypistats.org/packages/supervision)
+        [![license](https://img.shields.io/pypi/l/supervision)](https://github.com/roboflow/supervision/blob/main/LICENSE.md)
+        [![python-version](https://img.shields.io/pypi/pyversions/supervision)](https://badge.fury.io/py/supervision)
+
+        ```bash
+        uv pip install supervision
+        ```
+
+        For uv projects:
+
+        ```bash
+        uv add supervision
+        ```
+
+    === "rye"
+        [![version](https://badge.fury.io/py/supervision.svg)](https://badge.fury.io/py/supervision)
+        [![downloads](https://img.shields.io/pypi/dm/supervision)](https://pypistats.org/packages/supervision)
+        [![license](https://img.shields.io/pypi/l/supervision)](https://github.com/roboflow/supervision/blob/main/LICENSE.md)
+        [![python-version](https://img.shields.io/pypi/pyversions/supervision)](https://badge.fury.io/py/supervision)
+
+        ```bash
+        rye add supervision
+        ```
+
+
 !!! example "conda/mamba install"
     === "conda"
         [![conda-recipe](https://img.shields.io/badge/recipe-supervision-green.svg)](https://anaconda.org/conda-forge/supervision) [![conda-downloads](https://img.shields.io/conda/dn/conda-forge/supervision.svg)](https://anaconda.org/conda-forge/supervision) [![conda-version](https://img.shields.io/conda/vn/conda-forge/supervision.svg)](https://anaconda.org/conda-forge/supervision) [![conda-platforms](https://img.shields.io/conda/pn/conda-forge/supervision.svg)](https://anaconda.org/conda-forge/supervision)
@@ -63,7 +107,7 @@ You can install `supervision` in a
     === "virtualenv"
         ```bash
         # clone repository and navigate to root directory
-        git clone https://github.com/roboflow/supervision.git
+        git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
         cd supervision
 
         # setup python environment and activate it
@@ -75,18 +119,19 @@ You can install `supervision` in a
         pip install -e "."
         ```
 
-    === "poetry"
+    === "uv"
         ```bash
         # clone repository and navigate to root directory
-        git clone https://github.com/roboflow/supervision.git
+        git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
         cd supervision
 
         # setup python environment and activate it
-        poetry env use python3.10
-        poetry shell
+        uv venv
+        source .venv/bin/activate
 
         # installation
-        poetry install
+        uv pip install -r pyproject.toml -e . --all-extras
+
         ```
 
 ## 🚀 Quickstart

docs/javascripts/cookbooks-card.js

@@ -59,7 +59,7 @@ document.addEventListener("DOMContentLoaded", function () {
             <div
                 class="author-container"
                 data-login="${authorData.login}-${elementIndex}"
-                style="margin-left: ${marginLeft}; z-index: ${zIndex};"
+                style="margin-left: ${marginLeft};"
             >
                 <a
                     href="https://github.com/${authorData.login}"
@@ -90,14 +90,17 @@ document.addEventListener("DOMContentLoaded", function () {
         ).join(',&nbsp;');
 
         let authorsHTML = `
-        <div class="authors">
+        <div class="authors" style="margin: 0;">
             ${authorAvatarsHTML}
             <div class="author-names">${authorNamesHTML}</div>
         </div>
         `;
 
         element.innerText = `
-            <div style="flex-direction: column; height: 100%; display: flex;
+            <div style="
+                display: grid !important;
+                grid-template-rows: auto;
+                height: 100%;
             font-family: -apple-system,BlinkMacSystemFont,Segoe UI,Helvetica,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji; background: ${theme.background}; font-size: 14px; line-height: 1.5; color: ${theme.color}">
             <div style="display: flex; align-items: center;">
                 <span style="font-weight: 700; font-size: 1rem; color: ${theme.linkColor};">
@@ -105,13 +108,14 @@ document.addEventListener("DOMContentLoaded", function () {
                 </span>
             </div>
             ${authorsHTML}
-            <div style="font-size: 12px; color: ${theme.color}; display: flex; flex: 0; justify-content: space-between">
+            <div style="font-size: 12px; color: ${theme.color}; display: grid; grid-template-columns: auto 3fr; justify-content: space-between; gap: 1rem;">
                 <div style="display: flex; align-items: center;">
                 <img src="/assets/supervision-lenny.png" aria-label="stars" width="20" height="20" role="img" />
                 &nbsp;
                 <span style="margin-left: 4px">${version}</span>
                 </div>
-                <div style="display: flex; align-items: center; flex-wrap: wrap">
+                <div style="display: flex; align-items: center; flex-wrap: wrap; align-content: right;
+  gap: 0.1rem;">
                 ${labelHTML}
                 </div>
             </div>

docs/javascripts/mathjax.js

@@ -0,0 +1,19 @@
+window.MathJax = {
+    tex: {
+      inlineMath: [["\\(", "\\)"]],
+      displayMath: [["\\[", "\\]"]],
+      processEscapes: true,
+      processEnvironments: true
+    },
+    options: {
+      ignoreHtmlClass: ".*|",
+      processHtmlClass: "arithmatex"
+    }
+  };
+
+  document$.subscribe(() => {
+    MathJax.startup.output.clearCache()
+    MathJax.typesetClear()
+    MathJax.texReset()
+    MathJax.typesetPromise()
+  })

docs/javascripts/segment.js

@@ -0,0 +1,4 @@
+!function(){var i="analytics",analytics=window[i]=window[i]||[];if(!analytics.initialize)if(analytics.invoked)window.console&&console.error&&console.error("Segment snippet included twice.");else{analytics.invoked=!0;analytics.methods=["trackSubmit","trackClick","trackLink","trackForm","pageview","identify","reset","group","track","ready","alias","debug","page","screen","once","off","on","addSourceMiddleware","addIntegrationMiddleware","setAnonymousId","addDestinationMiddleware","register"];analytics.factory=function(e){return function(){if(window[i].initialized)return window[i][e].apply(window[i],arguments);var n=Array.prototype.slice.call(arguments);if(["track","screen","alias","group","page","identify"].indexOf(e)>-1){var c=document.querySelector("link[rel='canonical']");n.push({__t:"bpc",c:c&&c.getAttribute("href")||void 0,p:location.pathname,u:location.href,s:location.search,t:document.title,r:document.referrer})}n.unshift(e);analytics.push(n);return analytics}};for(var n=0;n<analytics.methods.length;n++){var key=analytics.methods[n];analytics[key]=analytics.factory(key)}analytics.load=function(key,n){var t=document.createElement("script");t.type="text/javascript";t.async=!0;t.setAttribute("data-global-segment-analytics-key",i);t.src="https://cdn.segment.com/analytics.js/v1/" + key + "/analytics.min.js";var r=document.getElementsByTagName("script")[0];r.parentNode.insertBefore(t,r);analytics._loadOptions=n};analytics._writeKey="rMvrPeZBJYyOPJSGCNhMlnTJb8VhFiWU";;analytics.SNIPPET_VERSION="5.2.0";
+  analytics.load("eohFog7VZiAhGJGEr5Sh7BM1mFKmUvDC");
+  document$.subscribe(analytics.page);
+  }}();

docs/keypoint/annotators.md

@@ -78,19 +78,19 @@ comments: true
     </div>
 
 <div class="md-typeset">
-  <h2><a href="#supervision.keypoint.annotators.VertexAnnotator">VertexAnnotator</a></h2>
+  <h2><a href="#supervision.key_points.annotators.VertexAnnotator">VertexAnnotator</a></h2>
 </div>
 
-:::supervision.keypoint.annotators.VertexAnnotator
+:::supervision.key_points.annotators.VertexAnnotator
 
 <div class="md-typeset">
-  <h2><a href="#supervision.keypoint.annotators.EdgeAnnotator">EdgeAnnotator</a></h2>
+  <h2><a href="#supervision.key_points.annotators.EdgeAnnotator">EdgeAnnotator</a></h2>
 </div>
 
-:::supervision.keypoint.annotators.EdgeAnnotator
+:::supervision.key_points.annotators.EdgeAnnotator
 
 <div class="md-typeset">
-  <h2><a href="#supervision.keypoint.annotators.VertexLabelAnnotator">VertexLabelAnnotator</a></h2>
+  <h2><a href="#supervision.key_points.annotators.VertexLabelAnnotator">VertexLabelAnnotator</a></h2>
 </div>
 
-:::supervision.keypoint.annotators.VertexLabelAnnotator
+:::supervision.key_points.annotators.VertexLabelAnnotator

docs/keypoint/core.md

@@ -1,7 +1,8 @@
 ---
 comments: true
+status: new
 ---
 
 # Keypoint Detection
 
-:::supervision.keypoint.core.KeyPoints
+:::supervision.key_points.core.KeyPoints

docs/metrics/common_values.md

@@ -0,0 +1,19 @@
+---
+comments: true
+---
+
+# Common Values
+
+This page contains supplementary values, types and enums that metrics use.
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.core.MetricTarget">MetricTarget</a></h2>
+</div>
+
+:::supervision.metrics.core.MetricTarget
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.core.AveragingMethod">AveragingMethod</a></h2>
+</div>
+
+:::supervision.metrics.core.AveragingMethod

docs/metrics/f1_score.md

@@ -1,6 +1,5 @@
 ---
 comments: true
-status: new
 ---
 
 # F1 Score

docs/metrics/mean_average_precision.md

@@ -16,3 +16,9 @@ status: new
 </div>
 
 :::supervision.metrics.mean_average_precision.MeanAveragePrecisionResult
+
+<div class="md-typeset">
+    <h2><a href="#supervision.dataset.formats.coco.get_coco_class_index_mapping">get_coco_class_index_mapping</a></h2>
+</div>
+
+:::supervision.dataset.formats.coco.get_coco_class_index_mapping

docs/metrics/mean_average_recall.md

@@ -0,0 +1,17 @@
+---
+comments: true
+---
+
+# Mean Average Recall
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.mean_average_recall.MeanAverageRecall">MeanAverageRecall</a></h2>
+</div>
+
+:::supervision.metrics.mean_average_recall.MeanAverageRecall
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.mean_average_recall.MeanAverageRecallResult">MeanAverageRecallResult</a></h2>
+</div>
+
+:::supervision.metrics.mean_average_recall.MeanAverageRecallResult

docs/metrics/precision.md

@@ -0,0 +1,17 @@
+---
+comments: true
+---
+
+# Precision
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.precision.Precision">Precision</a></h2>
+</div>
+
+:::supervision.metrics.precision.Precision
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.precision.PrecisionResult">PrecisionResult</a></h2>
+</div>
+
+:::supervision.metrics.precision.PrecisionResult

docs/metrics/recall.md

@@ -0,0 +1,17 @@
+---
+comments: true
+---
+
+# Recall
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.recall.Recall">Recall</a></h2>
+</div>
+
+:::supervision.metrics.recall.Recall
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.recall.RecallResult">RecallResult</a></h2>
+</div>
+
+:::supervision.metrics.recall.RecallResult

docs/notebooks/annotate-video-with-detections.ipynb

@@ -93,7 +93,7 @@
             },
             "outputs": [],
             "source": [
-                "!pip install -q inference-gpu \"supervision[assets]\""
+                "!pip install -q inference-gpu \"supervision\""
             ]
         },
         {
@@ -156,7 +156,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 4,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/"
@@ -207,7 +207,6 @@
             ],
             "source": [
                 "import supervision as sv\n",
-                "from supervision.assets import download_assets, VideoAssets\n",
                 "from inference.models.utils import get_roboflow_model\n",
                 "\n",
                 "\n",

docs/notebooks/download-supervision-assets.ipynb

@@ -17,7 +17,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 10,
+            "execution_count": null,
             "metadata": {
                 "vscode": {
                     "languageId": "shellscript"
@@ -25,7 +25,7 @@
             },
             "outputs": [],
             "source": [
-                "!pip install -q \"supervision[assets]\""
+                "pip install -q \"supervision\""
             ]
         },
         {

docs/notebooks/evaluating-alignment-of-text-to-image-diffusion-models.ipynb

@@ -101,7 +101,7 @@
             },
             "outputs": [],
             "source": [
-                "!pip install -q torch diffusers accelerate inference-gpu[yolo-world] dill git+https://github.com/openai/CLIP.git supervision==0.19.0rc5"
+                "!pip install -q torch diffusers accelerate inference-gpu[yolo-world] dill git+https://github.com/openai/CLIP.git supervision"
             ]
         },
         {

docs/notebooks/object-tracking.ipynb

@@ -92,7 +92,7 @@
             },
             "outputs": [],
             "source": [
-                "!pip install -q inference-gpu \"supervision[assets]\""
+                "!pip install -q inference-gpu \"supervision\""
             ]
         },
         {

docs/notebooks/occupancy_analytics.ipynb

@@ -72,7 +72,7 @@
             },
             "outputs": [],
             "source": [
-                "!pip install roboflow supervision==0.19.0 -q"
+                "!pip install roboflow supervision -q"
             ]
         },
         {

docs/notebooks/quickstart.ipynb

@@ -495,7 +495,7 @@
             "source": [
                 "from ultralytics import YOLO\n",
                 "\n",
-                "model = YOLO(\"yolov8x.pt\")\n",
+                "model = YOLO(\"yolo11x.pt\")\n",
                 "result = model(image, verbose=False)[0]\n",
                 "detections = sv.Detections.from_ultralytics(result)"
             ]
@@ -594,7 +594,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 33,
+            "execution_count": null,
             "metadata": {
                 "id": "yM6dmicTRGl6"
             },
@@ -602,7 +602,7 @@
             "source": [
                 "from ultralytics import YOLO\n",
                 "\n",
-                "model = YOLO(\"yolov8x-seg.pt\")\n",
+                "model = YOLO(\"yolo11x-seg.pt\")\n",
                 "result = model(image, verbose=False)[0]\n",
                 "detections = sv.Detections.from_ultralytics(result)"
             ]
@@ -926,7 +926,7 @@
             },
             "outputs": [],
             "source": [
-                "!pip install -q supervision[assets]"
+                "!pip install -q supervision"
             ]
         },
         {

docs/notebooks/serialise-detections-to-json.ipynb

@@ -32,7 +32,7 @@
             },
             "outputs": [],
             "source": [
-                "!pip install -q inference requests tqdm supervision==0.21.0"
+                "!pip install -q inference requests tqdm supervision"
             ]
         },
         {
@@ -695,12 +695,12 @@
             "source": [
                 "###\u00a0Annotate Image with Detections\n",
                 "\n",
-                "Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoundingBoxAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.BoundingBoxAnnotator) and [`sv.LabelAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator) classes."
+                "Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoxAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`sv.LabelAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator) classes."
             ]
         },
         {
             "cell_type": "code",
-            "execution_count": 49,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/",
@@ -722,7 +722,7 @@
                 }
             ],
             "source": [
-                "bounding_box_annotator = sv.BoundingBoxAnnotator()\n",
+                "bounding_box_annotator = sv.BoxAnnotator()\n",
                 "label_annotator = sv.LabelAnnotator()\n",
                 "\n",
                 "annotated_frame = frame.copy()\n",

docs/notebooks/small-object-detection-with-sahi.ipynb

@@ -15,7 +15,7 @@
                 "\n",
                 "This cookbook shows how to use [Slicing Aided Hyper Inference (SAHI) ](https://arxiv.org/abs/2202.06934) for small object detection with `supervision`.\n",
                 "\n",
-                "![\"Small Object Detection\"](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/animation.gif \"Small Object Detection\")\n",
+                "![\"Small Object Detection\"](https://media.roboflow.com/supervision/cookbooks/sahi/animation.gif \"Small Object Detection\")\n",
                 "\n",
                 "Click the Open in Colab button to run the cookbook on Google Colab.\n",
                 "\n",
@@ -70,7 +70,7 @@
                 "\n",
                 "Detecting people (or their heads) is a common problem that has been addressed by many researchers in the past. In this project, we\u2019ll use an open-source public dataset and a fine-tuned model to perform inference on images.\n",
                 "\n",
-                "![Roboflow Universe](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/roboflow_universe.png \"Open source model for counting people's heads\")\n",
+                "![Roboflow Universe](https://media.roboflow.com/supervision/cookbooks/sahi/roboflow_universe.png \"Open source model for counting people's heads\")\n",
                 "\n",
                 "Some details about the project [\"people_counterv0 Computer Vision Project\"](https://universe.roboflow.com/sit-cx0ng/people_counterv0):\n",
                 "\n",
@@ -782,9 +782,9 @@
                 "\n",
                 "| Example| Observations   |\n",
                 "|----|----|\n",
-                "| ![Overlapping](https://github.com/ediardo/notebooks/blob/main/sahi/overlapping_1.png?raw=true \"Overlapping\") | False Negative, Incomplete bbox  |\n",
-                "| ![Overlapping](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/overlapping_2.png \"Overlapping\")| Double detection, Incomplete bbox|\n",
-                "| ![Overlapping](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/overlapping_3.png \"Overlapping\")| Incomplete bounding box|\n",
+                "| ![Overlapping](https://media.roboflow.com/supervision/cookbooks/sahi/overlapping_1.png \"Overlapping\") | False Negative, Incomplete bbox  |\n",
+                "| ![Overlapping](https://media.roboflow.com/supervision/cookbooks/sahi/overlapping_2.png \"Overlapping\")| Double detection, Incomplete bbox|\n",
+                "| ![Overlapping](https://media.roboflow.com/supervision/cookbooks/sahi/overlapping_3.png \"Overlapping\")| Incomplete bounding box|\n",
                 "\n",
                 "## Improving Object Detection Near Boundaries with Overlapping\n",
                 "\n",

docs/stylesheets/cookbooks_card.css

@@ -15,7 +15,7 @@
 /* Large screens (1024px and up) */
 @media (min-width: 1024px) {
     .custom-grid {
-        grid-template-columns: repeat(3, minmax(0, 1fr));
+        grid-template-columns: repeat(4, minmax(0, 1fr));
     }
 }
 

docs/stylesheets/extra.css

@@ -1,7 +1,10 @@
-:root {
+:root, body {
   /* Default to light theme */
   --md-primary-fg-color:        #8315F9;
-  --md-accent-fg-color:         #00FFCE;
+  --md-code-hl-color:          #8315F9 !important;
+  --md-accent-fg-color:        #8315F9 !important;
+  --md-code-hl-color--light: #e8d2ff89 !important;
+  --md-footer-fg-color--light: rgb(111, 108, 121) !important;
 }
 
 body.light {
@@ -9,6 +12,202 @@ body.light {
   --md-text-color:              #000000;
   --md-h2-color:                #000000;
 }
+.md-grid {
+  max-width: 85%;
+  margin: auto;
+}
+.sublist {
+  display: none;
+  list-style: none;
+  padding-left: 0;
+  background: white;
+  position: absolute;
+  border-radius: 8px;
+  margin-top: 0.25rem;
+}
+.sublist {
+  transition: opacity 0.5s ease-in-out;
+  display: none;
+  position: absolute; /* Ensure it overlaps and doesn't break flow */
+  background: white;  /* So it's visible */
+  z-index: 1000;
+}
+.sublist li {
+  padding: 0.5rem;
+}
+#products-list *:hover .products-sublist {
+  display: block;
+}
+#resources-list *, #products-list * {
+  cursor: pointer;
+}
+.products-sublist, .resources-sublist {
+  padding: 0.25rem;
+}
+.products-sublist li:hover, .resources-sublist li:hover, .md-nav__link[href]:hover {
+  background: rgb(242, 241, 247) !important;
+  border-radius: 6px;
+  color: initial !important;
+}
+.md-search {
+  flex-grow: 2;
+}
+.portfolio-section .md-grid {
+  max-width: 100%;
+}
+.md-header__inner {
+  align-items: center;
+  display: grid;
+  grid-template-columns: 0.1fr 1.4fr 2fr 2fr;
+  padding-right: 1rem;
+}
+.md-search__inner {
+  max-width: 600px;
+  width: 100%;
+  min-width: 100%;
+}
+.md-search__input {
+  background: white;
+  border: 1px solid rgb(229, 231, 235);
+  border-radius: 8px;
+  color: rgb(111, 108, 121);
+}
+.md-search__form *, .md-search__icon, .md-search__input {
+  color: rgb(111, 108, 121);
+}
+.md-search__input::placeholder {
+  color: rgb(156, 163, 175);
+}
+.md-search__form {
+  background: none !important;
+}
+.md-footer, .md-footer-meta {
+  background-color: transparent;
+  color: rgb(111, 108, 121);
+}
+.md-typeset .tabbed-set > input:first-child:checked ~ .tabbed-labels > :first-child, .md-typeset .tabbed-set > input:nth-child(10):checked ~ .tabbed-labels > :nth-child(10), .md-typeset .tabbed-set > input:nth-child(11):checked ~ .tabbed-labels > :nth-child(11), .md-typeset .tabbed-set > input:nth-child(12):checked ~ .tabbed-labels > :nth-child(12), .md-typeset .tabbed-set > input:nth-child(13):checked ~ .tabbed-labels > :nth-child(13), .md-typeset .tabbed-set > input:nth-child(14):checked ~ .tabbed-labels > :nth-child(14), .md-typeset .tabbed-set > input:nth-child(15):checked ~ .tabbed-labels > :nth-child(15), .md-typeset .tabbed-set > input:nth-child(16):checked ~ .tabbed-labels > :nth-child(16), .md-typeset .tabbed-set > input:nth-child(17):checked ~ .tabbed-labels > :nth-child(17), .md-typeset .tabbed-set > input:nth-child(18):checked ~ .tabbed-labels > :nth-child(18), .md-typeset .tabbed-set > input:nth-child(19):checked ~ .tabbed-labels > :nth-child(19), .md-typeset .tabbed-set > input:nth-child(2):checked ~ .tabbed-labels > :nth-child(2), .md-typeset .tabbed-set > input:nth-child(20):checked ~ .tabbed-labels > :nth-child(20), .md-typeset .tabbed-set > input:nth-child(3):checked ~ .tabbed-labels > :nth-child(3), .md-typeset .tabbed-set > input:nth-child(4):checked ~ .tabbed-labels > :nth-child(4), .md-typeset .tabbed-set > input:nth-child(5):checked ~ .tabbed-labels > :nth-child(5), .md-typeset .tabbed-set > input:nth-child(6):checked ~ .tabbed-labels > :nth-child(6), .md-typeset .tabbed-set > input:nth-child(7):checked ~ .tabbed-labels > :nth-child(7), .md-typeset .tabbed-set > input:nth-child(8):checked ~ .tabbed-labels > :nth-child(8), .md-typeset .tabbed-set > input:nth-child(9):checked ~ .tabbed-labels > :nth-child(9) {
+  color: #8315F9;
+  border-bottom: 1px solid #8315F9;
+}
+.md-footer *, html .md-footer-meta.md-typeset a {
+  color: rgb(111, 108, 121);
+}
+.repo-card {
+  height: 100%;
+}
+.header-btn {
+  text-align: center;
+}
+.header-btn, .sublist {
+  box-shadow: rgb(255, 255, 255) 0px 0px 0px 0px, rgb(217, 215, 226) 0px 0px 0px 1px, rgb(217, 215, 226) 0px 1px 2px 0px;
+}
+.header-btn:hover {
+  box-shadow: rgb(255, 255, 255) 0px 0px 0px 0px, rgb(217, 215, 226) 0px 0px 0px 1px, rgb(217, 215, 226) 0px 1.0001px 2.00013px -0.0000327245px, rgba(0, 0, 0, 0) 0px 0.000065449px 0.000130898px -0.000065449px;
+}
+.md-typeset .headerlink:hover, .md-typeset .headerlink:target {
+  color: #8315F9;
+}
+.md-typeset h1, .md-header__title {
+  color: black;
+  font-weight: 800;
+}
+.md-typeset h1 {
+  font-weight: normal;
+  margin-bottom: 1rem;
+}
+body {
+  background: linear-gradient(to left bottom, rgb(243, 238, 255), rgb(255, 255, 255) 60%) no-repeat;
+}
+
+/* .md-nav__link:has([tabindex=""]) {
+  text-transform: uppercase;
+} */
+
+.header-list {
+  display: flex;
+  align-items: center;
+  gap: 1rem;
+  list-style: none;
+  font-size: 0.75rem;
+  justify-content: flex-end;
+}
+.md-nav__list label, .md-nav--secondary label {
+  /* text-transform: uppercase; */
+  color: rgb(29, 29, 31) !important;
+  font-size: 0.7rem;
+  margin-bottom: 0;
+}
+.md-nav--secondary label {
+  margin-left: 0.5rem;
+}
+
+.md-nav__link {
+  padding: 0.25rem;
+  padding-left: 0.5rem;
+  padding-right: 0.5rem;
+}
+
+.md-nav__link--active {
+  background: rgb(243, 238, 255);
+  border-radius: 6px;
+  padding-top: 0.25rem;
+}
+
+.md-tabs__item--active {
+  color: var(--md-primary-fg-color);
+  border-bottom: 2px solid var(--md-primary-fg-color);
+}
+
+.md-nav--secondary .md-nav__title {
+  background: transparent;
+  box-shadow: none;
+}
+
+.md-header, .md-tabs {
+  color: rgb(111, 108, 121);
+  background-color: transparent;
+}
+.md-header--shadow {
+  background: linear-gradient(to left bottom, rgb(243, 238, 255), rgb(255, 255, 255) 60%);
+  box-shadow: none;
+  border-bottom: 1px solid rgb(229, 231, 235);
+}
+
+#item-logo {
+  display: none;
+}
+.md-main__inner, .md-header__inner, .md-grid {
+  max-width: 100%;
+}
+@media (max-width: 1200px) {
+  .md-header__inner {
+    display: flex;
+  }
+  .header-list {
+    display: none;
+  }
+  #item-logo {
+    display: block;
+  }
+}
+.md-content {
+  max-width: 40rem;
+  margin: auto;
+}
+/* // if no md-sidebar--primary, make .md-content full width */
+.md-main__inner:has(.md-sidebar--primary[hidden]) .md-content {
+  max-width: 100%;
+}
+.md-sidebar--primary {
+  flex: 0 20%;
+}
+.md-tabs {
+  border-bottom: 1px solid rgb(229, 231, 235);
+}
+.md-main__inner {
+  padding-top: 1rem;
+  margin-top: 0;
+}
 
 body.dark {
   /* Dark theme */
@@ -29,3 +228,43 @@ body[data-md-url$="/cookbooks/"] .md-content {
     margin-left: 0;
     width: 100%;
 }
+
+.md-main, nav .md-grid, .md-header__inner {
+  max-width: 1600px;
+  width: 100%;
+  margin: auto;
+}
+.md-search__scrollwrap {
+  width: 100% !important;
+}
+.md-nav--secondary .md-nav__title {
+  position: initial !important;
+}
+
+.md-header__title .md-ellipsis {
+  overflow: initial !important;
+  text-overflow: initial !important;
+}
+.md-search {
+  flex-grow: 0;
+}
+
+/* Table style */
+
+th, td {
+  border: 1px solid var(--md-typeset-table-color);
+}
+
+.md-typeset__table {
+  line-height: 1.5;
+}
+
+.md-typeset__table table:not([class]) {
+  font-size: 0.6rem;
+  border-collapse: collapse;
+}
+
+.md-typeset__table table:not([class]) td,
+.md-typeset__table table:not([class]) th {
+  padding: 10px;
+}

docs/theme/cookbooks.html

@@ -12,15 +12,15 @@
     </div>
     <div class="custom-grid">
       <a href="/develop/notebooks/quickstart"> <p class="card repo-card" data-name="Supervision Quickstart" data-labels="ANNOTATOR,DETECTION,SAM"
-          data-version="v0.18.0" data-author="SkalskiP,onuralpszr"></p>
+          data-version="v0.26.0" data-author="SkalskiP,onuralpszr"></p>
       </a>
       <a href="/develop/notebooks/count-objects-crossing-the-line">
         <p class="card repo-card" data-name="Count Objects Crossing the Line"
-          data-labels="ANNOTATORS,LINE ZONE,TRACKING" data-version="v0.18.0" data-author="SkalskiP"></p>
+          data-labels="ANNOTATORS,LINE ZONE,TRACKING" data-version="v0.26.0" data-author="SkalskiP"></p>
       </a>
       <a href="/develop/notebooks/zero-shot-object-detection-with-yolo-world">
         <p class="card repo-card" data-name="Zero-Shot Object Detection with YOLO-World"
-          data-labels="ANNOTATORS,DETECTION,INFERENCE" data-version="v0.19.0" data-author="SkalskiP"></p>
+          data-labels="ANNOTATORS,DETECTION,INFERENCE" data-version="v0.26.0" data-author="SkalskiP"></p>
       </a>
       <a href="/develop/notebooks/download-supervision-assets">
         <p class="card repo-card" data-name="Downloading Supervision Assets" data-labels="ASSETS" data-version="v0.18.0"
@@ -28,7 +28,7 @@
       </a>
       <a href="/develop/notebooks/annotate-video-with-detections">
         <p class="card repo-card" data-name="Annotate Video with Detections" data-labels="INFERENCE,YOLOV8"
-          data-version="v0.18.0" data-author="nickherrig"></p>
+          data-version="v0.26.0" data-author="nickherrig"></p>
       </a>
       <a href="/develop/notebooks/object-tracking">
         <p class="card repo-card" data-name="Object Tracking" data-labels="TRACKING, ANNOTATOR" data-version="v0.18.0"
@@ -36,23 +36,23 @@
       </a>
       <a href="/develop/notebooks/occupancy_analytics">
         <p class="card repo-card" data-name="Analyzing Zone Occupancy" data-labels="ANNOTATOR,DETECTION,ZONES"
-          data-version="v0.19.0" data-author="stellasphere"></p>
+          data-version="v0.26.0" data-author="stellasphere"></p>
       </a>
       <a href="/develop/notebooks/evaluating-alignment-of-text-to-image-diffusion-models">
         <p class="card repo-card" data-name="Evaluating Alignment of Text-to-image Diffusion Models"
-          data-labels="ANNOTATORS,YOLO WORLD" data-version="v0.19.0rc5" data-author="iamhatesz"></p>
+          data-labels="ANNOTATORS,YOLO WORLD" data-version="v0.26.0" data-author="iamhatesz"></p>
       </a>
       <a href="/develop/notebooks/serialise-detections-to-csv">
         <p class="card repo-card" data-name="Serialise Detections to a CSV File"
-          data-labels="DETECTIONS,CSV SINK,INFERENCE" data-version="v0.21.0" data-author="onuralpszr"></p>
+          data-labels="DETECTIONS,CSV SINK,INFERENCE" data-version="v0.26.0" data-author="onuralpszr"></p>
       </a>
       <a href="/develop/notebooks/serialise-detections-to-json">
         <p class="card repo-card" data-name="Serialise Detections to a JSON File"
-          data-labels="DETECTIONS,JSON SINK,INFERENCE" data-version="v0.21.0" data-author="onuralpszr"></p>
+          data-labels="DETECTIONS,JSON SINK,INFERENCE" data-version="v0.26.0" data-author="onuralpszr"></p>
       </a>
       <a href="/develop/notebooks/small-object-detection-with-sahi">
         <p class="card repo-card" data-name="Small Object Detection with SAHI"
-          data-labels="DETECTIONS,SAHI,SMALL,OBJECT,INFERENCE" data-version="v0.23.0" data-author="ediardo"></p>
+          data-labels="DETECTIONS,SAHI,SMALL,OBJECT,INFERENCE" data-version="v0.26.0" data-author="ediardo"></p>
       </a>
     </div>
   </div>

docs/theme/partials/header.html

@@ -0,0 +1,181 @@
+{% set class = "md-header" %}
+{% if "navigation.tabs.sticky" in features %}
+  {% set class = class ~ " md-header--shadow md-header--lifted" %}
+{% elif "navigation.tabs" not in features %}
+  {% set class = class ~ " md-header--shadow" %}
+{% endif %}
+
+<!-- Header -->
+<header class="{{ class }}" data-md-component="header">
+  <nav
+    class="md-header__inner md-grid"
+    aria-label="{{ lang.t('header') }}"
+  >
+
+    <!-- Link to home -->
+    <a
+      href="{{ config.extra.homepage | d(nav.homepage.url, true) | url }}"
+      title="{{ config.site_name | e }}"
+      class="md-header__button md-logo"
+      aria-label="{{ config.site_name }}"
+      data-md-component="logo"
+    >
+      {% include "partials/logo.html" %}
+    </a>
+
+    <!-- Button to open drawer -->
+    <label class="md-header__button md-icon" for="__drawer">
+      {% set icon = config.theme.icon.menu or "material/menu" %}
+      {% include ".icons/" ~ icon ~ ".svg" %}
+    </label>
+
+    <!-- Header title -->
+    <div class="md-header__title" data-md-component="header-title">
+      <div class="md-header__ellipsis">
+        <div class="md-header__topic">
+          <span class="md-ellipsis">
+            {{ config.site_name }} Docs
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis" style="display: flex; align-items: center; gap: 0.5rem;">
+            <a
+            href="{{ config.extra.homepage | d(nav.homepage.url, true) | url }}"
+            title="{{ config.site_name | e }}"
+            class="md-header__button md-logo"
+            aria-label="{{ config.site_name }}"
+            data-md-component="logo"
+            id="item-logo"
+            >
+            {% include "partials/logo.html" %}
+            </a>
+            {% if page.meta and page.meta.title %}
+              {{ page.meta.title }}
+            {% else %}
+              {{ page.title }}
+            {% endif %}
+          </span>
+        </div>
+      </div>
+    </div>
+    <!-- Button to open search modal -->
+    {% if "material/search" in config.plugins %}
+      {% set search = config.plugins["material/search"] | attr("config") %}
+
+      <!-- Check if search is actually enabled - see https://t.ly/DT_0V -->
+      {% if search.enabled %}
+        <label class="md-header__button md-icon" for="__search">
+          {% set icon = config.theme.icon.search or "material/magnify" %}
+          {% include ".icons/" ~ icon ~ ".svg" %}
+        </label>
+
+        <!-- Search interface -->
+        {% include "partials/search.html" %}
+      {% endif %}
+    {% endif %}
+
+
+    <ul class="header-list">
+        <li style="align-items: center;" id="resources-list">
+            <label for="dropdown-resources"><span>Resources <img src="https://ka-p.fontawesome.com/releases/v6.6.0/svgs/regular/chevron-down.svg?v=2&token=a463935e93" style="height: 0.5rem;" /></span></label>
+            <input type="radio" name="dropdown" id="dropdown-resources" style="display: none;" />
+            <ul class="resources-sublist sublist">
+                <li><a href="https://blog.roboflow.com">Blog</a></li>
+                <li><a href="https://discuss.roboflow.com">Community Forum</a></li>
+                <li><a href="https://roboflow.com/sales">Contact Sales</a></li>
+                <li><a href="https://universe.roboflow.com">Universe</a></li>
+            </ul>
+        </li>
+        <li style="align-items: center;" id="products-list">
+            <label for="dropdown-products"><span>Docs <img src="https://ka-p.fontawesome.com/releases/v6.6.0/svgs/regular/chevron-down.svg?v=2&token=a463935e93" style="height: 0.5rem;" /></span></label>
+            <input type="radio" name="dropdown" id="dropdown-products" style="display: none;" />
+            <ul class="products-sublist sublist">
+                <li><a href="https://inference.roboflow.com">Inference</a></li>
+                <li><a href="https://supervision.roboflow.com">Supervision</a></li>
+                <li><a href="https://trackers.roboflow.com">Trackers</a></li>
+                <li><a href="https://maestro.roboflow.com">Maestro</a></li>
+                <li><a href="https://docs.roboflow.com">Roboflow</a></li>
+            </ul>
+        </li>
+        <script>
+          document.addEventListener('click', function(event) {
+            const resourcesList = document.getElementById('resources-list');
+            const productsList = document.getElementById('products-list');
+            const dropdownResources = document.getElementById('dropdown-resources');
+            const dropdownProducts = document.getElementById('dropdown-products');
+
+            if (!resourcesList.contains(event.target)) {
+              dropdownResources.checked = false;
+            }
+            if (!productsList.contains(event.target)) {
+              dropdownProducts.checked = false;
+            }
+          });
+          // on mouse over
+          document.getElementById('resources-list').addEventListener('mouseover', function() {
+            document.getElementById('dropdown-resources').checked = true;
+          });
+          document.getElementById('products-list').addEventListener('mouseover', function() {
+            document.getElementById('dropdown-products').checked = true;
+          });
+          // on mouse out
+          document.getElementById('resources-list').addEventListener('mouseout', function() {
+            // if not hovering over the sublist or the label, uncheck the dropdown
+            // wait 1 sec
+            setTimeout(function() {
+              if (!document.querySelector('.resources-sublist:hover') && !document.querySelector('#resources-list:hover')) {
+                document.getElementById('dropdown-resources').checked = false;
+              }
+            }, 350);
+          });
+          // if mouseout of sublist, uncheck immediately
+          document.querySelector('.resources-sublist').addEventListener('mouseout', function() {
+            setTimeout(function() {
+              if (!document.querySelector('.resources-sublist:hover') && !document.querySelector('#resources-list:hover')) {
+                document.getElementById('dropdown-resources').checked = false;
+              }
+            }, 450);
+          });
+          document.getElementById('products-list').addEventListener('mouseout', function() {
+            // if not hovering over the sublist, uncheck the dropdown
+            // wait 1 sec
+            setTimeout(function() {
+              if (!document.querySelector('.products-sublist:hover') && !document.querySelector('#products-list:hover')) {
+                document.getElementById('dropdown-products').checked = false;
+              }
+            }, 500);
+          });
+          // if mouseout of sublist, uncheck immediately
+          document.querySelector('.products-sublist').addEventListener('mouseout', function() {
+            setTimeout(function() {
+              if (!document.querySelector('.products-sublist:hover') && !document.querySelector('#products-list:hover')) {
+                document.getElementById('dropdown-products').checked = false;
+              }
+            }, 500);
+          });
+
+        </script>
+        <style>
+          #dropdown-resources:checked ~ .resources-sublist {
+            display: block;
+          }
+          #dropdown-products:checked ~ .products-sublist {
+            display: block;
+          }
+          /* Hide dropdown if clicking outside */
+          body:not(:has(#dropdown-resources:checked)) .resources-sublist,
+          body:not(:has(#dropdown-products:checked)) .products-sublist {
+            display: none;
+          }
+        </style>
+        <a href="https://github.com/roboflow/supervision"><li class="header-btn" style="border-radius: 5px; color: white; background: var(--md-typeset-a-color); padding-top: 0.25rem; padding-left: 0.5rem; padding-bottom: 0.25rem; padding-right: 0.5rem; border: 1px solid #8315F9;">Go to GitHub</li></a>
+    </ul>
+  </nav>
+
+  <!-- Navigation tabs (sticky) -->
+  {% if "navigation.tabs.sticky" in features %}
+    {% if "navigation.tabs" in features %}
+      {% include "partials/tabs.html" %}
+    {% endif %}
+  {% endif %}
+</header>

docs/utils/image.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+status: new
 ---
 
 # Image Utils
@@ -29,10 +30,16 @@ comments: true
 :::supervision.utils.image.letterbox_image
 
 <div class="md-typeset">
-    <h2><a href="#supervision.utils.image.overlay_image">overlay_image</a></h2>
+    <h2><a href="#supervision.utils.image.tint_image">tint_image</a></h2>
 </div>
 
-:::supervision.utils.image.overlay_image
+:::supervision.utils.image.tint_image
+
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.image.grayscale_image">grayscale_image</a></h2>
+</div>
+
+:::supervision.utils.image.grayscale_image
 
 <div class="md-typeset">
     <h2><a href="#supervision.utils.image.ImageSink">ImageSink</a></h2>

examples/count_people_in_zone/README.md

@@ -17,7 +17,7 @@ https://github.com/roboflow/supervision/assets/26109316/f84db7b5-79e2-4142-a1da-
 - clone repository and navigate to example directory
 
     ```bash
-    git clone https://github.com/roboflow/supervision.git
+    git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
     cd supervision/examples/count_people_in_zone
     ```
 

examples/count_people_in_zone/inference_example.py

@@ -1,7 +1,6 @@
 import argparse
 import json
 import os
-from typing import List, Tuple
 
 import cv2
 import numpy as np
@@ -14,7 +13,7 @@ import supervision as sv
 COLORS = sv.ColorPalette.DEFAULT
 
 
-def load_zones_config(file_path: str) -> List[np.ndarray]:
+def load_zones_config(file_path: str) -> list[np.ndarray]:
     """
     Load polygon zone configurations from a JSON file.
 
@@ -28,16 +27,14 @@ def load_zones_config(file_path: str) -> List[np.ndarray]:
     Returns:
     List[np.ndarray]: A list of polygons, each represented as a NumPy array.
     """
-    with open(file_path, "r") as file:
+    with open(file_path) as file:
         data = json.load(file)
         return [np.array(polygon, np.int32) for polygon in data["polygons"]]
 
 
 def initiate_annotators(
-    polygons: List[np.ndarray], resolution_wh: Tuple[int, int]
-) -> Tuple[
-    List[sv.PolygonZone], List[sv.PolygonZoneAnnotator], List[sv.BoundingBoxAnnotator]
-]:
+    polygons: list[np.ndarray], resolution_wh: tuple[int, int]
+) -> tuple[list[sv.PolygonZone], list[sv.PolygonZoneAnnotator], list[sv.BoxAnnotator]]:
     line_thickness = sv.calculate_optimal_line_thickness(resolution_wh=resolution_wh)
     text_scale = sv.calculate_optimal_text_scale(resolution_wh=resolution_wh)
 
@@ -54,7 +51,7 @@ def initiate_annotators(
             text_thickness=line_thickness * 2,
             text_scale=text_scale * 2,
         )
-        box_annotator = sv.BoundingBoxAnnotator(
+        box_annotator = sv.BoxAnnotator(
             color=COLORS.by_idx(index), thickness=line_thickness
         )
         zones.append(zone)
@@ -95,9 +92,9 @@ def detect(
 
 def annotate(
     frame: np.ndarray,
-    zones: List[sv.PolygonZone],
-    zone_annotators: List[sv.PolygonZoneAnnotator],
-    box_annotators: List[sv.BoundingBoxAnnotator],
+    zones: list[sv.PolygonZone],
+    zone_annotators: list[sv.PolygonZoneAnnotator],
+    box_annotators: list[sv.BoxAnnotator],
     detections: sv.Detections,
 ) -> np.ndarray:
     """
@@ -108,7 +105,7 @@ def annotate(
         zones (List[sv.PolygonZone]): A list of polygon zones used for detection.
         zone_annotators (List[sv.PolygonZoneAnnotator]): A list of annotators for
             drawing zone annotations.
-        box_annotators (List[sv.BoundingBoxAnnotator]): A list of annotators for
+        box_annotators (List[sv.BoxAnnotator]): A list of annotators for
             drawing box annotations.
         detections (sv.Detections): Detections to be used for annotation.
 

examples/count_people_in_zone/requirements.txt

@@ -1,5 +1,5 @@
 gdown
-inference==0.9.17
-supervision>=0.20.0
+inference
+supervision
 tqdm
 ultralytics

examples/count_people_in_zone/ultralytics_example.py

@@ -1,6 +1,5 @@
 import argparse
 import json
-from typing import List, Tuple
 
 import cv2
 import numpy as np
@@ -12,7 +11,7 @@ import supervision as sv
 COLORS = sv.ColorPalette.DEFAULT
 
 
-def load_zones_config(file_path: str) -> List[np.ndarray]:
+def load_zones_config(file_path: str) -> list[np.ndarray]:
     """
     Load polygon zone configurations from a JSON file.
 
@@ -26,16 +25,14 @@ def load_zones_config(file_path: str) -> List[np.ndarray]:
     Returns:
     List[np.ndarray]: A list of polygons, each represented as a NumPy array.
     """
-    with open(file_path, "r") as file:
+    with open(file_path) as file:
         data = json.load(file)
         return [np.array(polygon, np.int32) for polygon in data["polygons"]]
 
 
 def initiate_annotators(
-    polygons: List[np.ndarray], resolution_wh: Tuple[int, int]
-) -> Tuple[
-    List[sv.PolygonZone], List[sv.PolygonZoneAnnotator], List[sv.BoundingBoxAnnotator]
-]:
+    polygons: list[np.ndarray], resolution_wh: tuple[int, int]
+) -> tuple[list[sv.PolygonZone], list[sv.PolygonZoneAnnotator], list[sv.BoxAnnotator]]:
     line_thickness = sv.calculate_optimal_line_thickness(resolution_wh=resolution_wh)
     text_scale = sv.calculate_optimal_text_scale(resolution_wh=resolution_wh)
 
@@ -52,7 +49,7 @@ def initiate_annotators(
             text_thickness=line_thickness * 2,
             text_scale=text_scale * 2,
         )
-        box_annotator = sv.BoundingBoxAnnotator(
+        box_annotator = sv.BoxAnnotator(
             color=COLORS.by_idx(index), thickness=line_thickness
         )
         zones.append(zone)
@@ -92,9 +89,9 @@ def detect(
 
 def annotate(
     frame: np.ndarray,
-    zones: List[sv.PolygonZone],
-    zone_annotators: List[sv.PolygonZoneAnnotator],
-    box_annotators: List[sv.BoundingBoxAnnotator],
+    zones: list[sv.PolygonZone],
+    zone_annotators: list[sv.PolygonZoneAnnotator],
+    box_annotators: list[sv.BoxAnnotator],
     detections: sv.Detections,
 ) -> np.ndarray:
     """
@@ -105,7 +102,7 @@ def annotate(
         zones (List[sv.PolygonZone]): A list of polygon zones used for detection.
         zone_annotators (List[sv.PolygonZoneAnnotator]): A list of annotators for
             drawing zone annotations.
-        box_annotators (List[sv.BoundingBoxAnnotator]): A list of annotators for
+        box_annotators (List[sv.BoxAnnotator]): A list of annotators for
             drawing box annotations.
         detections (sv.Detections): Detections to be used for annotation.
 
@@ -137,7 +134,7 @@ if __name__ == "__main__":
     )
     parser.add_argument(
         "--source_weights_path",
-        default="yolov8x.pt",
+        default="yolo11x.pt",
         help="Path to the source weights file",
         type=str,
     )

examples/heatmap_and_track/README.md

@@ -11,7 +11,7 @@ supervision package for multiple tasks such as drawing heatmap annotations, trac
 - clone repository and navigate to example directory
 
     ```bash
-    git clone https://github.com/roboflow/supervision.git
+    git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
     cd supervision/examples/heatmap_and_track
     ```
 

examples/heatmap_and_track/requirements.txt

@@ -1,2 +1,2 @@
-supervision[assets]==0.19.0
+supervision
 ultralytics

examples/speed_estimation/README.md

@@ -22,7 +22,7 @@ https://github.com/roboflow/supervision/assets/26109316/d50118c1-2ae4-458d-915a-
 - clone repository and navigate to example directory
 
     ```bash
-    git clone https://github.com/roboflow/supervision.git
+    git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
     cd supervision/examples/speed_estimation
     ```
 

examples/speed_estimation/requirements.txt

@@ -1,6 +1,6 @@
-supervision>=0.20.0
-tqdm==4.66.3
+supervision
+tqdm
 requests
-ultralytics==8.0.237
+ultralytics
 super-gradients==3.5.0
-inference==0.9.17
+inference

examples/speed_estimation/ultralytics_example.py

@@ -70,7 +70,7 @@ if __name__ == "__main__":
     args = parse_arguments()
 
     video_info = sv.VideoInfo.from_video_path(video_path=args.source_video_path)
-    model = YOLO("yolov8x.pt")
+    model = YOLO("yolo11x.pt")
 
     byte_track = sv.ByteTrack(
         frame_rate=video_info.fps, track_activation_threshold=args.confidence_threshold

examples/time_in_zone/README.md

@@ -16,7 +16,7 @@ https://github.com/roboflow/supervision/assets/26109316/d051cc8a-dd15-41d4-aa36-
 - clone repository and navigate to example directory
 
     ```bash
-    git clone https://github.com/roboflow/supervision.git
+    git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
     cd supervision/examples/time_in_zone
     ```
 

examples/time_in_zone/inference_file_example.py

@@ -1,5 +1,4 @@
 import argparse
-from typing import List
 
 import cv2
 import numpy as np
@@ -22,7 +21,7 @@ def main(
     model_id: str,
     confidence: float,
     iou: float,
-    classes: List[int],
+    classes: list[int],
 ) -> None:
     model = get_model(model_id=model_id)
     tracker = sv.ByteTrack(minimum_matching_threshold=0.5)

examples/time_in_zone/inference_naive_stream_example.py

@@ -1,5 +1,4 @@
 import argparse
-from typing import List
 
 import cv2
 import numpy as np
@@ -22,7 +21,7 @@ def main(
     model_id: str,
     confidence: float,
     iou: float,
-    classes: List[int],
+    classes: list[int],
 ) -> None:
     model = get_model(model_id=model_id)
     tracker = sv.ByteTrack(minimum_matching_threshold=0.5)

examples/time_in_zone/inference_stream_example.py

@@ -1,5 +1,4 @@
 import argparse
-from typing import List
 
 import cv2
 import numpy as np
@@ -18,7 +17,7 @@ LABEL_ANNOTATOR = sv.LabelAnnotator(
 
 
 class CustomSink:
-    def __init__(self, zone_configuration_path: str, classes: List[int]):
+    def __init__(self, zone_configuration_path: str, classes: list[int]):
         self.classes = classes
         self.tracker = sv.ByteTrack(minimum_matching_threshold=0.5)
         self.fps_monitor = sv.FPSMonitor()
@@ -83,7 +82,7 @@ def main(
     model_id: str,
     confidence: float,
     iou: float,
-    classes: List[int],
+    classes: list[int],
 ) -> None:
     sink = CustomSink(zone_configuration_path=zone_configuration_path, classes=classes)
 

examples/time_in_zone/requirements.txt

@@ -1,5 +1,6 @@
-opencv-python
-supervision>=0.20.0
+supervision
 ultralytics
-inference==0.9.17
-pytube
+inference
+# https://github.com/pytube/pytube/issues/2044
+# pytube
+pytubefix

examples/time_in_zone/scripts/download_from_youtube.py

@@ -1,11 +1,12 @@
+from __future__ import annotations
+
 import argparse
 import os
-from typing import Optional
 
-from pytube import YouTube
+from pytubefix import YouTube
 
 
-def main(url: str, output_path: Optional[str], file_name: Optional[str]) -> None:
+def main(url: str, output_path: str | None, file_name: str | None) -> None:
     yt = YouTube(url)
     stream = yt.streams.get_highest_resolution()
 

examples/time_in_zone/scripts/draw_zones.py

@@ -1,7 +1,9 @@
+from __future__ import annotations
+
 import argparse
 import json
 import os
-from typing import Any, Optional, Tuple
+from typing import Any
 
 import cv2
 import numpy as np
@@ -19,10 +21,10 @@ COLORS = sv.ColorPalette.DEFAULT
 WINDOW_NAME = "Draw Zones"
 POLYGONS = [[]]
 
-current_mouse_position: Optional[Tuple[int, int]] = None
+current_mouse_position: tuple[int, int] | None = None
 
 
-def resolve_source(source_path: str) -> Optional[np.ndarray]:
+def resolve_source(source_path: str) -> np.ndarray | None:
     if not os.path.exists(source_path):
         return None
 

examples/time_in_zone/ultralytics_file_example.py

@@ -1,5 +1,4 @@
 import argparse
-from typing import List
 
 import cv2
 import numpy as np
@@ -23,7 +22,7 @@ def main(
     device: str,
     confidence: float,
     iou: float,
-    classes: List[int],
+    classes: list[int],
 ) -> None:
     model = YOLO(weights)
     tracker = sv.ByteTrack(minimum_matching_threshold=0.5)

examples/time_in_zone/ultralytics_naive_stream_example.py

@@ -1,5 +1,4 @@
 import argparse
-from typing import List
 
 import cv2
 import numpy as np
@@ -23,7 +22,7 @@ def main(
     device: str,
     confidence: float,
     iou: float,
-    classes: List[int],
+    classes: list[int],
 ) -> None:
     model = YOLO(weights)
     tracker = sv.ByteTrack(minimum_matching_threshold=0.5)

examples/time_in_zone/ultralytics_stream_example.py

@@ -1,5 +1,4 @@
 import argparse
-from typing import List
 
 import cv2
 import numpy as np
@@ -19,7 +18,7 @@ LABEL_ANNOTATOR = sv.LabelAnnotator(
 
 
 class CustomSink:
-    def __init__(self, zone_configuration_path: str, classes: List[int]):
+    def __init__(self, zone_configuration_path: str, classes: list[int]):
         self.classes = classes
         self.tracker = sv.ByteTrack(minimum_matching_threshold=0.8)
         self.fps_monitor = sv.FPSMonitor()
@@ -84,7 +83,7 @@ def main(
     device: str,
     confidence: float,
     iou: float,
-    classes: List[int],
+    classes: list[int],
 ) -> None:
     model = YOLO(weights)
 

examples/time_in_zone/utils/general.py

@@ -1,11 +1,11 @@
 import json
-from typing import Generator, List
+from collections.abc import Generator
 
 import cv2
 import numpy as np
 
 
-def load_zones_config(file_path: str) -> List[np.ndarray]:
+def load_zones_config(file_path: str) -> list[np.ndarray]:
     """
     Load polygon zone configurations from a JSON file.
 
@@ -19,12 +19,12 @@ def load_zones_config(file_path: str) -> List[np.ndarray]:
     Returns:
         List[np.ndarray]: A list of polygons, each represented as a NumPy array.
     """
-    with open(file_path, "r") as file:
+    with open(file_path) as file:
         data = json.load(file)
         return [np.array(polygon, np.int32) for polygon in data]
 
 
-def find_in_list(array: np.ndarray, search_list: List[int]) -> np.ndarray:
+def find_in_list(array: np.ndarray, search_list: list[int]) -> np.ndarray:
     """Determines if elements of a numpy array are present in a list.
 
     Args:

examples/time_in_zone/utils/timers.py

@@ -1,5 +1,4 @@
 from datetime import datetime
-from typing import Dict
 
 import numpy as np
 
@@ -26,7 +25,7 @@ class FPSBasedTimer:
         """
         self.fps = fps
         self.frame_id = 0
-        self.tracker_id2frame_id: Dict[int, int] = {}
+        self.tracker_id2frame_id: dict[int, int] = {}
 
     def tick(self, detections: sv.Detections) -> np.ndarray:
         """Processes the current frame, updating time durations for each tracker.
@@ -63,7 +62,7 @@ class ClockBasedTimer:
 
     def __init__(self) -> None:
         """Initializes the ClockBasedTimer."""
-        self.tracker_id2start_time: Dict[int, datetime] = {}
+        self.tracker_id2start_time: dict[int, datetime] = {}
 
     def tick(self, detections: sv.Detections) -> np.ndarray:
         """Processes the current frame, updating time durations for each tracker.

examples/tracking/README.md

@@ -10,7 +10,7 @@ detection and Supervision for tracking and annotation.
 - clone repository and navigate to example directory
 
     ```bash
-    git clone https://github.com/roboflow/supervision.git
+    git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
     cd supervision/examples/tracking
     ```
 

examples/tracking/inference_example.py

@@ -18,7 +18,7 @@ def process_video(
     model = get_roboflow_model(model_id=model_id, api_key=roboflow_api_key)
 
     tracker = sv.ByteTrack()
-    box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
     label_annotator = sv.LabelAnnotator()
     frame_generator = sv.get_video_frames_generator(source_path=source_video_path)
     video_info = sv.VideoInfo.from_video_path(video_path=source_video_path)

examples/tracking/requirements.txt

@@ -1,4 +1,4 @@
-inference==0.9.17
-supervision==0.19.0
+inference
+supervision
 tqdm
 ultralytics

examples/tracking/ultralytics_example.py

@@ -16,7 +16,7 @@ def process_video(
     model = YOLO(source_weights_path)
 
     tracker = sv.ByteTrack()
-    box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
     label_annotator = sv.LabelAnnotator()
     frame_generator = sv.get_video_frames_generator(source_path=source_video_path)
     video_info = sv.VideoInfo.from_video_path(video_path=source_video_path)

examples/traffic_analysis/README.md

@@ -13,7 +13,7 @@ https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-
 - clone repository and navigate to example directory
 
     ```bash
-    git clone https://github.com/roboflow/supervision.git
+    git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
     cd supervision/examples/traffic_analysis
     ```
 

examples/traffic_analysis/inference_example.py

@@ -1,6 +1,8 @@
+from __future__ import annotations
+
 import argparse
 import os
-from typing import Dict, Iterable, List, Optional, Set
+from collections.abc import Iterable
 
 import cv2
 import numpy as np
@@ -29,14 +31,14 @@ ZONE_OUT_POLYGONS = [
 
 class DetectionsManager:
     def __init__(self) -> None:
-        self.tracker_id_to_zone_id: Dict[int, int] = {}
-        self.counts: Dict[int, Dict[int, Set[int]]] = {}
+        self.tracker_id_to_zone_id: dict[int, int] = {}
+        self.counts: dict[int, dict[int, set[int]]] = {}
 
     def update(
         self,
         detections_all: sv.Detections,
-        detections_in_zones: List[sv.Detections],
-        detections_out_zones: List[sv.Detections],
+        detections_in_zones: list[sv.Detections],
+        detections_out_zones: list[sv.Detections],
     ) -> sv.Detections:
         for zone_in_id, detections_in_zone in enumerate(detections_in_zones):
             for tracker_id in detections_in_zone.tracker_id:
@@ -59,9 +61,9 @@ class DetectionsManager:
 
 
 def initiate_polygon_zones(
-    polygons: List[np.ndarray],
+    polygons: list[np.ndarray],
     triggering_anchors: Iterable[sv.Position] = [sv.Position.CENTER],
-) -> List[sv.PolygonZone]:
+) -> list[sv.PolygonZone]:
     return [
         sv.PolygonZone(
             polygon=polygon,
@@ -77,7 +79,7 @@ class VideoProcessor:
         roboflow_api_key: str,
         model_id: str,
         source_video_path: str,
-        target_video_path: Optional[str] = None,
+        target_video_path: str | None = None,
         confidence_threshold: float = 0.3,
         iou_threshold: float = 0.7,
     ) -> None:

examples/traffic_analysis/requirements.txt

@@ -1,5 +1,5 @@
 gdown
-inference==0.9.17
-supervision>=0.20.0
+inference
+supervision
 tqdm
 ultralytics

examples/traffic_analysis/ultralytics_example.py

@@ -1,5 +1,7 @@
+from __future__ import annotations
+
 import argparse
-from typing import Dict, Iterable, List, Optional, Set
+from collections.abc import Iterable
 
 import cv2
 import numpy as np
@@ -27,14 +29,14 @@ ZONE_OUT_POLYGONS = [
 
 class DetectionsManager:
     def __init__(self) -> None:
-        self.tracker_id_to_zone_id: Dict[int, int] = {}
-        self.counts: Dict[int, Dict[int, Set[int]]] = {}
+        self.tracker_id_to_zone_id: dict[int, int] = {}
+        self.counts: dict[int, dict[int, set[int]]] = {}
 
     def update(
         self,
         detections_all: sv.Detections,
-        detections_in_zones: List[sv.Detections],
-        detections_out_zones: List[sv.Detections],
+        detections_in_zones: list[sv.Detections],
+        detections_out_zones: list[sv.Detections],
     ) -> sv.Detections:
         for zone_in_id, detections_in_zone in enumerate(detections_in_zones):
             for tracker_id in detections_in_zone.tracker_id:
@@ -57,9 +59,9 @@ class DetectionsManager:
 
 
 def initiate_polygon_zones(
-    polygons: List[np.ndarray],
+    polygons: list[np.ndarray],
     triggering_anchors: Iterable[sv.Position] = [sv.Position.CENTER],
-) -> List[sv.PolygonZone]:
+) -> list[sv.PolygonZone]:
     return [
         sv.PolygonZone(
             polygon=polygon,
@@ -74,7 +76,7 @@ class VideoProcessor:
         self,
         source_weights_path: str,
         source_video_path: str,
-        target_video_path: Optional[str] = None,
+        target_video_path: str | None = None,
         confidence_threshold: float = 0.3,
         iou_threshold: float = 0.7,
     ) -> None:

mkdocs.yml

@@ -3,9 +3,8 @@ site_url: https://supervision.roboflow.com/
 site_author: Roboflow
 site_description: A set of easy-to-use utilities that will come in handy in any computer vision project.
 repo_name: roboflow/supervision
-repo_url: https://github.com/roboflow/supervision
 edit_uri: https://github.com/roboflow/supervision/tree/main/docs
-copyright: Roboflow 2024. All rights reserved.
+copyright: Roboflow 2025. All rights reserved.
 
 extra:
   social:
@@ -13,12 +12,8 @@ extra:
       link: https://github.com/roboflow
     - icon: fontawesome/brands/python
       link: https://pypi.org/project/supervision
-    - icon: fontawesome/brands/docker
-      link: https://hub.docker.com/u/roboflow
     - icon: fontawesome/brands/youtube
       link: https://www.youtube.com/roboflow
-    - icon: fontawesome/brands/linkedin
-      link: https://www.linkedin.com/company/roboflow-ai/
     - icon: fontawesome/brands/x-twitter
       link: https://twitter.com/roboflow
     - icon: fontawesome/brands/discord
@@ -31,10 +26,10 @@ extra:
 
 extra_css:
   - stylesheets/extra.css
-  - stylesheets/cookbooks-card.css
+  - stylesheets/cookbooks_card.css
 
 nav:
-  - Supervision: index.md
+  - Home: index.md
   - Learn:
       - Detect and Annotate: how_to/detect_and_annotate.md
       - Save Detections: how_to/save_detections.md
@@ -42,13 +37,17 @@ nav:
       - Detect Small Objects: how_to/detect_small_objects.md
       - Track Objects on Video: how_to/track_objects.md
       - Process Datasets: how_to/process_datasets.md
-
-  - Reference - Code API:
+      - Benchmark a Model: how_to/benchmark_a_model.md
+  - Reference:
       - Detection and Segmentation:
           - Core: detection/core.md
           - Annotators: detection/annotators.md
-          - Double Detection Filter: detection/double_detection_filter.md
-          - Utils: detection/utils.md
+          - Converters: detection/utils/converters.md
+          - IoU and NMS: detection/utils/iou_and_nms.md
+          - Boxes: detection/utils/boxes.md
+          - Masks: detection/utils/masks.md
+          - Polygons: detection/utils/polygons.md
+          - VLMs: detection/utils/vlms.md
       - Keypoint Detection:
           - Core: keypoint/core.md
           - Annotators: keypoint/annotators.md
@@ -66,7 +65,11 @@ nav:
           - Utils: datasets/utils.md
       - Metrics:
           - mAP: metrics/mean_average_precision.md
+          - mAR: metrics/mean_average_recall.md
+          - Precision: metrics/precision.md
+          - Recall: metrics/recall.md
           - F1 Score: metrics/f1_score.md
+          - Common Values: metrics/common_values.md
           - Legacy Metrics: detection/metrics.md
       - Utils:
           - Video: utils/video.md
@@ -78,12 +81,7 @@ nav:
           - Geometry: utils/geometry.md
       - Assets: assets.md
   - Cookbooks: cookbooks.md
-  - Cheatsheet: https://roboflow.github.io/cheatsheet-supervision/
-  - Contribute:
-      - Contributing: contributing.md
-      - Code of Conduct: code_of_conduct.md
-      - License: license.md
-  - Release Notes:
+  - Changelog:
       - Changelog: changelog.md
       - Deprecated: deprecated.md
 
@@ -101,6 +99,7 @@ theme:
     - content.tooltips
     - content.code.annotate
     - navigation.tabs
+    - navigation.tabs.sticky
 
   palette:
     # Palette for light mode
@@ -118,11 +117,8 @@ theme:
         name: Switch to light mode
 
   font:
-    text: Roboto
-    code: Roboto Mono
-    features:
-      - content.code.copy
-      - content.code.annotate
+    text: Inter
+    code: IBM Plex Mono
 
 plugins:
   - search
@@ -144,9 +140,12 @@ plugins:
             group_by_category: true
             docstring_style: google
             show_symbol_type_heading: true
+            show_root_heading: True
             show_symbol_type_toc: true
             show_category_heading: true
-          domains: [std, py]
+          inventories:
+          - url: https://docs.python-requests.org/en/master/objects.inv
+            domains: [std, py]
   - git-committers:
       repository: roboflow/supervision
       branch: develop
@@ -171,12 +170,18 @@ markdown_extensions:
       check_paths: true
   - pymdownx.highlight:
       anchor_linenums: true
+      line_spans: __span
+      pygments_lang_class: true
+  - pymdownx.arithmatex:
+      generic: true
 
 extra_javascript:
-  - "https://widget.kapa.ai/kapa-widget.bundle.js"
   - "javascripts/init_kapa_widget.js"
   - "javascripts/cookbooks-card.js"
+  - "javascripts/segment.js"
+  - "javascripts/mathjax.js"
   - "https://cdnjs.cloudflare.com/ajax/libs/dompurify/3.0.8/purify.min.js"
+  - "https://unpkg.com/mathjax@3/es5/tex-mml-chtml.js"
 
 # Messages shown during document build
 # Reference: https://www.mkdocs.org/user-guide/configuration/#validation

pyproject.toml

@@ -1,18 +1,16 @@
-[tool.poetry]
+[project]
 name = "supervision"
-version = "0.24.0"
 description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
-authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
-maintainers = [
-    "Piotr Skalski <piotr.skalski92@gmail.com>",
-    "Linas Kondrackis <linas@roboflow.com>",
-]
+license = { text = "MIT" }
+version = "0.27.0rc4"
 readme = "README.md"
-license = "MIT"
-packages = [{ include = "supervision" }]
-homepage = "https://github.com/roboflow/supervision"
-repository = "https://github.com/roboflow/supervision"
-documentation = "https://supervision.roboflow.com/latest/"
+requires-python = ">=3.9"
+authors = [
+    { name = "Piotr Skalski", email = "piotr.skalski92@gmail.com" }
+]
+maintainers = [
+    { name = "Piotr Skalski", email = "piotr.skalski92@gmail.com" },
+]
 keywords = [
     "machine-learning",
     "deep-learning",
@@ -22,7 +20,6 @@ keywords = [
     "AI",
     "Roboflow",
 ]
-
 classifiers = [
     'Development Status :: 4 - Beta',
     'Intended Audience :: Developers',
@@ -40,82 +37,63 @@ classifiers = [
     'Operating System :: POSIX :: Linux',
     'Operating System :: MacOS',
 ]
-
-[tool.poetry.dependencies]
-python = "^3.8"
-numpy = [
-    { version = ">=1.21.2,<1.23.3", python = "<=3.10" },
-    { version = ">=1.23.3", python = ">3.10" },
-]
-scipy = [
-    { version = "1.10.0", python = "<3.9" },
-    { version = "^1.10.0", python = ">=3.9" },
-]
-matplotlib = ">=3.6.0"
-pyyaml = ">=5.3"
-defusedxml = "^0.7.1"
-pillow = ">=9.4"
-requests = { version = ">=2.26.0,<=2.32.3", optional = true }
-tqdm = { version = ">=4.62.3,<=4.66.5", optional = true }
-# pandas: picked lowest major version that supports Python 3.8
-pandas = { version = ">=2.0.0", optional = true }
-pandas-stubs = { version = ">=2.0.0.230412", optional = true }
-opencv-python = ">=4.5.5.64"
-
-[tool.poetry.extras]
-assets = ["requests", "tqdm"]
-metrics = ["pandas", "pandas-stubs"]
-
-[tool.poetry.group.dev.dependencies]
-twine = "^5.1.1"
-pytest = ">=7.2.2,<9.0.0"
-wheel = ">=0.40,<0.45"
-build = ">=0.10,<1.3"
-ruff = ">=0.1.0"
-mypy = "^1.4.1"
-pre-commit = "^3.3.3"
-tox = "^4.11.4"
-notebook = ">=6.5.3,<8.0.0"
-ipywidgets = "^8.1.1"
-jupytext = "^1.16.1"
-nbconvert = "^7.14.2"
-docutils = [
-    { version = "^0.20.1", python = "<3.9" },
-    { version = "^0.21.1", python = ">=3.9" },
+dependencies = [
+    "numpy>=1.21.2",
+    "scipy>=1.10.0",
+    "matplotlib>=3.6.0",
+    "pyyaml>=5.3",
+    "defusedxml>=0.7.1",
+    "pillow>=9.4",
+    "requests>=2.26.0",
+    "tqdm>=4.62.3",
+    "opencv-python>=4.5.5.64"
 ]
 
-[tool.poetry.group.docs.dependencies]
-mkdocs-material = { extras = ["imaging"], version = "^9.5.5" }
-mkdocstrings = ">=0.25.2,<0.27.0"
-mkdocstrings-python = "^1.10.9"
-mike = "^2.0.0"
-# For Documentation Development use Python 3.10 or above
-# Use Latest mkdocs-jupyter min 0.24.6 for Jupyter Notebook Theme support
-mkdocs-jupyter = "^0.24.3"
-mkdocs-git-committers-plugin-2 = "^2.4.1"
-mkdocs-git-revision-date-localized-plugin = "^1.2.4"
+[project.urls]
+Homepage = "https://github.com/roboflow/supervision"
+Repository = "https://github.com/roboflow/supervision"
+Documentation = "https://supervision.roboflow.com/latest/"
 
-[tool.poetry.group.typecheck]
-optional = true
-
-[tool.poetry.group.typecheck.dependencies]
-types-pyyaml = "^6.0.12.20240808"
-types-cffi = "^1.16.0.20240331"
-types-requests = "^2.32.0.20240712"
-types-tqdm = "^4.66.0.20240417"
+[project.optional-dependencies]
+metrics = [
+    "pandas>=2.0.0",
+]
 
+[dependency-groups]
+dev = [
+    "pytest>=7.2.2,<9.0.0",
+    "tox>=4.11.4",
+    "notebook>=6.5.3,<8.0.0",
+    "ipywidgets>=8.1.1",
+    "jupytext>=1.16.1",
+    "nbconvert>=7.14.2",
+    "docutils!=0.21"
+]
+docs = [
+    "mkdocs-material[imaging]>=9.5.5",
+    "mkdocstrings>=0.25.2,<0.30.0",
+    "mkdocstrings-python>=1.10.9",
+    "mike>=2.0.0",
+    "mkdocs-jupyter>=0.24.3",
+    "mkdocs-git-committers-plugin-2>=2.4.1; python_version >= '3.9' and python_version < '4'",
+    "mkdocs-git-revision-date-localized-plugin>=1.2.4"
+]
+build = [
+    "twine>=5.1.1,<7.0.0",
+    "wheel>=0.40,<0.46",
+    "build>=0.10,<1.3"
+]
 
 [tool.bandit]
 target = ["test", "supervision"]
-tests = ["B201", "B301", "B318", "B314", "B303", "B413", "B412", "B410"]
+tests = ["B201", "B301", "B318", "B314", "B303", "B413", "B412"]
 
 [tool.autoflake]
 check = true
 imports = ["cv2", "supervision"]
 
-
 [tool.ruff]
-target-version = "py38"
+target-version = "py39"
 
 # Exclude a variety of commonly ignored directories.
 exclude = [
@@ -150,7 +128,7 @@ indent-width = 4
 
 [tool.ruff.lint]
 # Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default.
-select = ["E", "F", "I", "A", "Q", "W", "RUF"]
+select = ["E", "F", "I", "A", "Q", "W", "RUF", "UP"]
 ignore = []
 # Allow autofix for all enabled rules (when `--fix`) is provided.
 fixable = [
@@ -237,7 +215,7 @@ skip-magic-trailing-comma = false
 line-ending = "auto"
 
 [tool.codespell]
-skip = "*.ipynb,poetry.lock"
+skip = "*.ipynb"
 count = true
 quiet-level = 3
 ignore-words-list = "STrack,sTrack,strack"
@@ -246,8 +224,12 @@ ignore-words-list = "STrack,sTrack,strack"
 include-package-data = false
 
 [tool.setuptools.packages.find]
+include = ["supervision*"]
 exclude = ["docs*", "test*", "examples*"]
 
+[tool.setuptools.package-data]
+supervision = ["py.typed"]
+
 [build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+requires = ["setuptools >= 61.0"]
+build-backend = "setuptools.build_meta"

supervision/__init__.py

@@ -9,11 +9,11 @@ except importlib_metadata.PackageNotFoundError:
 from supervision.annotators.core import (
     BackgroundOverlayAnnotator,
     BlurAnnotator,
-    BoundingBoxAnnotator,
     BoxAnnotator,
     BoxCornerAnnotator,
     CircleAnnotator,
     ColorAnnotator,
+    ComparisonAnnotator,
     CropAnnotator,
     DotAnnotator,
     EllipseAnnotator,
@@ -38,6 +38,7 @@ from supervision.dataset.core import (
     ClassificationDataset,
     DetectionDataset,
 )
+from supervision.dataset.formats.coco import get_coco_class_index_mapping
 from supervision.dataset.utils import mask_to_rle, rle_to_mask
 from supervision.detection.core import Detections
 from supervision.detection.line_zone import (
@@ -45,38 +46,56 @@ from supervision.detection.line_zone import (
     LineZoneAnnotator,
     LineZoneAnnotatorMulticlass,
 )
-from supervision.detection.lmm import LMM
-from supervision.detection.overlap_filter import (
-    OverlapFilter,
-    box_non_max_merge,
-    box_non_max_suppression,
-    mask_non_max_suppression,
-)
 from supervision.detection.tools.csv_sink import CSVSink
 from supervision.detection.tools.inference_slicer import InferenceSlicer
 from supervision.detection.tools.json_sink import JSONSink
 from supervision.detection.tools.polygon_zone import PolygonZone, PolygonZoneAnnotator
 from supervision.detection.tools.smoother import DetectionsSmoother
-from supervision.detection.utils import (
-    box_iou_batch,
-    calculate_masks_centroids,
+from supervision.detection.utils.boxes import (
     clip_boxes,
-    contains_holes,
-    contains_multiple_segments,
-    filter_polygons_by_area,
-    mask_iou_batch,
+    denormalize_boxes,
+    move_boxes,
+    pad_boxes,
+    scale_boxes,
+)
+from supervision.detection.utils.converters import (
     mask_to_polygons,
     mask_to_xyxy,
-    move_boxes,
-    move_masks,
-    oriented_box_iou_batch,
-    pad_boxes,
     polygon_to_mask,
     polygon_to_xyxy,
-    scale_boxes,
     xcycwh_to_xyxy,
     xywh_to_xyxy,
+    xyxy_to_mask,
+    xyxy_to_polygons,
+    xyxy_to_xcycarh,
+    xyxy_to_xywh,
 )
+from supervision.detection.utils.iou_and_nms import (
+    OverlapFilter,
+    OverlapMetric,
+    box_iou,
+    box_iou_batch,
+    box_iou_batch_with_jaccard,
+    box_non_max_merge,
+    box_non_max_suppression,
+    mask_iou_batch,
+    mask_non_max_merge,
+    mask_non_max_suppression,
+    oriented_box_iou_batch,
+)
+from supervision.detection.utils.masks import (
+    calculate_masks_centroids,
+    contains_holes,
+    contains_multiple_segments,
+    filter_segments_by_distance,
+    move_masks,
+)
+from supervision.detection.utils.polygons import (
+    approximate_polygon,
+    filter_polygons_by_area,
+)
+from supervision.detection.utils.vlms import edit_distance, fuzzy_match_index
+from supervision.detection.vlm import LMM, VLM
 from supervision.draw.color import Color, ColorPalette
 from supervision.draw.utils import (
     calculate_optimal_line_thickness,
@@ -91,24 +110,25 @@ from supervision.draw.utils import (
 )
 from supervision.geometry.core import Point, Position, Rect
 from supervision.geometry.utils import get_polygon_center
-from supervision.keypoint.annotators import (
+from supervision.key_points.annotators import (
     EdgeAnnotator,
     VertexAnnotator,
     VertexLabelAnnotator,
 )
-from supervision.keypoint.core import KeyPoints
+from supervision.key_points.core import KeyPoints
 from supervision.metrics.detection import ConfusionMatrix, MeanAveragePrecision
 from supervision.tracker.byte_tracker.core import ByteTrack
 from supervision.utils.conversion import cv2_to_pillow, pillow_to_cv2
 from supervision.utils.file import list_files_with_extensions
 from supervision.utils.image import (
     ImageSink,
-    create_tiles,
     crop_image,
+    grayscale_image,
     letterbox_image,
     overlay_image,
     resize_image,
     scale_image,
+    tint_image,
 )
 from supervision.utils.notebook import plot_image, plot_images_grid
 from supervision.utils.video import (
@@ -118,3 +138,122 @@ from supervision.utils.video import (
     get_video_frames_generator,
     process_video,
 )
+
+__all__ = [
+    "LMM",
+    "BackgroundOverlayAnnotator",
+    "BaseDataset",
+    "BlurAnnotator",
+    "BoxAnnotator",
+    "BoxCornerAnnotator",
+    "ByteTrack",
+    "CSVSink",
+    "CircleAnnotator",
+    "ClassificationDataset",
+    "Classifications",
+    "Color",
+    "ColorAnnotator",
+    "ColorLookup",
+    "ColorPalette",
+    "ComparisonAnnotator",
+    "ConfusionMatrix",
+    "CropAnnotator",
+    "DetectionDataset",
+    "Detections",
+    "DetectionsSmoother",
+    "DotAnnotator",
+    "EdgeAnnotator",
+    "EllipseAnnotator",
+    "FPSMonitor",
+    "HaloAnnotator",
+    "HeatMapAnnotator",
+    "IconAnnotator",
+    "ImageSink",
+    "InferenceSlicer",
+    "JSONSink",
+    "KeyPoints",
+    "LabelAnnotator",
+    "LineZone",
+    "LineZoneAnnotator",
+    "LineZoneAnnotatorMulticlass",
+    "MaskAnnotator",
+    "MeanAveragePrecision",
+    "OrientedBoxAnnotator",
+    "OverlapFilter",
+    "OverlapMetric",
+    "PercentageBarAnnotator",
+    "PixelateAnnotator",
+    "Point",
+    "PolygonAnnotator",
+    "PolygonZone",
+    "PolygonZoneAnnotator",
+    "Position",
+    "Rect",
+    "RichLabelAnnotator",
+    "RoundBoxAnnotator",
+    "TraceAnnotator",
+    "TriangleAnnotator",
+    "VertexAnnotator",
+    "VertexLabelAnnotator",
+    "VideoInfo",
+    "VideoSink",
+    "approximate_polygon",
+    "box_iou",
+    "box_iou_batch",
+    "box_iou_batch_with_jaccard",
+    "box_non_max_merge",
+    "box_non_max_suppression",
+    "calculate_masks_centroids",
+    "calculate_optimal_line_thickness",
+    "calculate_optimal_text_scale",
+    "clip_boxes",
+    "contains_holes",
+    "contains_multiple_segments",
+    "crop_image",
+    "cv2_to_pillow",
+    "draw_filled_polygon",
+    "draw_filled_rectangle",
+    "draw_image",
+    "draw_line",
+    "draw_polygon",
+    "draw_rectangle",
+    "draw_text",
+    "edit_distance",
+    "filter_polygons_by_area",
+    "filter_segments_by_distance",
+    "fuzzy_match_index",
+    "get_coco_class_index_mapping",
+    "get_polygon_center",
+    "get_video_frames_generator",
+    "grayscale_image",
+    "letterbox_image",
+    "list_files_with_extensions",
+    "mask_iou_batch",
+    "mask_non_max_merge",
+    "mask_non_max_suppression",
+    "mask_to_polygons",
+    "mask_to_rle",
+    "mask_to_xyxy",
+    "move_boxes",
+    "move_masks",
+    "oriented_box_iou_batch",
+    "overlay_image",
+    "pad_boxes",
+    "pillow_to_cv2",
+    "plot_image",
+    "plot_images_grid",
+    "polygon_to_mask",
+    "polygon_to_xyxy",
+    "process_video",
+    "resize_image",
+    "rle_to_mask",
+    "scale_boxes",
+    "scale_image",
+    "tint_image",
+    "xcycwh_to_xyxy",
+    "xywh_to_xyxy",
+    "xyxy_to_mask",
+    "xyxy_to_polygons",
+    "xyxy_to_xcycarh",
+    "xyxy_to_xywh",
+]