Merge branch 'develop' of https://github.com/tc360950/supervision into line-zone-unit-tests

2024-05-24 18:11:17 +02:00 · 2024-05-24 18:11:17 +02:00 · 72d677ad89
parent a09b6fac6f 2723735b01
commit 72d677ad89
33 changed files with 2300 additions and 341 deletions
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@ -45,7 +45,7 @@ repos:


  -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.4.2
+      rev: v0.4.4
      hooks:
      -   id: ruff
          args: [--fix, --exit-non-zero-on-fix]
--- a/docs/datasets/core.md
+++ b/docs/datasets/core.md
@ -1,5 +1,6 @@
 ---
 comments: true
+status: new
 ---

 # Datasets
--- a/docs/datasets/utils.md
+++ b/docs/datasets/utils.md
@ -0,0 +1,18 @@
+---
+comments: true
+status: new
+---
+
+# Datasets Utils
+
+<div class="md-typeset">
+  <h2><a href="#supervision.dataset.utils.rle_to_mask">rle_to_mask</a></h2>
+</div>
+
+:::supervision.dataset.utils.rle_to_mask
+
+<div class="md-typeset">
+  <h2><a href="#supervision.dataset.utils.mask_to_rle">mask_to_rle</a></h2>
+</div>
+
+:::supervision.dataset.utils.mask_to_rle
--- a/docs/detection/annotators.md
+++ b/docs/detection/annotators.md
@ -285,6 +285,37 @@ status: new

    </div>

+=== "RichLabel"
+
+    ```python
+    import supervision as sv
+
+    image = ...
+    detections = sv.Detections(...)
+
+    labels = [
+        f"{class_name} {confidence:.2f}"
+        for class_name, confidence
+        in zip(detections['class_name'], detections.confidence)
+    ]
+
+    rich_label_annotator = sv.RichLabelAnnotator(
+        font_path=".../font.ttf",
+        text_position=sv.Position.CENTER
+    )
+    annotated_frame = label_annotator.annotate(
+        scene=image.copy(),
+        detections=detections,
+        labels=labels
+    )
+    ```
+
+    <div class="result" markdown>
+
+    ![label-annotator-example](https://media.roboflow.com/supervision-annotator-examples/label-annotator-example-purple.png){ align=center width="800" }
+
+    </div>
+
 === "Crop"

    ```python
@ -492,6 +523,12 @@ status: new

 :::supervision.annotators.core.LabelAnnotator

+<div class="md-typeset">
+    <h2><a href="#supervision.annotators.core.RichLabelAnnotator">RichLabelAnnotator</a></h2>
+</div>
+
+:::supervision.annotators.core.RichLabelAnnotator
+
 <div class="md-typeset">
    <h2><a href="#supervision.annotators.core.BlurAnnotator">BlurAnnotator</a></h2>
 </div>
--- a/docs/detection/utils.md
+++ b/docs/detection/utils.md
@ -65,8 +65,38 @@ status: new

 :::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.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
--- a/docs/how_to/detect_small_objects.md
+++ b/docs/how_to/detect_small_objects.md
@ -6,7 +6,7 @@ status: new
 # Detect Small Objects

 This guide shows how to detect small objects
-with the  [Inference](https://github.com/roboflow/inference),
+with the [Inference](https://github.com/roboflow/inference),
 [Ultralytics](https://github.com/ultralytics/ultralytics) or
 [Transformers](https://github.com/huggingface/transformers) packages using
 [`InferenceSlicer`](/latest/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer).
@ -68,10 +68,10 @@ size relative to the image resolution.
    import torch
    import supervision as sv
    from PIL import Image
-    from transformers import DetrImageProcessor, DetrForObjectDetection
+    from transformers import DetrImageProcessor, DetrForSegmentation

    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
-    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50")

    image = Image.open(<SOURCE_IMAGE_PATH>)
    inputs = processor(images=image, return_tensors="pt")
@ -79,8 +79,8 @@ size relative to the image resolution.
    with torch.no_grad():
        outputs = model(**inputs)

-    width, height = image.size
-    target_size = torch.tensor([[height, width]])
+    width, height = image_slice.size
+    target_size = torch.tensor([[width, height]])
    results = processor.post_process_object_detection(
        outputs=outputs, target_sizes=target_size)[0]
    detections = sv.Detections.from_transformers(results)
@ -175,7 +175,7 @@ objects within each, and aggregating the results.

    def callback(image_slice: np.ndarray) -> sv.Detections:
        results = model.infer(image_slice)[0]
-        detections = sv.Detections.from_inference(results)
+        return sv.Detections.from_inference(results)

    slicer = sv.InferenceSlicer(callback = callback)
    detections = slicer(image)
@ -239,8 +239,8 @@ objects within each, and aggregating the results.
        with torch.no_grad():
            outputs = model(**inputs)

-        width, height = image.size
-        target_size = torch.tensor([[height, width]])
+        width, height = image_slice.size
+        target_size = torch.tensor([[width, height]])
        results = processor.post_process_object_detection(
            outputs=outputs, target_sizes=target_size)[0]
        return sv.Detections.from_transformers(results)
@ -264,3 +264,63 @@ objects within each, and aggregating the results.
    ```

 ![detection-with-inference-slicer](https://media.roboflow.com/supervision_detect_small_objects_example_3.png)
+
+## Small Object Segmentation
+
+[`InferenceSlicer`](/latest/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer) can perform segmentation tasks too.
+
+=== "Inference"
+
+    ```{ .py hl_lines="6 16 19-20" }
+    import cv2
+    import numpy as np
+    import supervision as sv
+    from inference import get_model
+
+    model = get_model(model_id="yolov8x-seg-640")
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
+
+    def callback(image_slice: np.ndarray) -> sv.Detections:
+        results = model.infer(image_slice)[0]
+        return sv.Detections.from_inference(results)
+
+    slicer = sv.InferenceSlicer(callback = callback)
+    detections = slicer(image)
+
+    mask_annotator = sv.MaskAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    annotated_image = mask_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections)
+    ```
+
+=== "Ultralytics"
+
+    ```{ .py hl_lines="6 16 19-20" }
+    import cv2
+    import numpy as np
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8x-seg.pt")
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
+
+    def callback(image_slice: np.ndarray) -> sv.Detections:
+        result = model(image_slice)[0]
+        return sv.Detections.from_ultralytics(result)
+
+    slicer = sv.InferenceSlicer(callback = callback)
+    detections = slicer(image)
+
+    mask_annotator = sv.MaskAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    annotated_image = mask_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections)
+    ```
+
+![detection-with-inference-slicer](https://media.roboflow.com/supervision-docs/inference-slicer-segmentation-example.png)
--- a/docs/keypoint/annotators.md
+++ b/docs/keypoint/annotators.md
@ -13,7 +13,10 @@ status: new
    image = ...
    key_points = sv.KeyPoints(...)

-    vertex_annotator = sv.VertexAnnotator(color=sv.Color.GREEN, radius=10)
+    vertex_annotator = sv.VertexAnnotator(
+        color=sv.Color.GREEN,
+        radius=10
+    )
    annotated_frame = vertex_annotator.annotate(
        scene=image.copy(),
        key_points=key_points
@ -34,7 +37,10 @@ status: new
    image = ...
    key_points = sv.KeyPoints(...)

-    edge_annotator = sv.EdgeAnnotator(color=sv.Color.GREEN, thickness=5)
+    edge_annotator = sv.EdgeAnnotator(
+        color=sv.Color.GREEN,
+        thickness=5
+    )
    annotated_frame = edge_annotator.annotate(
        scene=image.copy(),
        key_points=key_points
@ -47,6 +53,31 @@ status: new

    </div>

+=== "VertexLabelAnnotator"
+
+    ```python
+    import supervision as sv
+
+    image = ...
+    key_points = sv.KeyPoints(...)
+
+    vertex_label_annotator = sv.VertexLabelAnnotator(
+        color=sv.Color.GREEN,
+        text_color=sv.Color.BLACK,
+        border_radius=5
+    )
+    annotated_frame = vertex_label_annotator.annotate(
+        scene=image.copy(),
+        key_points=key_points
+    )
+    ```
+
+    <div class="result" markdown>
+
+    ![vertex-label-annotator-example](https://media.roboflow.com/supervision-annotator-examples/vertex-label-annotator-example.png){ align=center width="800" }
+
+    </div>
+
 <div class="md-typeset">
  <h2><a href="#supervision.keypoint.annotators.VertexAnnotator">VertexAnnotator</a></h2>
 </div>
@ -58,3 +89,9 @@ status: new
 </div>

 :::supervision.keypoint.annotators.EdgeAnnotator
+
+<div class="md-typeset">
+  <h2><a href="#supervision.keypoint.annotators.VertexLabelAnnotator">VertexLabelAnnotator</a></h2>
+</div>
+
+:::supervision.keypoint.annotators.VertexLabelAnnotator
--- a/docs/utils/draw.md
+++ b/docs/utils/draw.md
@ -41,7 +41,7 @@ comments: true
 :::supervision.draw.utils.draw_image

 <div class="md-typeset">
-    <h2><a href="#supervision.draw.utils.calculate_optimal_font_scale">calculate_optimal_font_scale</a></h2>
+    <h2><a href="#supervision.draw.utils.calculate_optimal_text_scale">calculate_optimal_text_scale</a></h2>
 </div>

 :::supervision.draw.utils.calculate_optimal_text_scale
--- a/examples/time_in_zone/README.md
+++ b/examples/time_in_zone/README.md
@ -103,7 +103,7 @@ python scripts/draw_zones.py \
 ```bash
 python scripts/draw_zones.py \
 --source_path "data/traffic/video.mp4" \
--zone_configuration_path "data/traffic/custom_config.json"
+--zone_configuration_path "data/traffic/config.json"
 ```

 https://github.com/roboflow/supervision/assets/26109316/9d514c9e-2a61-418b-ae49-6ac1ad6ae5ac
@ -157,7 +157,7 @@ Script to run object detection on a video stream using the Roboflow Inference mo
  - `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.

 ```bash
-python inference_file_example.py \
+python inference_stream_example.py \
 --zone_configuration_path "data/checkout/config.json" \
 --rtsp_url "rtsp://localhost:8554/live0.stream" \
 --model_id "yolov8x-640" \
@ -167,7 +167,7 @@ python inference_file_example.py \
 ```

 ```bash
-python inference_file_example.py \
+python inference_stream_example.py \
 --zone_configuration_path "data/traffic/config.json" \
 --rtsp_url "rtsp://localhost:8554/live0.stream" \
 --model_id "yolov8x-640" \
@ -192,7 +192,7 @@ Script to run object detection on a video file using the Ultralytics YOLOv8 mode
  - `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.

 ```bash
-python inference_file_example.py \
+python ultralytics_file_example.py \
 --zone_configuration_path "data/checkout/config.json" \
 --source_video_path "data/checkout/video.mp4" \
 --weights "yolov8x.pt" \
@ -203,7 +203,7 @@ python inference_file_example.py \
 ```

 ```bash
-python inference_file_example.py \
+python ultralytics_file_example.py \
 --zone_configuration_path "data/traffic/config.json" \
 --source_video_path "data/traffic/video.mp4" \
 --weights "yolov8x.pt" \
@ -226,7 +226,7 @@ Script to run object detection on a video stream using the Ultralytics YOLOv8 mo
  - `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.

 ```bash
-python inference_file_example.py \
+python ultralytics_stream_example.py \
 --zone_configuration_path "data/checkout/config.json" \
 --rtsp_url "rtsp://localhost:8554/live0.stream" \
 --weights "yolov8x.pt" \
@ -237,7 +237,7 @@ python inference_file_example.py \
 ```

 ```bash
-python inference_file_example.py \
+python ultralytics_stream_example.py \
 --zone_configuration_path "data/traffic/config.json" \
 --rtsp_url "rtsp://localhost:8554/live0.stream" \
 --weights "yolov8x.pt" \
--- a/mkdocs.yml
+++ b/mkdocs.yml
@ -41,7 +41,7 @@ nav:
      - Save Detections: how_to/save_detections.md
      - Filter Detections: how_to/filter_detections.md
      - Detect Small Objects: how_to/detect_small_objects.md
-      - Track Objects: how_to/track_objects.md
+      - Track Objects on Video: how_to/track_objects.md

  - API:
    - Detection and Segmentation:
@ -61,7 +61,9 @@ nav:
        - Detection Smoother: detection/tools/smoother.md
        - Save Detections: detection/tools/save_detections.md
    - Trackers: trackers.md
-    - Datasets: datasets.md
+    - Datasets:
+        - Core: datasets/core.md
+        - Utils: datasets/utils.md
    - Utils:
        - Video: utils/video.md
        - Image: utils/image.md
--- a/poetry.lock
+++ b/poetry.lock
@ -1340,13 +1340,13 @@ trio = ["async_generator", "trio"]

 [[package]]
 name = "jinja2"
-version = "3.1.3"
+version = "3.1.4"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "Jinja2-3.1.3-py3-none-any.whl", hash = "sha256:7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa"},
-    {file = "Jinja2-3.1.3.tar.gz", hash = "sha256:ac8bd6544d4bb2c9792bf3a159e80bba8fda7f07e81bc3aed565432d5925ba90"},
+    {file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"},
+    {file = "jinja2-3.1.4.tar.gz", hash = "sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369"},
 ]

 [package.dependencies]
@ -1566,13 +1566,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>

 [[package]]
 name = "jupyterlab"
-version = "4.1.2"
+version = "4.2.0"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab-4.1.2-py3-none-any.whl", hash = "sha256:aa88193f03cf4d3555f6712f04d74112b5eb85edd7d222c588c7603a26d33c5b"},
-    {file = "jupyterlab-4.1.2.tar.gz", hash = "sha256:5d6348b3ed4085181499f621b7dfb6eb0b1f57f3586857aadfc8e3bf4c4885f9"},
+    {file = "jupyterlab-4.2.0-py3-none-any.whl", hash = "sha256:0dfe9278e25a145362289c555d9beb505697d269c10e99909766af7c440ad3cc"},
+    {file = "jupyterlab-4.2.0.tar.gz", hash = "sha256:356e9205a6a2ab689c47c8fe4919dba6c076e376d03f26baadc05748c2435dd5"},
 ]

 [package.dependencies]
@ -1580,23 +1580,24 @@ async-lru = ">=1.0.0"
 httpx = ">=0.25.0"
 importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
 importlib-resources = {version = ">=1.4", markers = "python_version < \"3.9\""}
-ipykernel = "*"
+ipykernel = ">=6.5.0"
 jinja2 = ">=3.0.3"
 jupyter-core = "*"
 jupyter-lsp = ">=2.0.0"
 jupyter-server = ">=2.4.0,<3"
-jupyterlab-server = ">=2.19.0,<3"
+jupyterlab-server = ">=2.27.1,<3"
 notebook-shim = ">=0.2"
 packaging = "*"
-tomli = {version = "*", markers = "python_version < \"3.11\""}
+tomli = {version = ">=1.2.2", markers = "python_version < \"3.11\""}
 tornado = ">=6.2.0"
 traitlets = "*"

 [package.extras]
-dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.2.0)"]
+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.3.5)"]
 docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<7.3.0)", "sphinx-copybutton"]
-docs-screenshots = ["altair (==5.2.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.1)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.0.post6)", "matplotlib (==3.8.2)", "nbconvert (>=7.0.0)", "pandas (==2.2.0)", "scipy (==1.12.0)", "vega-datasets (==0.9.0)"]
+docs-screenshots = ["altair (==5.3.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.2)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.1.post2)", "matplotlib (==3.8.3)", "nbconvert (>=7.0.0)", "pandas (==2.2.1)", "scipy (==1.12.0)", "vega-datasets (==0.9.0)"]
 test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
+upgrade-extension = ["copier (>=8,<10)", "jinja2-time (<0.3)", "pydantic (<2.0)", "pyyaml-include (<2.0)", "tomli-w (<2.0)"]

 [[package]]
 name = "jupyterlab-pygments"
@ -1611,13 +1612,13 @@ files = [

 [[package]]
 name = "jupyterlab-server"
-version = "2.25.3"
+version = "2.27.1"
 description = "A set of server components for JupyterLab and JupyterLab like applications."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab_server-2.25.3-py3-none-any.whl", hash = "sha256:c48862519fded9b418c71645d85a49b2f0ec50d032ba8316738e9276046088c1"},
-    {file = "jupyterlab_server-2.25.3.tar.gz", hash = "sha256:846f125a8a19656611df5b03e5912c8393cea6900859baa64fa515eb64a8dc40"},
+    {file = "jupyterlab_server-2.27.1-py3-none-any.whl", hash = "sha256:f5e26156e5258b24d532c84e7c74cc212e203bff93eb856f81c24c16daeecc75"},
+    {file = "jupyterlab_server-2.27.1.tar.gz", hash = "sha256:097b5ac709b676c7284ac9c5e373f11930a561f52cd5a86e4fc7e5a9c8a8631d"},
 ]

 [package.dependencies]
@ -1633,7 +1634,7 @@ requests = ">=2.31"
 [package.extras]
 docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi (>0.8)"]
 openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"]
-test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
+test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0,<8)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]

 [[package]]
 name = "jupyterlab-widgets"
@ -1648,13 +1649,13 @@ files = [

 [[package]]
 name = "jupytext"
-version = "1.16.1"
+version = "1.16.2"
 description = "Jupyter notebooks as Markdown documents, Julia, Python or R scripts"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupytext-1.16.1-py3-none-any.whl", hash = "sha256:796ec4f68ada663569e5d38d4ef03738a01284bfe21c943c485bc36433898bd0"},
-    {file = "jupytext-1.16.1.tar.gz", hash = "sha256:68c7b68685e870e80e60fda8286fbd6269e9c74dc1df4316df6fe46eabc94c99"},
+    {file = "jupytext-1.16.2-py3-none-any.whl", hash = "sha256:197a43fef31dca612b68b311e01b8abd54441c7e637810b16b6cb8f2ab66065e"},
+    {file = "jupytext-1.16.2.tar.gz", hash = "sha256:8627dd9becbbebd79cc4a4ed4727d89d78e606b4b464eab72357b3b029023a14"},
 ]

 [package.dependencies]
@ -1663,16 +1664,16 @@ mdit-py-plugins = "*"
 nbformat = "*"
 packaging = "*"
 pyyaml = "*"
-toml = "*"
+tomli = {version = "*", markers = "python_version < \"3.11\""}

 [package.extras]
-dev = ["jupytext[test-cov,test-external]"]
+dev = ["autopep8", "black", "flake8", "gitpython", "ipykernel", "isort", "jupyter-fs (<0.4.0)", "jupyter-server (!=2.11)", "nbconvert", "pre-commit", "pytest", "pytest-cov (>=2.6.1)", "pytest-randomly", "pytest-xdist", "sphinx-gallery (<0.8)"]
 docs = ["myst-parser", "sphinx", "sphinx-copybutton", "sphinx-rtd-theme"]
 test = ["pytest", "pytest-randomly", "pytest-xdist"]
-test-cov = ["jupytext[test-integration]", "pytest-cov (>=2.6.1)"]
-test-external = ["autopep8", "black", "flake8", "gitpython", "isort", "jupyter-fs (<0.4.0)", "jupytext[test-integration]", "pre-commit", "sphinx-gallery (<0.8)"]
-test-functional = ["jupytext[test]"]
-test-integration = ["ipykernel", "jupyter-server (!=2.11)", "jupytext[test-functional]", "nbconvert"]
+test-cov = ["ipykernel", "jupyter-server (!=2.11)", "nbconvert", "pytest", "pytest-cov (>=2.6.1)", "pytest-randomly", "pytest-xdist"]
+test-external = ["autopep8", "black", "flake8", "gitpython", "ipykernel", "isort", "jupyter-fs (<0.4.0)", "jupyter-server (!=2.11)", "nbconvert", "pre-commit", "pytest", "pytest-randomly", "pytest-xdist", "sphinx-gallery (<0.8)"]
+test-functional = ["pytest", "pytest-randomly", "pytest-xdist"]
+test-integration = ["ipykernel", "jupyter-server (!=2.11)", "nbconvert", "pytest", "pytest-randomly", "pytest-xdist"]
 test-ui = ["calysto-bash"]

 [[package]]
@ -2048,13 +2049,13 @@ files = [

 [[package]]
 name = "mike"
-version = "2.1.0"
+version = "2.1.1"
 description = "Manage multiple versions of your MkDocs-powered documentation"
 optional = false
 python-versions = "*"
 files = [
-    {file = "mike-2.1.0-py3-none-any.whl", hash = "sha256:b3885f9b9e31fc4b0d61de473750d38ac170a6b291585076effb51a806245608"},
-    {file = "mike-2.1.0.tar.gz", hash = "sha256:f0b8e51cbfae1273d648ffb602a4ab3061e57972ca1cd6836df1c51c01a36eb5"},
+    {file = "mike-2.1.1-py3-none-any.whl", hash = "sha256:0b1d01a397a423284593eeb1b5f3194e37169488f929b860c9bfe95c0d5efb79"},
+    {file = "mike-2.1.1.tar.gz", hash = "sha256:f39ed39f3737da83ad0adc33e9f885092ed27f8c9e7ff0523add0480352a2c22"},
 ]

 [package.dependencies]
@ -2064,6 +2065,7 @@ jinja2 = ">=2.7"
 mkdocs = ">=1.0"
 pyparsing = ">=3.0"
 pyyaml = ">=5.1"
+pyyaml-env-tag = "*"
 verspec = "*"

 [package.extras]
@ -2197,13 +2199,13 @@ pygments = ">2.12.0"

 [[package]]
 name = "mkdocs-material"
-version = "9.5.20"
+version = "9.5.24"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.20-py3-none-any.whl", hash = "sha256:ad0094a7597bcb5d0cc3e8e543a10927c2581f7f647b9bb4861600f583180f9b"},
-    {file = "mkdocs_material-9.5.20.tar.gz", hash = "sha256:986eef0250d22f70fb06ce0f4eac64cc92bd797a589ec3892ce31fad976fe3da"},
+    {file = "mkdocs_material-9.5.24-py3-none-any.whl", hash = "sha256:e12cd75954c535b61e716f359cf2a5056bf4514889d17161fdebd5df4b0153c6"},
+    {file = "mkdocs_material-9.5.24.tar.gz", hash = "sha256:02d5aaba0ee755e707c3ef6e748f9acb7b3011187c0ea766db31af8905078a34"},
 ]

 [package.dependencies]
@ -2239,13 +2241,13 @@ files = [

 [[package]]
 name = "mkdocstrings"
-version = "0.25.0"
+version = "0.25.1"
 description = "Automatic documentation from sources, for MkDocs."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocstrings-0.25.0-py3-none-any.whl", hash = "sha256:df1b63f26675fcde8c1b77e7ea996cd2f93220b148e06455428f676f5dc838f1"},
-    {file = "mkdocstrings-0.25.0.tar.gz", hash = "sha256:066986b3fb5b9ef2d37c4417255a808f7e63b40ff8f67f6cab8054d903fbc91d"},
+    {file = "mkdocstrings-0.25.1-py3-none-any.whl", hash = "sha256:da01fcc2670ad61888e8fe5b60afe9fee5781017d67431996832d63e887c2e51"},
+    {file = "mkdocstrings-0.25.1.tar.gz", hash = "sha256:c3a2515f31577f311a9ee58d089e4c51fc6046dbd9e9b4c3de4c3194667fe9bf"},
 ]

 [package.dependencies]
@ -2483,26 +2485,26 @@ setuptools = "*"

 [[package]]
 name = "notebook"
-version = "7.1.3"
+version = "7.2.0"
 description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "notebook-7.1.3-py3-none-any.whl", hash = "sha256:919b911e59f41f6e3857ce93c9d93535ba66bb090059712770e5968c07e1004d"},
-    {file = "notebook-7.1.3.tar.gz", hash = "sha256:41fcebff44cf7bb9377180808bcbae066629b55d8c7722f1ebbe75ca44f9cfc1"},
+    {file = "notebook-7.2.0-py3-none-any.whl", hash = "sha256:b4752d7407d6c8872fc505df0f00d3cae46e8efb033b822adacbaa3f1f3ce8f5"},
+    {file = "notebook-7.2.0.tar.gz", hash = "sha256:34a2ba4b08ad5d19ec930db7484fb79746a1784be9e1a5f8218f9af8656a141f"},
 ]

 [package.dependencies]
 jupyter-server = ">=2.4.0,<3"
-jupyterlab = ">=4.1.1,<4.2"
-jupyterlab-server = ">=2.22.1,<3"
+jupyterlab = ">=4.2.0,<4.3"
+jupyterlab-server = ">=2.27.1,<3"
 notebook-shim = ">=0.2,<0.3"
 tornado = ">=6.2.0"

 [package.extras]
 dev = ["hatch", "pre-commit"]
 docs = ["myst-parser", "nbsphinx", "pydata-sphinx-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
-test = ["importlib-resources (>=5.0)", "ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[test] (>=2.22.1,<3)", "nbval", "pytest (>=7.0)", "pytest-console-scripts", "pytest-timeout", "pytest-tornasync", "requests"]
+test = ["importlib-resources (>=5.0)", "ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[test] (>=2.27.1,<3)", "nbval", "pytest (>=7.0)", "pytest-console-scripts", "pytest-timeout", "pytest-tornasync", "requests"]

 [[package]]
 name = "notebook-shim"
@ -3037,13 +3039,13 @@ tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}

 [[package]]
 name = "pytest"
-version = "8.2.0"
+version = "8.2.1"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytest-8.2.0-py3-none-any.whl", hash = "sha256:1733f0620f6cda4095bbf0d9ff8022486e91892245bb9e7d5542c018f612f233"},
-    {file = "pytest-8.2.0.tar.gz", hash = "sha256:d507d4482197eac0ba2bae2e9babf0672eb333017bcedaa5fb1a3d42c1174b3f"},
+    {file = "pytest-8.2.1-py3-none-any.whl", hash = "sha256:faccc5d332b8c3719f40283d0d44aa5cf101cec36f88cde9ed8f2bc0538612b1"},
+    {file = "pytest-8.2.1.tar.gz", hash = "sha256:5046e5b46d8e4cac199c373041f26be56fdb81eb4e67dc11d4e10811fc3408fd"},
 ]

 [package.dependencies]
@ -3459,13 +3461,13 @@ files = [

 [[package]]
 name = "requests"
-version = "2.31.0"
+version = "2.32.2"
 description = "Python HTTP for Humans."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "requests-2.31.0-py3-none-any.whl", hash = "sha256:58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f"},
-    {file = "requests-2.31.0.tar.gz", hash = "sha256:942c5a758f98d790eaed1a29cb6eefc7ffb0d1cf7af05c3d2791656dbd6ad1e1"},
+    {file = "requests-2.32.2-py3-none-any.whl", hash = "sha256:fc06670dd0ed212426dfeb94fc1b983d917c4f9847c863f313c9dfaaffb7c23c"},
+    {file = "requests-2.32.2.tar.gz", hash = "sha256:dd951ff5ecf3e3b3aa26b40703ba77495dab41da839ae72ef3c8e5d8e2433289"},
 ]

 [package.dependencies]
@ -3660,28 +3662,28 @@ files = [

 [[package]]
 name = "ruff"
-version = "0.4.2"
+version = "0.4.5"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ruff-0.4.2-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:8d14dc8953f8af7e003a485ef560bbefa5f8cc1ad994eebb5b12136049bbccc5"},
-    {file = "ruff-0.4.2-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:24016ed18db3dc9786af103ff49c03bdf408ea253f3cb9e3638f39ac9cf2d483"},
-    {file = "ruff-0.4.2-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0e2e06459042ac841ed510196c350ba35a9b24a643e23db60d79b2db92af0c2b"},
-    {file = "ruff-0.4.2-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:3afabaf7ba8e9c485a14ad8f4122feff6b2b93cc53cd4dad2fd24ae35112d5c5"},
-    {file = "ruff-0.4.2-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:799eb468ea6bc54b95527143a4ceaf970d5aa3613050c6cff54c85fda3fde480"},
-    {file = "ruff-0.4.2-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:ec4ba9436a51527fb6931a8839af4c36a5481f8c19e8f5e42c2f7ad3a49f5069"},
-    {file = "ruff-0.4.2-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6a2243f8f434e487c2a010c7252150b1fdf019035130f41b77626f5655c9ca22"},
-    {file = "ruff-0.4.2-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8772130a063f3eebdf7095da00c0b9898bd1774c43b336272c3e98667d4fb8fa"},
-    {file = "ruff-0.4.2-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6ab165ef5d72392b4ebb85a8b0fbd321f69832a632e07a74794c0e598e7a8376"},
-    {file = "ruff-0.4.2-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:1f32cadf44c2020e75e0c56c3408ed1d32c024766bd41aedef92aa3ca28eef68"},
-    {file = "ruff-0.4.2-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:22e306bf15e09af45ca812bc42fa59b628646fa7c26072555f278994890bc7ac"},
-    {file = "ruff-0.4.2-py3-none-musllinux_1_2_i686.whl", hash = "sha256:82986bb77ad83a1719c90b9528a9dd663c9206f7c0ab69282af8223566a0c34e"},
-    {file = "ruff-0.4.2-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:652e4ba553e421a6dc2a6d4868bc3b3881311702633eb3672f9f244ded8908cd"},
-    {file = "ruff-0.4.2-py3-none-win32.whl", hash = "sha256:7891ee376770ac094da3ad40c116258a381b86c7352552788377c6eb16d784fe"},
-    {file = "ruff-0.4.2-py3-none-win_amd64.whl", hash = "sha256:5ec481661fb2fd88a5d6cf1f83403d388ec90f9daaa36e40e2c003de66751798"},
-    {file = "ruff-0.4.2-py3-none-win_arm64.whl", hash = "sha256:cbd1e87c71bca14792948c4ccb51ee61c3296e164019d2d484f3eaa2d360dfaf"},
-    {file = "ruff-0.4.2.tar.gz", hash = "sha256:33bcc160aee2520664bc0859cfeaebc84bb7323becff3f303b8f1f2d81cb4edc"},
+    {file = "ruff-0.4.5-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:8f58e615dec58b1a6b291769b559e12fdffb53cc4187160a2fc83250eaf54e96"},
+    {file = "ruff-0.4.5-py3-none-macosx_11_0_arm64.whl", hash = "sha256:84dd157474e16e3a82745d2afa1016c17d27cb5d52b12e3d45d418bcc6d49264"},
+    {file = "ruff-0.4.5-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:25f483ad9d50b00e7fd577f6d0305aa18494c6af139bce7319c68a17180087f4"},
+    {file = "ruff-0.4.5-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:63fde3bf6f3ad4e990357af1d30e8ba2730860a954ea9282c95fc0846f5f64af"},
+    {file = "ruff-0.4.5-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:78e3ba4620dee27f76bbcad97067766026c918ba0f2d035c2fc25cbdd04d9c97"},
+    {file = "ruff-0.4.5-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:441dab55c568e38d02bbda68a926a3d0b54f5510095c9de7f95e47a39e0168aa"},
+    {file = "ruff-0.4.5-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1169e47e9c4136c997f08f9857ae889d614c5035d87d38fda9b44b4338909cdf"},
+    {file = "ruff-0.4.5-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:755ac9ac2598a941512fc36a9070a13c88d72ff874a9781493eb237ab02d75df"},
+    {file = "ruff-0.4.5-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f4b02a65985be2b34b170025a8b92449088ce61e33e69956ce4d316c0fe7cce0"},
+    {file = "ruff-0.4.5-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:75a426506a183d9201e7e5664de3f6b414ad3850d7625764106f7b6d0486f0a1"},
+    {file = "ruff-0.4.5-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:6e1b139b45e2911419044237d90b60e472f57285950e1492c757dfc88259bb06"},
+    {file = "ruff-0.4.5-py3-none-musllinux_1_2_i686.whl", hash = "sha256:a6f29a8221d2e3d85ff0c7b4371c0e37b39c87732c969b4d90f3dad2e721c5b1"},
+    {file = "ruff-0.4.5-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:d6ef817124d72b54cc923f3444828ba24fa45c3164bc9e8f1813db2f3d3a8a11"},
+    {file = "ruff-0.4.5-py3-none-win32.whl", hash = "sha256:aed8166c18b1a169a5d3ec28a49b43340949e400665555b51ee06f22813ef062"},
+    {file = "ruff-0.4.5-py3-none-win_amd64.whl", hash = "sha256:b0b03c619d2b4350b4a27e34fd2ac64d0dabe1afbf43de57d0f9d8a05ecffa45"},
+    {file = "ruff-0.4.5-py3-none-win_arm64.whl", hash = "sha256:9d15de3425f53161b3f5a5658d4522e4eee5ea002bf2ac7aa380743dd9ad5fba"},
+    {file = "ruff-0.4.5.tar.gz", hash = "sha256:286eabd47e7d4d521d199cab84deca135557e6d1e0f0d01c29e757c3cb151b54"},
 ]

 [[package]]
@ -3913,17 +3915,6 @@ webencodings = ">=0.4"
 doc = ["sphinx", "sphinx_rtd_theme"]
 test = ["flake8", "isort", "pytest"]

-[[package]]
-name = "toml"
-version = "0.10.2"
-description = "Python Library for Tom's Obvious, Minimal Language"
-optional = false
-python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
-files = [
-    {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"},
-    {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"},
-]
-
 [[package]]
 name = "tomli"
 version = "2.0.1"
@ -4019,13 +4010,13 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,

 [[package]]
 name = "twine"
-version = "5.0.0"
+version = "5.1.0"
 description = "Collection of utilities for publishing packages on PyPI"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "twine-5.0.0-py3-none-any.whl", hash = "sha256:a262933de0b484c53408f9edae2e7821c1c45a3314ff2df9bdd343aa7ab8edc0"},
-    {file = "twine-5.0.0.tar.gz", hash = "sha256:89b0cc7d370a4b66421cc6102f269aa910fe0f1861c124f573cf2ddedbc10cf4"},
+    {file = "twine-5.1.0-py3-none-any.whl", hash = "sha256:fe1d814395bfe50cfbe27783cb74efe93abeac3f66deaeb6c8390e4e92bacb43"},
+    {file = "twine-5.1.0.tar.gz", hash = "sha256:4d74770c88c4fcaf8134d2a6a9d863e40f08255ff7d8e2acb3cbbd57d25f6e9d"},
 ]

 [package.dependencies]
@ -4267,4 +4258,4 @@ desktop = ["opencv-python"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "29af5aa06f97e77a2dba94c5a6d77d7d1903448724df07416026a378d3c6a64d"
+content-hash = "ad8402ec1767f9427ab38bad7dab54b302a30f9e08b6489fad224c8481745b37"
--- a/pyproject.toml
+++ b/pyproject.toml
@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.21.0rc3"
+version = "0.21.0rc5"
 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>"]
@ -42,7 +42,7 @@ pyyaml = ">=5.3"
 defusedxml = "^0.7.1"
 opencv-python = { version = ">=4.5.5.64", optional = true }
 opencv-python-headless = ">=4.5.5.64"
-requests = { version = ">=2.26.0,<=2.31.0", optional = true }
+requests = { version = ">=2.26.0,<=2.32.2", optional = true }
 tqdm = { version = ">=4.62.3,<=4.66.4", optional = true }
 pillow = ">=9.4"

--- a/supervision/init.py
+++ b/supervision/init.py
@ -23,6 +23,7 @@ from supervision.annotators.core import (
    PercentageBarAnnotator,
    PixelateAnnotator,
    PolygonAnnotator,
+    RichLabelAnnotator,
    RoundBoxAnnotator,
    TraceAnnotator,
    TriangleAnnotator,
@ -34,6 +35,7 @@ from supervision.dataset.core import (
    ClassificationDataset,
    DetectionDataset,
 )
+from supervision.dataset.utils import mask_to_rle, rle_to_mask
 from supervision.detection.annotate import BoxAnnotator
 from supervision.detection.core import Detections
 from supervision.detection.line_zone import LineZone, LineZoneAnnotator
@ -46,12 +48,17 @@ from supervision.detection.utils import (
    box_iou_batch,
    box_non_max_suppression,
    calculate_masks_centroids,
+    clip_boxes,
+    contains_holes,
+    contains_multiple_segments,
    filter_polygons_by_area,
    mask_iou_batch,
    mask_non_max_suppression,
    mask_to_polygons,
    mask_to_xyxy,
    move_boxes,
+    move_masks,
+    pad_boxes,
    polygon_to_mask,
    polygon_to_xyxy,
    scale_boxes,
@ -69,7 +76,11 @@ 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 EdgeAnnotator, VertexAnnotator
+from supervision.keypoint.annotators import (
+    EdgeAnnotator,
+    VertexAnnotator,
+    VertexLabelAnnotator,
+)
 from supervision.keypoint.core import KeyPoints
 from supervision.metrics.detection import ConfusionMatrix, MeanAveragePrecision
 from supervision.tracker.byte_tracker.core import ByteTrack
--- a/supervision/annotators/core.py
+++ b/supervision/annotators/core.py
@ -3,9 +3,15 @@ from typing import List, Optional, Tuple, Union

 import cv2
 import numpy as np
+from PIL import Image, ImageDraw, ImageFont

 from supervision.annotators.base import BaseAnnotator, ImageType
-from supervision.annotators.utils import ColorLookup, Trace, resolve_color
+from supervision.annotators.utils import (
+    ColorLookup,
+    Trace,
+    resolve_color,
+    resolve_text_background_xyxy,
+)
 from supervision.config import CLASS_NAME_DATA_FIELD, ORIENTED_BOX_COORDINATES
 from supervision.detection.core import Detections
 from supervision.detection.utils import clip_boxes, mask_to_polygons
@ -936,59 +942,6 @@ class LabelAnnotator:
        self.text_anchor: Position = text_position
        self.color_lookup: ColorLookup = color_lookup

-    @staticmethod
-    def resolve_text_background_xyxy(
-        center_coordinates: Tuple[int, int],
-        text_wh: Tuple[int, int],
-        position: Position,
-    ) -> Tuple[int, int, int, int]:
-        center_x, center_y = center_coordinates
-        text_w, text_h = text_wh
-
-        if position == Position.TOP_LEFT:
-            return center_x, center_y - text_h, center_x + text_w, center_y
-        elif position == Position.TOP_RIGHT:
-            return center_x - text_w, center_y - text_h, center_x, center_y
-        elif position == Position.TOP_CENTER:
-            return (
-                center_x - text_w // 2,
-                center_y - text_h,
-                center_x + text_w // 2,
-                center_y,
-            )
-        elif position == Position.CENTER or position == Position.CENTER_OF_MASS:
-            return (
-                center_x - text_w // 2,
-                center_y - text_h // 2,
-                center_x + text_w // 2,
-                center_y + text_h // 2,
-            )
-        elif position == Position.BOTTOM_LEFT:
-            return center_x, center_y, center_x + text_w, center_y + text_h
-        elif position == Position.BOTTOM_RIGHT:
-            return center_x - text_w, center_y, center_x, center_y + text_h
-        elif position == Position.BOTTOM_CENTER:
-            return (
-                center_x - text_w // 2,
-                center_y,
-                center_x + text_w // 2,
-                center_y + text_h,
-            )
-        elif position == Position.CENTER_LEFT:
-            return (
-                center_x - text_w,
-                center_y - text_h // 2,
-                center_x,
-                center_y + text_h // 2,
-            )
-        elif position == Position.CENTER_RIGHT:
-            return (
-                center_x,
-                center_y - text_h // 2,
-                center_x + text_w,
-                center_y + text_h // 2,
-            )
-
    @convert_for_annotation_method
    def annotate(
        self,
@ -1056,9 +1009,11 @@ class LabelAnnotator:
                color=self.color,
                detections=detections,
                detection_idx=detection_idx,
-                color_lookup=self.color_lookup
-                if custom_color_lookup is None
-                else custom_color_lookup,
+                color_lookup=(
+                    self.color_lookup
+                    if custom_color_lookup is None
+                    else custom_color_lookup
+                ),
            )

            if labels is not None:
@ -1078,7 +1033,7 @@ class LabelAnnotator:
            )[0]
            text_w_padded = text_w + 2 * self.text_padding
            text_h_padded = text_h + 2 * self.text_padding
-            text_background_xyxy = self.resolve_text_background_xyxy(
+            text_background_xyxy = resolve_text_background_xyxy(
                center_coordinates=tuple(center_coordinates),
                text_wh=(text_w_padded, text_h_padded),
                position=self.text_anchor,
@ -1148,6 +1103,165 @@ class LabelAnnotator:
        return scene


+class RichLabelAnnotator:
+    """
+    A class for annotating labels on an image using provided detections,
+    with support for Unicode characters by using a custom font.
+    """
+
+    def __init__(
+        self,
+        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
+        text_color: Color = Color.WHITE,
+        font_path: str = None,
+        font_size: int = 10,
+        text_padding: int = 10,
+        text_position: Position = Position.TOP_LEFT,
+        color_lookup: ColorLookup = ColorLookup.CLASS,
+        border_radius: int = 0,
+    ):
+        """
+        Args:
+            color (Union[Color, ColorPalette]): The color or color palette to use for
+                annotating the text background.
+            text_color (Color): The color to use for the text.
+            font_path (str): Path to the font file (e.g., ".ttf" or ".otf") to use for
+                rendering text. If `None`, the default PIL font will be used.
+            font_size (int): Font size for the text.
+            text_padding (int): Padding around the text within its background box.
+            text_position (Position): Position of the text relative to the detection.
+                Possible values are defined in the `Position` enum.
+            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
+                Options are `INDEX`, `CLASS`, `TRACK`.
+            border_radius (int): The radius to apply round edges. If the selected
+                value is higher than the lower dimension, width or height, is clipped.
+        """
+        self.color = color
+        self.text_color = text_color
+        self.text_padding = text_padding
+        self.text_anchor = text_position
+        self.color_lookup = color_lookup
+        self.border_radius = border_radius
+        if font_path is not None:
+            try:
+                self.font = ImageFont.truetype(font_path, font_size)
+            except OSError:
+                print(f"Font path '{font_path}' not found. Using PIL's default font.")
+                self.font = ImageFont.load_default(size=font_size)
+        else:
+            self.font = ImageFont.load_default(size=font_size)
+
+    def annotate(
+        self,
+        scene: ImageType,
+        detections: Detections,
+        labels: List[str] = None,
+        custom_color_lookup: Optional[np.ndarray] = None,
+    ) -> ImageType:
+        """
+        Annotates the given scene with labels based on the provided
+        detections, with support for Unicode characters.
+
+        Args:
+            scene (ImageType): The image where labels will be drawn.
+                `ImageType` is a flexible type, accepting either `numpy.ndarray`
+                or `PIL.Image.Image`.
+            detections (Detections): Object detections to annotate.
+            labels (List[str]): Optional. Custom labels for each detection.
+            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
+                Allows to override the default color mapping strategy.
+
+        Returns:
+            The annotated image, matching the type of `scene` (`numpy.ndarray`
+                or `PIL.Image.Image`)
+
+        Example:
+            ```python
+            import supervision as sv
+
+            image = ...
+            detections = sv.Detections(...)
+
+            labels = [
+                f"{class_name} {confidence:.2f}"
+                for class_name, confidence
+                in zip(detections['class_name'], detections.confidence)
+            ]
+
+            rich_label_annotator = sv.RichLabelAnnotator(font_path="path/to/font.ttf")
+            annotated_frame = label_annotator.annotate(
+                scene=image.copy(),
+                detections=detections,
+                labels=labels
+            )
+            ```
+
+        """
+        if isinstance(scene, np.ndarray):
+            scene = Image.fromarray(cv2.cvtColor(scene, cv2.COLOR_BGR2RGB))
+        draw = ImageDraw.Draw(scene)
+        anchors_coordinates = detections.get_anchors_coordinates(
+            anchor=self.text_anchor
+        ).astype(int)
+        if labels is not None and len(labels) != len(detections):
+            raise ValueError(
+                f"The number of labels provided ({len(labels)}) does not match the "
+                f"number of detections ({len(detections)}). Each detection should have "
+                f"a corresponding label. This discrepancy can occur if the labels and "
+                f"detections are not aligned or if an incorrect number of labels has "
+                f"been provided. Please ensure that the labels array has the same "
+                f"length as the Detections object."
+            )
+        for detection_idx, center_coordinates in enumerate(anchors_coordinates):
+            color = resolve_color(
+                color=self.color,
+                detections=detections,
+                detection_idx=detection_idx,
+                color_lookup=(
+                    self.color_lookup
+                    if custom_color_lookup is None
+                    else custom_color_lookup
+                ),
+            )
+            if labels is not None:
+                text = labels[detection_idx]
+            elif detections[CLASS_NAME_DATA_FIELD] is not None:
+                text = detections[CLASS_NAME_DATA_FIELD][detection_idx]
+            elif detections.class_id is not None:
+                text = str(detections.class_id[detection_idx])
+            else:
+                text = str(detection_idx)
+
+            left, top, right, bottom = draw.textbbox((0, 0), text, font=self.font)
+            text_width = right - left
+            text_height = bottom - top
+            text_w_padded = text_width + 2 * self.text_padding
+            text_h_padded = text_height + 2 * self.text_padding
+            text_background_xyxy = resolve_text_background_xyxy(
+                center_coordinates=tuple(center_coordinates),
+                text_wh=(text_w_padded, text_h_padded),
+                position=self.text_anchor,
+            )
+
+            text_x = text_background_xyxy[0] + self.text_padding - left
+            text_y = text_background_xyxy[1] + self.text_padding - top
+
+            draw.rounded_rectangle(
+                text_background_xyxy,
+                radius=self.border_radius,
+                fill=color.as_rgb(),
+                outline=None,
+            )
+            draw.text(
+                xy=(text_x, text_y),
+                text=text,
+                font=self.font,
+                fill=self.text_color.as_rgb(),
+            )
+
+        return scene
+
+
 class BlurAnnotator(BaseAnnotator):
    """
    A class for blurring regions in an image using provided detections.
--- a/supervision/annotators/utils.py
+++ b/supervision/annotators/utils.py
@ -1,5 +1,5 @@
 from enum import Enum
-from typing import Optional, Union
+from typing import Optional, Tuple, Union

 import numpy as np

@ -34,14 +34,14 @@ def resolve_color_idx(
 ) -> int:
    if detection_idx >= len(detections):
        raise ValueError(
-            f"Detection index {detection_idx}"
+            f"Detection index {detection_idx} "
            f"is out of bounds for detections of length {len(detections)}"
        )

    if isinstance(color_lookup, np.ndarray):
        if len(color_lookup) != len(detections):
            raise ValueError(
-                f"Length of color lookup {len(color_lookup)}"
+                f"Length of color lookup {len(color_lookup)} "
                f"does not match length of detections {len(detections)}"
            )
        return color_lookup[detection_idx]
@ -50,19 +50,72 @@ def resolve_color_idx(
    elif color_lookup == ColorLookup.CLASS:
        if detections.class_id is None:
            raise ValueError(
-                "Could not resolve color by class because"
+                "Could not resolve color by class because "
                "Detections do not have class_id"
            )
        return detections.class_id[detection_idx]
    elif color_lookup == ColorLookup.TRACK:
        if detections.tracker_id is None:
            raise ValueError(
-                "Could not resolve color by track because"
+                "Could not resolve color by track because "
                "Detections do not have tracker_id"
            )
        return detections.tracker_id[detection_idx]


+def resolve_text_background_xyxy(
+    center_coordinates: Tuple[int, int],
+    text_wh: Tuple[int, int],
+    position: Position,
+) -> Tuple[int, int, int, int]:
+    center_x, center_y = center_coordinates
+    text_w, text_h = text_wh
+
+    if position == Position.TOP_LEFT:
+        return center_x, center_y - text_h, center_x + text_w, center_y
+    elif position == Position.TOP_RIGHT:
+        return center_x - text_w, center_y - text_h, center_x, center_y
+    elif position == Position.TOP_CENTER:
+        return (
+            center_x - text_w // 2,
+            center_y - text_h,
+            center_x + text_w // 2,
+            center_y,
+        )
+    elif position == Position.CENTER or position == Position.CENTER_OF_MASS:
+        return (
+            center_x - text_w // 2,
+            center_y - text_h // 2,
+            center_x + text_w // 2,
+            center_y + text_h // 2,
+        )
+    elif position == Position.BOTTOM_LEFT:
+        return center_x, center_y, center_x + text_w, center_y + text_h
+    elif position == Position.BOTTOM_RIGHT:
+        return center_x - text_w, center_y, center_x, center_y + text_h
+    elif position == Position.BOTTOM_CENTER:
+        return (
+            center_x - text_w // 2,
+            center_y,
+            center_x + text_w // 2,
+            center_y + text_h,
+        )
+    elif position == Position.CENTER_LEFT:
+        return (
+            center_x - text_w,
+            center_y - text_h // 2,
+            center_x,
+            center_y + text_h // 2,
+        )
+    elif position == Position.CENTER_RIGHT:
+        return (
+            center_x,
+            center_y - text_h // 2,
+            center_x + text_w,
+            center_y + text_h // 2,
+        )
+
+
 def get_color_by_index(color: Union[Color, ColorPalette], idx: int) -> Color:
    if isinstance(color, ColorPalette):
        return color.by_idx(idx)
--- a/supervision/dataset/core.py
+++ b/supervision/dataset/core.py
@ -116,13 +116,12 @@ class DetectionDataset(BaseDataset):
            Tuple[DetectionDataset, DetectionDataset]: A tuple containing
                the training and testing datasets.

-        Example:
+        Examples:
            ```python
            import supervision as sv

            ds = sv.DetectionDataset(...)
-            train_ds, test_ds = ds.split(split_ratio=0.7,
-                                         random_state=42, shuffle=True)
+            train_ds, test_ds = ds.split(split_ratio=0.7, random_state=42, shuffle=True)
            len(train_ds), len(test_ds)
            # (700, 300)
            ```
@ -229,7 +228,7 @@ class DetectionDataset(BaseDataset):
            DetectionDataset: A DetectionDataset instance containing
                the loaded images and annotations.

-        Example:
+        Examples:
            ```python
            import roboflow
            from roboflow import Roboflow
@ -286,7 +285,7 @@ class DetectionDataset(BaseDataset):
            DetectionDataset: A DetectionDataset instance
                containing the loaded images and annotations.

-        Example:
+        Examples:
            ```python
            import roboflow
            from roboflow import Roboflow
@ -391,7 +390,7 @@ class DetectionDataset(BaseDataset):
            DetectionDataset: A DetectionDataset instance containing
                the loaded images and annotations.

-        Example:
+        Examples:
            ```python
            import roboflow
            from roboflow import Roboflow
@ -431,6 +430,20 @@ class DetectionDataset(BaseDataset):
        Exports the dataset to COCO format. This method saves the
        images and their corresponding annotations in COCO format.

+        !!! tip
+
+            The format of the mask is determined automatically based on its structure:
+
+            - If a mask contains multiple disconnected components or holes, it will be
+            saved using the Run-Length Encoding (RLE) format for efficient storage and
+            processing.
+            - If a mask consists of a single, contiguous region without any holes, it
+            will be encoded as a polygon, preserving the outline of the object.
+
+            This automatic selection ensures that the masks are stored in the most
+            appropriate and space-efficient format, complying with COCO dataset
+            standards.
+
        Args:
            images_directory_path (Optional[str]): The path to the directory
                where the images should be saved.
@ -482,7 +495,7 @@ class DetectionDataset(BaseDataset):
            (DetectionDataset): A single `DetectionDataset` object containing
            the merged data from the input list.

-        Example:
+        Examples:
            ```python
            import supervision as sv

@ -567,13 +580,12 @@ class ClassificationDataset(BaseDataset):
            Tuple[ClassificationDataset, ClassificationDataset]: A tuple containing
            the training and testing datasets.

-        Example:
+        Examples:
            ```python
            import supervision as sv

            cd = sv.ClassificationDataset(...)
-            train_cd,test_cd = cd.split(split_ratio=0.7,
-                                        random_state=42,shuffle=True)
+            train_cd,test_cd = cd.split(split_ratio=0.7, random_state=42,shuffle=True)
            len(train_cd), len(test_cd)
            # (700, 300)
            ```
@ -635,7 +647,7 @@ class ClassificationDataset(BaseDataset):
        Returns:
            ClassificationDataset: The dataset.

-        Example:
+        Examples:
            ```python
            import roboflow
            from roboflow import Roboflow
--- a/supervision/dataset/formats/coco.py
+++ b/supervision/dataset/formats/coco.py
@ -5,13 +5,20 @@ from typing import Dict, List, Tuple

 import cv2
 import numpy as np
+import numpy.typing as npt

 from supervision.dataset.utils import (
    approximate_mask_with_polygons,
    map_detections_class_id,
+    mask_to_rle,
+    rle_to_mask,
 )
 from supervision.detection.core import Detections
-from supervision.detection.utils import polygon_to_mask
+from supervision.detection.utils import (
+    contains_holes,
+    contains_multiple_segments,
+    polygon_to_mask,
+)
 from supervision.utils.file import read_json_file, save_json_file


@ -57,13 +64,24 @@ def group_coco_annotations_by_image_id(
    return annotations


-def _polygons_to_masks(
-    polygons: List[np.ndarray], resolution_wh: Tuple[int, int]
-) -> np.ndarray:
+def coco_annotations_to_masks(
+    image_annotations: List[dict], resolution_wh: Tuple[int, int]
+) -> npt.NDArray[np.bool_]:
    return np.array(
        [
-            polygon_to_mask(polygon=polygon, resolution_wh=resolution_wh)
-            for polygon in polygons
+            rle_to_mask(
+                rle=np.array(image_annotation["segmentation"]["counts"]),
+                resolution_wh=resolution_wh,
+            )
+            if image_annotation["iscrowd"]
+            else polygon_to_mask(
+                polygon=np.reshape(
+                    np.asarray(image_annotation["segmentation"], dtype=np.int32),
+                    (-1, 2),
+                ),
+                resolution_wh=resolution_wh,
+            )
+            for image_annotation in image_annotations
        ],
        dtype=bool,
    )
@ -83,13 +101,9 @@ def coco_annotations_to_detections(
    xyxy[:, 2:4] += xyxy[:, 0:2]

    if with_masks:
-        polygons = [
-            np.reshape(
-                np.asarray(image_annotation["segmentation"], dtype=np.int32), (-1, 2)
-            )
-            for image_annotation in image_annotations
-        ]
-        mask = _polygons_to_masks(polygons=polygons, resolution_wh=resolution_wh)
+        mask = coco_annotations_to_masks(
+            image_annotations=image_annotations, resolution_wh=resolution_wh
+        )
        return Detections(
            class_id=np.asarray(class_ids, dtype=int), xyxy=xyxy, mask=mask
        )
@ -108,24 +122,35 @@ def detections_to_coco_annotations(
    coco_annotations = []
    for xyxy, mask, _, class_id, _, _ in detections:
        box_width, box_height = xyxy[2] - xyxy[0], xyxy[3] - xyxy[1]
-        polygon = []
+        segmentation = []
+        iscrowd = 0
        if mask is not None:
-            polygon = list(
-                approximate_mask_with_polygons(
-                    mask=mask,
-                    min_image_area_percentage=min_image_area_percentage,
-                    max_image_area_percentage=max_image_area_percentage,
-                    approximation_percentage=approximation_percentage,
-                )[0].flatten()
-            )
+            iscrowd = contains_holes(mask=mask) or contains_multiple_segments(mask=mask)
+
+            if iscrowd:
+                segmentation = {
+                    "counts": mask_to_rle(mask=mask),
+                    "size": list(mask.shape[:2]),
+                }
+            else:
+                segmentation = [
+                    list(
+                        approximate_mask_with_polygons(
+                            mask=mask,
+                            min_image_area_percentage=min_image_area_percentage,
+                            max_image_area_percentage=max_image_area_percentage,
+                            approximation_percentage=approximation_percentage,
+                        )[0].flatten()
+                    )
+                ]
        coco_annotation = {
            "id": annotation_id,
            "image_id": image_id,
            "category_id": int(class_id),
            "bbox": [xyxy[0], xyxy[1], box_width, box_height],
            "area": box_width * box_height,
-            "segmentation": [polygon] if polygon else [],
-            "iscrowd": 0,
+            "segmentation": segmentation,
+            "iscrowd": iscrowd,
        }
        coco_annotations.append(coco_annotation)
        annotation_id += 1
--- a/supervision/dataset/utils.py
+++ b/supervision/dataset/utils.py
@ -2,10 +2,11 @@ import copy
 import os
 import random
 from pathlib import Path
-from typing import Dict, List, Optional, Tuple, TypeVar
+from typing import Dict, List, Optional, Tuple, TypeVar, Union

 import cv2
 import numpy as np
+import numpy.typing as npt

 from supervision.detection.core import Detections
 from supervision.detection.utils import (
@ -129,3 +130,123 @@ def train_test_split(

    split_index = int(len(data) * train_ratio)
    return data[:split_index], data[split_index:]
+
+
+def rle_to_mask(
+    rle: Union[npt.NDArray[np.int_], List[int]], resolution_wh: Tuple[int, int]
+) -> npt.NDArray[np.bool_]:
+    """
+    Converts run-length encoding (RLE) to a binary mask.
+
+    Args:
+        rle (Union[npt.NDArray[np.int_], List[int]]): The 1D RLE array, the format
+            used in the COCO dataset (column-wise encoding, values of an array with
+            even indices represent the number of pixels assigned as background,
+            values of an array with odd indices represent the number of pixels
+            assigned as foreground object).
+        resolution_wh (Tuple[int, int]): The width (w) and height (h)
+            of the desired binary mask.
+
+    Returns:
+        The generated 2D Boolean mask of shape `(h, w)`, where the foreground object is
+            marked with `True`'s and the rest is filled with `False`'s.
+
+    Raises:
+        AssertionError: If the sum of pixels encoded in RLE differs from the
+            number of pixels in the expected mask (computed based on resolution_wh).
+
+    Examples:
+        ```python
+        import supervision as sv
+
+        sv.rle_to_mask([5, 2, 2, 2, 5], (4, 4))
+        # array([
+        #     [False, False, False, False],
+        #     [False, True,  True,  False],
+        #     [False, True,  True,  False],
+        #     [False, False, False, False],
+        # ])
+        ```
+    """
+    if isinstance(rle, list):
+        rle = np.array(rle, dtype=int)
+
+    width, height = resolution_wh
+
+    assert width * height == np.sum(rle), (
+        "the sum of the number of pixels in the RLE must be the same "
+        "as the number of pixels in the expected mask"
+    )
+
+    zero_one_values = np.zeros(shape=(rle.size, 1), dtype=np.uint8)
+    zero_one_values[1::2] = 1
+
+    decoded_rle = np.repeat(zero_one_values, rle, axis=0)
+    decoded_rle = np.append(
+        decoded_rle, np.zeros(width * height - len(decoded_rle), dtype=np.uint8)
+    )
+    return decoded_rle.reshape((height, width), order="F")
+
+
+def mask_to_rle(mask: npt.NDArray[np.bool_]) -> List[int]:
+    """
+    Converts a binary mask into a run-length encoding (RLE).
+
+    Args:
+        mask (npt.NDArray[np.bool_]): 2D binary mask where `True` indicates foreground
+            object and `False` indicates background.
+
+    Returns:
+        The run-length encoded mask. Values of a list with even indices
+            represent the number of pixels assigned as background (`False`), values
+            of a list with odd indices represent the number of pixels assigned
+            as foreground object (`True`).
+
+    Raises:
+        AssertionError: If input mask is not 2D or is empty.
+
+    Examples:
+        ```python
+        import numpy as np
+        import supervision as sv
+
+        mask = np.array([
+            [True, True, True, True],
+            [True, True, True, True],
+            [True, True, True, True],
+            [True, True, True, True],
+        ])
+        sv.mask_to_rle(mask)
+        # [0, 16]
+
+        mask = np.array([
+            [False, False, False, False],
+            [False, True,  True,  False],
+            [False, True,  True,  False],
+            [False, False, False, False],
+        ])
+        sv.mask_to_rle(mask)
+        # [5, 2, 2, 2, 5]
+        ```
+
+    ![mask_to_rle](https://media.roboflow.com/supervision-docs/mask-to-rle.png){ align=center width="800" }
+    """  # noqa E501 // docs
+    assert mask.ndim == 2, "Input mask must be 2D"
+    assert mask.size != 0, "Input mask cannot be empty"
+
+    on_value_change_indices = np.where(
+        mask.ravel(order="F") != np.roll(mask.ravel(order="F"), 1)
+    )[0]
+
+    on_value_change_indices = np.append(on_value_change_indices, mask.size)
+    # need to add 0 at the beginning when the same value is in the first and
+    # last element of the flattened mask
+    if on_value_change_indices[0] != 0:
+        on_value_change_indices = np.insert(on_value_change_indices, 0, 0)
+
+    rle = np.diff(on_value_change_indices)
+
+    if mask[0][0] == 1:
+        rle = np.insert(rle, 0, 0)
+
+    return list(rle)
--- a/supervision/detection/core.py
+++ b/supervision/detection/core.py
@ -7,6 +7,7 @@ from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
 import numpy as np

 from supervision.config import CLASS_NAME_DATA_FIELD, ORIENTED_BOX_COORDINATES
+from supervision.detection.lmm import LMM, from_paligemma, validate_lmm_and_kwargs
 from supervision.detection.utils import (
    box_non_max_suppression,
    calculate_masks_centroids,
@ -240,7 +241,7 @@ class Detections:
            Class names values can be accessed using `detections["class_name"]`.
        """  # noqa: E501 // docs

-        if "obb" in ultralytics_results and ultralytics_results.obb is not None:
+        if hasattr(ultralytics_results, "obb") and ultralytics_results.obb is not None:
            class_id = ultralytics_results.obb.cls.cpu().numpy().astype(int)
            class_names = np.array([ultralytics_results.names[i] for i in class_id])
            oriented_box_coordinates = ultralytics_results.obb.xyxyxyxy.cpu().numpy()
@ -418,6 +419,9 @@ class Detections:
            xyxy=mmdet_results.pred_instances.bboxes.cpu().numpy(),
            confidence=mmdet_results.pred_instances.scores.cpu().numpy(),
            class_id=mmdet_results.pred_instances.labels.cpu().numpy().astype(int),
+            mask=mmdet_results.pred_instances.masks.cpu().numpy()
+            if "masks" in mmdet_results.pred_instances
+            else None,
        )

    @classmethod
@ -802,6 +806,52 @@ class Detections:
            class_id=paddledet_result["bbox"][:, 0].astype(int),
        )

+    @classmethod
+    def from_lmm(cls, lmm: Union[LMM, str], result: str, **kwargs) -> Detections:
+        """
+        Creates a Detections object from the given result string based on the specified
+        Large Multimodal Model (LMM).
+
+        Args:
+            lmm (Union[LMM, str]): The type of LMM (Large Multimodal Model) to use.
+            result (str): The result string containing the detection data.
+            **kwargs: Additional keyword arguments required by the specified LMM.
+
+        Returns:
+            Detections: A new Detections object.
+
+        Raises:
+            ValueError: If the LMM is invalid, required arguments are missing, or
+                disallowed arguments are provided.
+            ValueError: If the specified LMM is not supported.
+
+        Examples:
+            ```python
+            import supervision as sv
+
+            paligemma_result = "<loc0256><loc0256><loc0768><loc0768> cat"
+            detections = sv.Detections.from_lmm(
+                sv.LMM.PALIGEMMA,
+                paligemma_result,
+                resolution_wh=(1000, 1000),
+                classes=['cat', 'dog']
+            )
+            detections.xyxy
+            # array([[250., 250., 750., 750.]])
+
+            detections.class_id
+            # array([0])
+            ```
+        """
+        lmm = validate_lmm_and_kwargs(lmm, kwargs)
+
+        if lmm == LMM.PALIGEMMA:
+            xyxy, class_id, class_name = from_paligemma(result, **kwargs)
+            data = {CLASS_NAME_DATA_FIELD: class_name}
+            return cls(xyxy=xyxy, class_id=class_id, data=data)
+
+        raise ValueError(f"Unsupported LMM: {lmm}")
+
    @classmethod
    def empty(cls) -> Detections:
        """
@ -831,9 +881,10 @@ class Detections:

        This method takes a list of Detections objects and combines their
        respective fields (`xyxy`, `mask`, `confidence`, `class_id`, and `tracker_id`)
-        into a single Detections object. If all elements in a field are not
-        `None`, the corresponding field will be stacked.
-        Otherwise, the field will be set to `None`.
+        into a single Detections object.
+
+        For example, if merging Detections with 3 and 4 detected objects, this method
+        will return a Detections with 7 objects (7 entries in `xyxy`, `mask`, etc).

        Args:
            detections_list (List[Detections]): A list of Detections objects to merge.
@ -891,13 +942,12 @@ class Detections:
        def stack_or_none(name: str):
            if all(d.__getattribute__(name) is None for d in detections_list):
                return None
-            if any(d.__getattribute__(name) is None for d in detections_list):
-                raise ValueError(f"All or none of the '{name}' fields must be None")
-            return (
-                np.vstack([d.__getattribute__(name) for d in detections_list])
-                if name == "mask"
-                else np.hstack([d.__getattribute__(name) for d in detections_list])
-            )
+            stack_list = [
+                d.__getattribute__(name)
+                for d in detections_list
+                if d.__getattribute__(name) is not None
+            ]
+            return np.vstack(stack_list) if name == "mask" else np.hstack(stack_list)

        mask = stack_or_none("mask")
        confidence = stack_or_none("confidence")
--- a/supervision/detection/line_zone.py
+++ b/supervision/detection/line_zone.py
@ -1,3 +1,4 @@
+import warnings
 from typing import Dict, Iterable, Optional, Tuple

 import cv2
@ -7,6 +8,7 @@ from supervision.detection.core import Detections
 from supervision.draw.color import Color
 from supervision.draw.utils import draw_text
 from supervision.geometry.core import Point, Position, Vector
+from supervision.utils.internal import SupervisionWarnings


 class LineZone:
@ -142,6 +144,15 @@ class LineZone:
        if len(detections) == 0:
            return crossed_in, crossed_out

+        if detections.tracker_id is None:
+            warnings.warn(
+                "Line zone counting skipped. LineZone requires tracker_id. Refer to "
+                "https://supervision.roboflow.com/latest/trackers for more "
+                "information.",
+                category=SupervisionWarnings,
+            )
+            return crossed_in, crossed_out
+
        all_anchors = np.array(
            [
                detections.get_anchors_coordinates(anchor)
@ -150,9 +161,6 @@ class LineZone:
        )

        for i, tracker_id in enumerate(detections.tracker_id):
-            if tracker_id is None:
-                continue
-
            box_anchors = [Point(x=x, y=y) for x, y in all_anchors[:, i, :]]

            in_limits = all(
--- a/supervision/detection/lmm.py
+++ b/supervision/detection/lmm.py
@ -0,0 +1,59 @@
+import re
+from enum import Enum
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+
+
+class LMM(Enum):
+    PALIGEMMA = "paligemma"
+
+
+REQUIRED_ARGUMENTS: Dict[LMM, List[str]] = {LMM.PALIGEMMA: ["resolution_wh"]}
+
+ALLOWED_ARGUMENTS: Dict[LMM, List[str]] = {LMM.PALIGEMMA: ["resolution_wh", "classes"]}
+
+
+def validate_lmm_and_kwargs(lmm: Union[LMM, str], kwargs: Dict[str, Any]) -> LMM:
+    if isinstance(lmm, str):
+        try:
+            lmm = LMM(lmm.lower())
+        except ValueError:
+            raise ValueError(
+                f"Invalid lmm value: {lmm}. Must be one of {[e.value for e in LMM]}"
+            )
+
+    required_args = REQUIRED_ARGUMENTS.get(lmm, [])
+    for arg in required_args:
+        if arg not in kwargs:
+            raise ValueError(f"Missing required argument: {arg}")
+
+    allowed_args = ALLOWED_ARGUMENTS.get(lmm, [])
+    for arg in kwargs:
+        if arg not in allowed_args:
+            raise ValueError(f"Argument {arg} is not allowed for {lmm.name}")
+
+    return lmm
+
+
+def from_paligemma(
+    result: str, resolution_wh: Tuple[int, int], classes: Optional[List[str]] = None
+) -> Tuple[np.ndarray, Optional[np.ndarray], np.ndarray]:
+    w, h = resolution_wh
+    pattern = re.compile(
+        r"(?<!<loc\d{4}>)<loc(\d{4})><loc(\d{4})><loc(\d{4})><loc(\d{4})> ([\w\s]+)"
+    )
+    matches = pattern.findall(result)
+    matches = np.array(matches) if matches else np.empty((0, 5))
+
+    xyxy, class_name = matches[:, [1, 0, 3, 2]], matches[:, 4]
+    xyxy = xyxy.astype(int) / 1024 * np.array([w, h, w, h])
+    class_name = np.char.strip(class_name.astype(str))
+    class_id = None
+
+    if classes is not None:
+        mask = np.array([name in classes for name in class_name]).astype(bool)
+        xyxy, class_name = xyxy[mask], class_name[mask]
+        class_id = np.array([classes.index(name) for name in class_name])
+
+    return xyxy, class_id, class_name
--- a/supervision/detection/tools/inference_slicer.py
+++ b/supervision/detection/tools/inference_slicer.py
@ -4,20 +4,36 @@ from typing import Callable, Optional, Tuple
 import numpy as np

 from supervision.detection.core import Detections
-from supervision.detection.utils import move_boxes
+from supervision.detection.utils import move_boxes, move_masks
 from supervision.utils.image import crop_image


-def move_detections(detections: Detections, offset: np.array) -> Detections:
+def move_detections(
+    detections: Detections,
+    offset: np.ndarray,
+    resolution_wh: Optional[Tuple[int, int]] = None,
+) -> Detections:
    """
    Args:
        detections (sv.Detections): Detections object to be moved.
-        offset (np.array): An array of shape `(2,)` containing offset values in format
+        offset (np.ndarray): An array of shape `(2,)` containing offset values in format
            is `[dx, dy]`.
+        resolution_wh (Tuple[int, int]): The width and height of the desired mask
+            resolution. Required for segmentation detections.
+
    Returns:
        (sv.Detections) repositioned Detections object.
    """
    detections.xyxy = move_boxes(xyxy=detections.xyxy, offset=offset)
+    if detections.mask is not None:
+        if resolution_wh is None:
+            raise ValueError(
+                "Resolution width and height are required for moving segmentation "
+                "detections. This should be the same as (width, height) of image shape."
+            )
+        detections.mask = move_masks(
+            masks=detections.mask, offset=offset, resolution_wh=resolution_wh
+        )
    return detections


@ -126,7 +142,10 @@ class InferenceSlicer:
        """
        image_slice = crop_image(image=image, xyxy=offset)
        detections = self.callback(image_slice)
-        detections = move_detections(detections=detections, offset=offset[:2])
+        resolution_wh = (image.shape[1], image.shape[0])
+        detections = move_detections(
+            detections=detections, offset=offset[:2], resolution_wh=resolution_wh
+        )

        return detections

--- a/supervision/detection/tools/smoother.py
+++ b/supervision/detection/tools/smoother.py
@ -1,3 +1,4 @@
+import warnings
 from collections import defaultdict, deque
 from copy import deepcopy
 from typing import Optional
@ -5,6 +6,7 @@ from typing import Optional
 import numpy as np

 from supervision.detection.core import Detections
+from supervision.utils.internal import SupervisionWarnings


 class DetectionsSmoother:
@ -70,16 +72,16 @@ class DetectionsSmoother:
        """

        if detections.tracker_id is None:
-            print(
+            warnings.warn(
                "Smoothing skipped. DetectionsSmoother requires tracker_id. Refer to "
-                "https://supervision.roboflow.com/latest/trackers for more information."
+                "https://supervision.roboflow.com/latest/trackers for more "
+                "information.",
+                category=SupervisionWarnings,
            )
            return detections

        for detection_idx in range(len(detections)):
            tracker_id = detections.tracker_id[detection_idx]
-            if tracker_id is None:
-                continue

            self.tracks[tracker_id].append(detections[detection_idx])

--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@ -3,6 +3,7 @@ from typing import Dict, List, Optional, Tuple, Union

 import cv2
 import numpy as np
+import numpy.typing as npt

 from supervision.config import CLASS_NAME_DATA_FIELD

@ -56,7 +57,9 @@ def box_iou_batch(boxes_true: np.ndarray, boxes_detection: np.ndarray) -> np.nda
    bottom_right = np.minimum(boxes_true[:, None, 2:], boxes_detection[:, 2:])

    area_inter = np.prod(np.clip(bottom_right - top_left, a_min=0, a_max=None), 2)
-    return area_inter / (area_true[:, None] + area_detection - area_inter)
+    ious = area_inter / (area_true[:, None] + area_detection - area_inter)
+    ious = np.nan_to_num(ious)
+    return ious


 def _mask_iou_batch_split(
@ -297,6 +300,35 @@ def clip_boxes(xyxy: np.ndarray, resolution_wh: Tuple[int, int]) -> np.ndarray:
    return result


+def pad_boxes(xyxy: np.ndarray, px: int, py: Optional[int] = None) -> np.ndarray:
+    """
+    Pads bounding boxes coordinates with a constant padding.
+
+    Args:
+        xyxy (np.ndarray): A numpy array of shape `(N, 4)` where each
+            row corresponds to a bounding box in the format
+            `(x_min, y_min, x_max, y_max)`.
+        px (int): The padding value to be added to both the left and right sides of
+            each bounding box.
+        py (Optional[int]): The padding value to be added to both the top and bottom
+            sides of each bounding box. If not provided, `px` will be used for both
+            dimensions.
+
+    Returns:
+        np.ndarray: A numpy array of shape `(N, 4)` where each row corresponds to a
+            bounding box with coordinates padded according to the provided padding
+            values.
+    """
+    if py is None:
+        py = px
+
+    result = xyxy.copy()
+    result[:, [0, 1]] -= [px, py]
+    result[:, [2, 3]] += [px, py]
+
+    return result
+
+
 def xywh_to_xyxy(boxes_xywh: np.ndarray) -> np.ndarray:
    xyxy = boxes_xywh.copy()
    xyxy[:, 2] = boxes_xywh[:, 0] + boxes_xywh[:, 2]
@ -500,7 +532,7 @@ def process_roboflow_result(
    np.ndarray,
    Optional[np.ndarray],
    Optional[np.ndarray],
-    Dict[str, List[np.ndarray]],
+    Dict[str, Union[List[np.ndarray], np.ndarray]],
 ]:
    if not roboflow_result["predictions"]:
        return (
@ -574,24 +606,61 @@ def move_boxes(xyxy: np.ndarray, offset: np.ndarray) -> np.ndarray:
    Returns:
        np.ndarray: Repositioned bounding boxes.

-    Example:
+    Examples:
        ```python
        import numpy as np
        import supervision as sv

-        boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
+        xyxy = np.array([
+            [10, 10, 20, 20],
+            [30, 30, 40, 40]
+        ])
        offset = np.array([5, 5])
-        moved_box = sv.move_boxes(boxes, offset)
-        print(moved_box)
-        # np.array([
+
+        sv.move_boxes(xyxy=xyxy, offset=offset)
+        # array([
        #    [15, 15, 25, 25],
-        #     [35, 35, 45, 45]
+        #    [35, 35, 45, 45]
        # ])
        ```
    """
    return xyxy + np.hstack([offset, offset])


+def move_masks(
+    masks: np.ndarray,
+    offset: np.ndarray,
+    resolution_wh: Tuple[int, int] = None,
+) -> np.ndarray:
+    """
+    Offset the masks in an array by the specified (x, y) amount.
+
+    Args:
+        masks (np.ndarray): A 3D array of binary masks corresponding to the predictions.
+            Shape: `(N, H, W)`, where N is the number of predictions, and H, W are the
+            dimensions of each mask.
+        offset (np.ndarray): An array of shape `(2,)` containing non-negative int values
+            `[dx, dy]`.
+        resolution_wh (Tuple[int, int]): The width and height of the desired mask
+            resolution.
+
+    Returns:
+        (np.ndarray) repositioned masks, optionally padded to the specified shape.
+    """
+
+    if offset[0] < 0 or offset[1] < 0:
+        raise ValueError(f"Offset values must be non-negative integers. Got: {offset}")
+
+    mask_array = np.full((masks.shape[0], resolution_wh[1], resolution_wh[0]), False)
+    mask_array[
+        :,
+        offset[1] : masks.shape[1] + offset[1],
+        offset[0] : masks.shape[2] + offset[0],
+    ] = masks
+
+    return mask_array
+
+
 def scale_boxes(xyxy: np.ndarray, factor: float) -> np.ndarray:
    """
    Scale the dimensions of bounding boxes.
@ -606,16 +675,18 @@ def scale_boxes(xyxy: np.ndarray, factor: float) -> np.ndarray:
    Returns:
        np.ndarray: Scaled bounding boxes.

-    Example:
+    Examples:
        ```python
        import numpy as np
        import supervision as sv

-        boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
-        factor = 1.5
-        scaled_bb = sv.scale_boxes(boxes, factor)
-        print(scaled_bb)
-        # np.array([
+        xyxy = np.array([
+            [10, 10, 20, 20],
+            [30, 30, 40, 40]
+        ])
+
+        scaled_bb = sv.scale_boxes(xyxy=xyxy, factor=1.5)
+        # array([
        #    [ 7.5,  7.5, 22.5, 22.5],
        #    [27.5, 27.5, 42.5, 42.5]
        # ])
@ -678,7 +749,9 @@ def merge_data(
    Merges the data payloads of a list of Detections instances.

    Args:
-        data_list: The data payloads of the instances.
+        data_list: The data payloads of the Detections instances. Each data payload
+            is a dictionary with the same keys, and the values are either lists or
+            np.ndarray.

    Returns:
        A single data payload containing the merged data, preserving the original data
@ -691,10 +764,6 @@ def merge_data(
    if not data_list:
        return {}

-    all_keys_sets = [set(data.keys()) for data in data_list]
-    if not all(keys_set == all_keys_sets[0] for keys_set in all_keys_sets):
-        raise ValueError("All data dictionaries must have the same keys to merge.")
-
    for data in data_list:
        lengths = [len(value) for value in data.values()]
        if len(set(lengths)) > 1:
@ -702,10 +771,23 @@ def merge_data(
                "All data values within a single object must have equal length."
            )

-    merged_data = {key: [] for key in all_keys_sets[0]}
+    keys_by_data = [set(data.keys()) for data in data_list]
+    keys_by_data = [keys for keys in keys_by_data if len(keys) > 0]
+    if not keys_by_data:
+        return {}

+    common_keys = set.intersection(*keys_by_data)
+    all_keys = set.union(*keys_by_data)
+    if common_keys != all_keys:
+        raise ValueError(
+            f"All sv.Detections.data dictionaries must have the same keys. Common "
+            f"keys: {common_keys}, but some dictionaries have additional keys: "
+            f"{all_keys.difference(common_keys)}."
+        )
+
+    merged_data = {key: [] for key in all_keys}
    for data in data_list:
-        for key in merged_data:
+        for key in data:
            merged_data[key].append(data[key])

    for key in merged_data:
@ -766,3 +848,121 @@ def get_data_item(
            raise TypeError(f"Unsupported data type for key '{key}': {type(value)}")

    return subset_data
+
+
+def contains_holes(mask: npt.NDArray[np.bool_]) -> bool:
+    """
+    Checks if the binary mask contains holes (background pixels fully enclosed by
+    foreground pixels).
+
+    Args:
+        mask (npt.NDArray[np.bool_]): 2D binary mask where `True` indicates foreground
+            object and `False` indicates background.
+
+    Returns:
+        True if holes are detected, False otherwise.
+
+    Examples:
+        ```python
+        import numpy as np
+        import supervision as sv
+
+        mask = np.array([
+            [0, 0, 0, 0, 0],
+            [0, 1, 1, 1, 0],
+            [0, 1, 0, 1, 0],
+            [0, 1, 1, 1, 0],
+            [0, 0, 0, 0, 0]
+        ]).astype(bool)
+
+        sv.contains_holes(mask=mask)
+        # True
+
+        mask = np.array([
+            [0, 0, 0, 0, 0],
+            [0, 1, 1, 1, 0],
+            [0, 1, 1, 1, 0],
+            [0, 1, 1, 1, 0],
+            [0, 0, 0, 0, 0]
+        ]).astype(bool)
+
+        sv.contains_holes(mask=mask)
+        # False
+        ```
+
+    ![contains_holes](https://media.roboflow.com/supervision-docs/contains-holes.png){ align=center width="800" }
+    """  # noqa E501 // docs
+    mask_uint8 = mask.astype(np.uint8)
+    _, hierarchy = cv2.findContours(mask_uint8, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
+
+    if hierarchy is not None:
+        parent_contour_index = 3
+        for h in hierarchy[0]:
+            if h[parent_contour_index] != -1:
+                return True
+    return False
+
+
+def contains_multiple_segments(
+    mask: npt.NDArray[np.bool_], connectivity: int = 4
+) -> bool:
+    """
+    Checks if the binary mask contains multiple unconnected foreground segments.
+
+    Args:
+        mask (npt.NDArray[np.bool_]): 2D binary mask where `True` indicates foreground
+            object and `False` indicates background.
+        connectivity (int) : Default: 4 is 4-way connectivity, which means that
+            foreground pixels are the part of the same segment/component
+            if their edges touch.
+            Alternatively: 8 for 8-way connectivity, when foreground pixels are
+            connected by their edges or corners touch.
+
+    Returns:
+        True when the mask contains multiple not connected components, False otherwise.
+
+    Raises:
+        ValueError: If connectivity(int) parameter value is not 4 or 8.
+
+    Examples:
+        ```python
+        import numpy as np
+        import supervision as sv
+
+        mask = np.array([
+            [0, 0, 0, 0, 0, 0],
+            [0, 1, 1, 0, 1, 1],
+            [0, 1, 1, 0, 1, 1],
+            [0, 0, 0, 0, 0, 0],
+            [0, 1, 1, 1, 0, 0],
+            [0, 1, 1, 1, 0, 0]
+        ]).astype(bool)
+
+        sv.contains_multiple_segments(mask=mask, connectivity=4)
+        # True
+
+        mask = np.array([
+            [0, 0, 0, 0, 0, 0],
+            [0, 1, 1, 1, 1, 1],
+            [0, 1, 1, 1, 1, 1],
+            [0, 1, 1, 1, 1, 1],
+            [0, 1, 1, 1, 1, 1],
+            [0, 0, 0, 0, 0, 0]
+        ]).astype(bool)
+
+        sv.contains_multiple_segments(mask=mask, connectivity=4)
+        # False
+        ```
+
+    ![contains_multiple_segments](https://media.roboflow.com/supervision-docs/contains-multiple-segments.png){ align=center width="800" }
+    """  # noqa E501 // docs
+    if connectivity != 4 and connectivity != 8:
+        raise ValueError(
+            "Incorrect connectivity value. Possible connectivity values: 4 or 8."
+        )
+    mask_uint8 = mask.astype(np.uint8)
+    labels = np.zeros_like(mask_uint8, dtype=np.int32)
+    number_of_labels, _ = cv2.connectedComponents(
+        mask_uint8, labels, connectivity=connectivity
+    )
+    return number_of_labels > 2
--- a/supervision/draw/utils.py
+++ b/supervision/draw/utils.py
@ -81,6 +81,58 @@ def draw_filled_rectangle(scene: np.ndarray, rect: Rect, color: Color) -> np.nda
    return scene


+def draw_rounded_rectangle(
+    scene: np.ndarray,
+    rect: Rect,
+    color: Color,
+    border_radius: int,
+) -> np.ndarray:
+    """
+    Draws a rounded rectangle on an image.
+
+    Parameters:
+        scene (np.ndarray): The image on which the rounded rectangle will be drawn.
+        rect (Rect): The rectangle to be drawn.
+        color (Color): The color of the rounded rectangle.
+        border_radius (int): The radius of the corner rounding.
+
+    Returns:
+        np.ndarray: The image with the rounded rectangle drawn on it.
+    """
+    x1, y1, x2, y2 = rect.as_xyxy_int_tuple()
+    width, height = x2 - x1, y2 - y1
+    border_radius = min(border_radius, min(width, height) // 2)
+
+    rectangle_coordinates = [
+        ((x1 + border_radius, y1), (x2 - border_radius, y2)),
+        ((x1, y1 + border_radius), (x2, y2 - border_radius)),
+    ]
+    circle_centers = [
+        (x1 + border_radius, y1 + border_radius),
+        (x2 - border_radius, y1 + border_radius),
+        (x1 + border_radius, y2 - border_radius),
+        (x2 - border_radius, y2 - border_radius),
+    ]
+
+    for coordinates in rectangle_coordinates:
+        cv2.rectangle(
+            img=scene,
+            pt1=coordinates[0],
+            pt2=coordinates[1],
+            color=color.as_bgr(),
+            thickness=-1,
+        )
+    for center in circle_centers:
+        cv2.circle(
+            img=scene,
+            center=center,
+            radius=border_radius,
+            color=color.as_bgr(),
+            thickness=-1,
+        )
+    return scene
+
+
 def draw_polygon(
    scene: np.ndarray, polygon: np.ndarray, color: Color, thickness: int = 2
 ) -> np.ndarray:
--- a/supervision/geometry/core.py
+++ b/supervision/geometry/core.py
@ -98,6 +98,11 @@ class Rect:
    width: float
    height: float

+    @classmethod
+    def from_xyxy(cls, xyxy: Tuple[float, float, float, float]) -> Rect:
+        x1, y1, x2, y2 = xyxy
+        return cls(x=x1, y=y1, width=x2 - x1, height=y2 - y1)
+
    @property
    def top_left(self) -> Point:
        return Point(x=self.x, y=self.y)
@ -113,3 +118,11 @@ class Rect:
            width=self.width + 2 * padding,
            height=self.height + 2 * padding,
        )
+
+    def as_xyxy_int_tuple(self) -> Tuple[int, int, int, int]:
+        return (
+            int(self.x),
+            int(self.y),
+            int(self.x + self.width),
+            int(self.y + self.height),
+        )
--- a/supervision/keypoint/annotators.py
+++ b/supervision/keypoint/annotators.py
@ -1,12 +1,14 @@
 from abc import ABC, abstractmethod
 from logging import warn
-from typing import List, Optional, Tuple
+from typing import List, Optional, Tuple, Union

 import cv2
 import numpy as np

+from supervision import Rect, pad_boxes
 from supervision.annotators.base import ImageType
 from supervision.draw.color import Color
+from supervision.draw.utils import draw_rounded_rectangle
 from supervision.keypoint.core import KeyPoints
 from supervision.keypoint.skeletons import SKELETONS_BY_VERTEX_COUNT
 from supervision.utils.conversion import convert_for_annotation_method
@ -46,8 +48,8 @@ class VertexAnnotator(BaseKeyPointAnnotator):
        points. It draws circles at each key point location.

        Args:
-            scene (ImageType): The image where bounding boxes will be drawn. `ImageType`
-                is a flexible type, accepting either `numpy.ndarray` or
+            scene (ImageType): The image where skeleton vertices will be drawn.
+                `ImageType` is a flexible type, accepting either `numpy.ndarray` or
                `PIL.Image.Image`.
            key_points (KeyPoints): A collection of key points where each key point
                consists of x and y coordinates.
@ -63,7 +65,10 @@ class VertexAnnotator(BaseKeyPointAnnotator):
            image = ...
            key_points = sv.KeyPoints(...)

-            vertex_annotator = sv.VertexAnnotator(color=sv.Color.GREEN, radius=10)
+            vertex_annotator = sv.VertexAnnotator(
+                color=sv.Color.GREEN,
+                radius=10
+            )
            annotated_frame = vertex_annotator.annotate(
                scene=image.copy(),
                key_points=key_points
@ -119,7 +124,7 @@ class EdgeAnnotator(BaseKeyPointAnnotator):
        edges.

        Args:
-            scene (ImageType): The image where bounding boxes will be drawn. `ImageType`
+            scene (ImageType): The image where skeleton edges will be drawn. `ImageType`
                is a flexible type, accepting either `numpy.ndarray` or
                `PIL.Image.Image`.
            key_points (KeyPoints): A collection of key points where each key point
@ -137,7 +142,10 @@ class EdgeAnnotator(BaseKeyPointAnnotator):
            image = ...
            key_points = sv.KeyPoints(...)

-            edge_annotator = sv.EdgeAnnotator(color=sv.Color.GREEN, thickness=5)
+            edge_annotator = sv.EdgeAnnotator(
+                color=sv.Color.GREEN,
+                thickness=5
+            )
            annotated_frame = edge_annotator.annotate(
                scene=image.copy(),
                key_points=key_points
@ -175,3 +183,236 @@ class EdgeAnnotator(BaseKeyPointAnnotator):
                )

        return scene
+
+
+class VertexLabelAnnotator:
+    """
+    A class that draws labels of skeleton vertices on images. It uses specified key
+    points to determine the locations where the vertices should be drawn.
+    """
+
+    def __init__(
+        self,
+        color: Union[Color, List[Color]] = Color.ROBOFLOW,
+        text_color: Color = Color.WHITE,
+        text_scale: float = 0.5,
+        text_thickness: int = 1,
+        text_padding: int = 10,
+        border_radius: int = 0,
+    ):
+        """
+        Args:
+            color (Union[Color, List[Color]], optional): The color to use for each
+                keypoint label. If a list is provided, the colors will be used in order
+                for each keypoint.
+            text_color (Color, optional): The color to use for the labels.
+            text_scale (float, optional): The scale of the text.
+            text_thickness (int, optional): The thickness of the text.
+            text_padding (int, optional): The padding around the text.
+            border_radius (int, optional): The radius of the rounded corners of the
+                boxes. Set to a high value to produce circles.
+        """
+        self.border_radius: int = border_radius
+        self.color: Union[Color, List[Color]] = color
+        self.text_color: Color = text_color
+        self.text_scale: float = text_scale
+        self.text_thickness: int = text_thickness
+        self.text_padding: int = text_padding
+
+    def annotate(
+        self, scene: ImageType, key_points: KeyPoints, labels: List[str] = None
+    ) -> ImageType:
+        """
+        A class that draws labels of skeleton vertices on images. It uses specified key
+            points to determine the locations where the vertices should be drawn.
+
+        Args:
+            scene (ImageType): The image where vertex labels will be drawn. `ImageType`
+                is a flexible type, accepting either `numpy.ndarray` or
+                `PIL.Image.Image`.
+            key_points (KeyPoints): A collection of key points where each key point
+                consists of x and y coordinates.
+            labels (List[str], optional): A list of labels to be displayed on the
+                annotated image. If not provided, keypoint indices will be used.
+
+        Returns:
+            The annotated image, matching the type of `scene` (`numpy.ndarray`
+                or `PIL.Image.Image`)
+
+        Example:
+            ```python
+            import supervision as sv
+
+            image = ...
+            key_points = sv.KeyPoints(...)
+
+            vertex_label_annotator = sv.VertexLabelAnnotator(
+                color=sv.Color.GREEN,
+                text_color=sv.Color.BLACK,
+                border_radius=5
+            )
+            annotated_frame = vertex_label_annotator.annotate(
+                scene=image.copy(),
+                key_points=key_points
+            )
+            ```
+
+        ![vertex-label-annotator-example](https://media.roboflow.com/
+        supervision-annotator-examples/vertex-label-annotator-example.png)
+
+        !!! tip
+
+            `VertexLabelAnnotator` allows to customize the color of each keypoint label
+            values.
+
+        Example:
+            ```python
+            import supervision as sv
+
+            image = ...
+            key_points = sv.KeyPoints(...)
+
+            LABELS = [
+                "nose", "left eye", "right eye", "left ear",
+                "right ear", "left shoulder", "right shoulder", "left elbow",
+                "right elbow", "left wrist", "right wrist", "left hip",
+                "right hip", "left knee", "right knee", "left ankle",
+                "right ankle"
+            ]
+
+            COLORS = [
+                "#FF6347", "#FF6347", "#FF6347", "#FF6347",
+                "#FF6347", "#FF1493", "#00FF00", "#FF1493",
+                "#00FF00", "#FF1493", "#00FF00", "#FFD700",
+                "#00BFFF", "#FFD700", "#00BFFF", "#FFD700",
+                "#00BFFF"
+            ]
+            COLORS = [sv.Color.from_hex(color_hex=c) for c in COLORS]
+
+            vertex_label_annotator = sv.VertexLabelAnnotator(
+                color=COLORS,
+                text_color=sv.Color.BLACK,
+                border_radius=5
+            )
+            annotated_frame = vertex_label_annotator.annotate(
+                scene=image.copy(),
+                key_points=key_points,
+                labels=labels
+            )
+            ```
+        ![vertex-label-annotator-custom-example](https://media.roboflow.com/
+        supervision-annotator-examples/vertex-label-annotator-custom-example.png)
+        """
+        font = cv2.FONT_HERSHEY_SIMPLEX
+
+        skeletons_count, points_count, _ = key_points.xy.shape
+        if skeletons_count == 0:
+            return scene
+
+        anchors = key_points.xy.reshape(points_count * skeletons_count, 2).astype(int)
+        mask = np.all(anchors != 0, axis=1)
+
+        if not np.any(mask):
+            return scene
+
+        colors = self.preprocess_and_validate_colors(
+            colors=self.color,
+            points_count=points_count,
+            skeletons_count=skeletons_count,
+        )
+
+        labels = self.preprocess_and_validate_labels(
+            labels=labels, points_count=points_count, skeletons_count=skeletons_count
+        )
+
+        anchors = anchors[mask]
+        colors = colors[mask]
+        labels = labels[mask]
+
+        xyxy = np.array(
+            [
+                self.get_text_bounding_box(
+                    text=label,
+                    font=font,
+                    text_scale=self.text_scale,
+                    text_thickness=self.text_thickness,
+                    center_coordinates=tuple(anchor),
+                )
+                for anchor, label in zip(anchors, labels)
+            ]
+        )
+
+        xyxy_padded = pad_boxes(xyxy=xyxy, px=self.text_padding)
+
+        for text, color, box, box_padded in zip(labels, colors, xyxy, xyxy_padded):
+            draw_rounded_rectangle(
+                scene=scene,
+                rect=Rect.from_xyxy(box_padded),
+                color=color,
+                border_radius=self.border_radius,
+            )
+            cv2.putText(
+                img=scene,
+                text=text,
+                org=(box[0], box[1] + self.text_padding),
+                fontFace=font,
+                fontScale=self.text_scale,
+                color=self.text_color.as_rgb(),
+                thickness=self.text_thickness,
+                lineType=cv2.LINE_AA,
+            )
+
+        return scene
+
+    @staticmethod
+    def get_text_bounding_box(
+        text: str,
+        font: int,
+        text_scale: float,
+        text_thickness: int,
+        center_coordinates: Tuple[int, int],
+    ) -> Tuple[int, int, int, int]:
+        text_w, text_h = cv2.getTextSize(
+            text=text,
+            fontFace=font,
+            fontScale=text_scale,
+            thickness=text_thickness,
+        )[0]
+        center_x, center_y = center_coordinates
+        return (
+            center_x - text_w // 2,
+            center_y - text_h // 2,
+            center_x + text_w // 2,
+            center_y + text_h // 2,
+        )
+
+    @staticmethod
+    def preprocess_and_validate_labels(
+        labels: Optional[List[str]], points_count: int, skeletons_count: int
+    ) -> np.array:
+        if labels and len(labels) != points_count:
+            raise ValueError(
+                f"Number of labels ({len(labels)}) must match number of key points "
+                f"({points_count})."
+            )
+        if labels is None:
+            labels = [str(i) for i in range(points_count)]
+
+        return np.array(labels * skeletons_count)
+
+    @staticmethod
+    def preprocess_and_validate_colors(
+        colors: Optional[Union[Color, List[Color]]],
+        points_count: int,
+        skeletons_count: int,
+    ) -> np.array:
+        if isinstance(colors, list) and len(colors) != points_count:
+            raise ValueError(
+                f"Number of colors ({len(colors)}) must match number of key points "
+                f"({points_count})."
+            )
+        return (
+            np.array(colors * skeletons_count)
+            if isinstance(colors, list)
+            else np.array([colors] * points_count * skeletons_count)
+        )
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@ -487,7 +487,7 @@ class ByteTrack:
        self.lost_tracks = sub_tracks(self.lost_tracks, self.tracked_tracks)
        self.lost_tracks.extend(lost_stracks)
        self.lost_tracks = sub_tracks(self.lost_tracks, self.removed_tracks)
-        self.removed_tracks.extend(removed_stracks)
+        self.removed_tracks = removed_stracks
        self.tracked_tracks, self.lost_tracks = remove_duplicate_tracks(
            self.tracked_tracks, self.lost_tracks
        )
--- a/test/dataset/formats/test_coco.py
+++ b/test/dataset/formats/test_coco.py
@ -1,5 +1,5 @@
 from contextlib import ExitStack as DoesNotRaise
-from typing import Dict, List, Tuple
+from typing import Dict, List, Tuple, Union

 import numpy as np
 import pytest
@ -10,24 +10,30 @@ from supervision.dataset.formats.coco import (
    classes_to_coco_categories,
    coco_annotations_to_detections,
    coco_categories_to_classes,
+    detections_to_coco_annotations,
    group_coco_annotations_by_image_id,
 )


-def mock_cock_coco_annotation(
+def mock_coco_annotation(
    annotation_id: int = 0,
    image_id: int = 0,
    category_id: int = 0,
    bbox: Tuple[float, float, float, float] = (0.0, 0.0, 0.0, 0.0),
    area: float = 0.0,
+    segmentation: Union[List[list], Dict] = None,
+    iscrowd: bool = False,
 ) -> dict:
+    if not segmentation:
+        segmentation = []
    return {
        "id": annotation_id,
        "image_id": image_id,
        "category_id": category_id,
        "bbox": list(bbox),
        "area": area,
-        "iscrowd": 0,
+        "segmentation": segmentation,
+        "iscrowd": int(iscrowd),
    }


@ -101,74 +107,46 @@ def test_classes_to_coco_categories_and_back_to_classes(
    [
        ([], {}, DoesNotRaise()),  # empty coco annotations
        (
-            [mock_cock_coco_annotation(annotation_id=0, image_id=0, category_id=0)],
-            {
-                0: [
-                    mock_cock_coco_annotation(
-                        annotation_id=0, image_id=0, category_id=0
-                    )
-                ]
-            },
+            [mock_coco_annotation(annotation_id=0, image_id=0, category_id=0)],
+            {0: [mock_coco_annotation(annotation_id=0, image_id=0, category_id=0)]},
            DoesNotRaise(),
        ),  # single coco annotation
        (
            [
-                mock_cock_coco_annotation(annotation_id=0, image_id=0, category_id=0),
-                mock_cock_coco_annotation(annotation_id=1, image_id=1, category_id=0),
+                mock_coco_annotation(annotation_id=0, image_id=0, category_id=0),
+                mock_coco_annotation(annotation_id=1, image_id=1, category_id=0),
            ],
            {
-                0: [
-                    mock_cock_coco_annotation(
-                        annotation_id=0, image_id=0, category_id=0
-                    )
-                ],
-                1: [
-                    mock_cock_coco_annotation(
-                        annotation_id=1, image_id=1, category_id=0
-                    )
-                ],
+                0: [mock_coco_annotation(annotation_id=0, image_id=0, category_id=0)],
+                1: [mock_coco_annotation(annotation_id=1, image_id=1, category_id=0)],
            },
            DoesNotRaise(),
        ),  # two coco annotations
        (
            [
-                mock_cock_coco_annotation(annotation_id=0, image_id=0, category_id=0),
-                mock_cock_coco_annotation(annotation_id=1, image_id=1, category_id=1),
-                mock_cock_coco_annotation(annotation_id=2, image_id=1, category_id=2),
-                mock_cock_coco_annotation(annotation_id=3, image_id=2, category_id=3),
-                mock_cock_coco_annotation(annotation_id=4, image_id=3, category_id=1),
-                mock_cock_coco_annotation(annotation_id=5, image_id=3, category_id=2),
-                mock_cock_coco_annotation(annotation_id=5, image_id=3, category_id=3),
+                mock_coco_annotation(annotation_id=0, image_id=0, category_id=0),
+                mock_coco_annotation(annotation_id=1, image_id=1, category_id=1),
+                mock_coco_annotation(annotation_id=2, image_id=1, category_id=2),
+                mock_coco_annotation(annotation_id=3, image_id=2, category_id=3),
+                mock_coco_annotation(annotation_id=4, image_id=3, category_id=1),
+                mock_coco_annotation(annotation_id=5, image_id=3, category_id=2),
+                mock_coco_annotation(annotation_id=5, image_id=3, category_id=3),
            ],
            {
                0: [
-                    mock_cock_coco_annotation(
-                        annotation_id=0, image_id=0, category_id=0
-                    ),
+                    mock_coco_annotation(annotation_id=0, image_id=0, category_id=0),
                ],
                1: [
-                    mock_cock_coco_annotation(
-                        annotation_id=1, image_id=1, category_id=1
-                    ),
-                    mock_cock_coco_annotation(
-                        annotation_id=2, image_id=1, category_id=2
-                    ),
+                    mock_coco_annotation(annotation_id=1, image_id=1, category_id=1),
+                    mock_coco_annotation(annotation_id=2, image_id=1, category_id=2),
                ],
                2: [
-                    mock_cock_coco_annotation(
-                        annotation_id=3, image_id=2, category_id=3
-                    ),
+                    mock_coco_annotation(annotation_id=3, image_id=2, category_id=3),
                ],
                3: [
-                    mock_cock_coco_annotation(
-                        annotation_id=4, image_id=3, category_id=1
-                    ),
-                    mock_cock_coco_annotation(
-                        annotation_id=5, image_id=3, category_id=2
-                    ),
-                    mock_cock_coco_annotation(
-                        annotation_id=5, image_id=3, category_id=3
-                    ),
+                    mock_coco_annotation(annotation_id=4, image_id=3, category_id=1),
+                    mock_coco_annotation(annotation_id=5, image_id=3, category_id=2),
+                    mock_coco_annotation(annotation_id=5, image_id=3, category_id=3),
                ],
            },
            DoesNotRaise(),
@ -195,7 +173,7 @@ def test_group_coco_annotations_by_image_id(
        ),  # empty image annotations
        (
            [
-                mock_cock_coco_annotation(
+                mock_coco_annotation(
                    category_id=0, bbox=(0, 0, 100, 100), area=100 * 100
                )
            ],
@ -209,10 +187,10 @@ def test_group_coco_annotations_by_image_id(
        ),  # single image annotations
        (
            [
-                mock_cock_coco_annotation(
+                mock_coco_annotation(
                    category_id=0, bbox=(0, 0, 100, 100), area=100 * 100
                ),
-                mock_cock_coco_annotation(
+                mock_coco_annotation(
                    category_id=0, bbox=(100, 100, 100, 100), area=100 * 100
                ),
            ],
@ -226,6 +204,156 @@ def test_group_coco_annotations_by_image_id(
            ),
            DoesNotRaise(),
        ),  # two image annotations
+        (
+            [
+                mock_coco_annotation(
+                    category_id=0,
+                    bbox=(0, 0, 5, 5),
+                    area=5 * 5,
+                    segmentation=[[0, 0, 2, 0, 2, 2, 4, 2, 4, 4, 0, 4]],
+                )
+            ],
+            (5, 5),
+            True,
+            Detections(
+                xyxy=np.array([[0, 0, 5, 5]], dtype=np.float32),
+                class_id=np.array([0], dtype=int),
+                mask=np.array(
+                    [
+                        [
+                            [1, 1, 1, 0, 0],
+                            [1, 1, 1, 0, 0],
+                            [1, 1, 1, 1, 1],
+                            [1, 1, 1, 1, 1],
+                            [1, 1, 1, 1, 1],
+                        ]
+                    ]
+                ),
+            ),
+            DoesNotRaise(),
+        ),  # single image annotations with mask as polygon
+        (
+            [
+                mock_coco_annotation(
+                    category_id=0,
+                    bbox=(0, 0, 5, 5),
+                    area=5 * 5,
+                    segmentation={
+                        "size": [5, 5],
+                        "counts": [0, 15, 2, 3, 2, 3],
+                    },
+                    iscrowd=True,
+                )
+            ],
+            (5, 5),
+            True,
+            Detections(
+                xyxy=np.array([[0, 0, 5, 5]], dtype=np.float32),
+                class_id=np.array([0], dtype=int),
+                mask=np.array(
+                    [
+                        [
+                            [1, 1, 1, 0, 0],
+                            [1, 1, 1, 0, 0],
+                            [1, 1, 1, 1, 1],
+                            [1, 1, 1, 1, 1],
+                            [1, 1, 1, 1, 1],
+                        ]
+                    ]
+                ),
+            ),
+            DoesNotRaise(),
+        ),  # single image annotations with mask, RLE segmentation mask
+        (
+            [
+                mock_coco_annotation(
+                    category_id=0,
+                    bbox=(0, 0, 5, 5),
+                    area=5 * 5,
+                    segmentation=[[0, 0, 2, 0, 2, 2, 4, 2, 4, 4, 0, 4]],
+                ),
+                mock_coco_annotation(
+                    category_id=0,
+                    bbox=(3, 0, 2, 2),
+                    area=2 * 2,
+                    segmentation={
+                        "size": [5, 5],
+                        "counts": [15, 2, 3, 2, 3],
+                    },
+                    iscrowd=True,
+                ),
+            ],
+            (5, 5),
+            True,
+            Detections(
+                xyxy=np.array([[0, 0, 5, 5], [3, 0, 5, 2]], dtype=np.float32),
+                class_id=np.array([0, 0], dtype=int),
+                mask=np.array(
+                    [
+                        [
+                            [1, 1, 1, 0, 0],
+                            [1, 1, 1, 0, 0],
+                            [1, 1, 1, 1, 1],
+                            [1, 1, 1, 1, 1],
+                            [1, 1, 1, 1, 1],
+                        ],
+                        [
+                            [0, 0, 0, 1, 1],
+                            [0, 0, 0, 1, 1],
+                            [0, 0, 0, 0, 0],
+                            [0, 0, 0, 0, 0],
+                            [0, 0, 0, 0, 0],
+                        ],
+                    ]
+                ),
+            ),
+            DoesNotRaise(),
+        ),  # two image annotations with mask, one mask as polygon ans second as RLE
+        (
+            [
+                mock_coco_annotation(
+                    category_id=0,
+                    bbox=(3, 0, 2, 2),
+                    area=2 * 2,
+                    segmentation={
+                        "size": [5, 5],
+                        "counts": [15, 2, 3, 2, 3],
+                    },
+                    iscrowd=True,
+                ),
+                mock_coco_annotation(
+                    category_id=1,
+                    bbox=(0, 0, 5, 5),
+                    area=5 * 5,
+                    segmentation=[[0, 0, 2, 0, 2, 2, 4, 2, 4, 4, 0, 4]],
+                ),
+            ],
+            (5, 5),
+            True,
+            Detections(
+                xyxy=np.array([[3, 0, 5, 2], [0, 0, 5, 5]], dtype=np.float32),
+                class_id=np.array([0, 1], dtype=int),
+                mask=np.array(
+                    [
+                        [
+                            [0, 0, 0, 1, 1],
+                            [0, 0, 0, 1, 1],
+                            [0, 0, 0, 0, 0],
+                            [0, 0, 0, 0, 0],
+                            [0, 0, 0, 0, 0],
+                        ],
+                        [
+                            [1, 1, 1, 0, 0],
+                            [1, 1, 1, 0, 0],
+                            [1, 1, 1, 1, 1],
+                            [1, 1, 1, 1, 1],
+                            [1, 1, 1, 1, 1],
+                        ],
+                    ]
+                ),
+            ),
+            DoesNotRaise(),
+        ),  # two image annotations with mask, first mask as RLE and second as polygon
    ],
 )
 def test_coco_annotations_to_detections(
@ -301,3 +429,131 @@ def test_build_coco_class_index_mapping(
            coco_categories=coco_categories, target_classes=target_classes
        )
        assert result == expected_result
+
+
+@pytest.mark.parametrize(
+    "detections, image_id, annotation_id, expected_result, exception",
+    [
+        (
+            Detections(
+                xyxy=np.array([[0, 0, 100, 100]], dtype=np.float32),
+                class_id=np.array([0], dtype=int),
+            ),
+            0,
+            0,
+            [
+                mock_coco_annotation(
+                    category_id=0, bbox=(0, 0, 100, 100), area=100 * 100
+                )
+            ],
+            DoesNotRaise(),
+        ),  # no segmentation mask
+        (
+            Detections(
+                xyxy=np.array([[0, 0, 4, 5]], dtype=np.float32),
+                class_id=np.array([0], dtype=int),
+                mask=np.array(
+                    [
+                        [
+                            [1, 1, 1, 1, 0],
+                            [1, 1, 1, 1, 0],
+                            [1, 1, 1, 1, 0],
+                            [1, 1, 1, 1, 0],
+                            [1, 1, 1, 1, 0],
+                        ]
+                    ]
+                ),
+            ),
+            0,
+            0,
+            [
+                mock_coco_annotation(
+                    category_id=0,
+                    bbox=(0, 0, 4, 5),
+                    area=4 * 5,
+                    segmentation=[[0, 0, 0, 4, 3, 4, 3, 0]],
+                )
+            ],
+            DoesNotRaise(),
+        ),  # segmentation mask in single component,no holes in mask,
+        # expects polygon mask
+        (
+            Detections(
+                xyxy=np.array([[0, 0, 5, 5]], dtype=np.float32),
+                class_id=np.array([0], dtype=int),
+                mask=np.array(
+                    [
+                        [
+                            [1, 1, 1, 0, 0],
+                            [1, 1, 1, 0, 0],
+                            [1, 1, 1, 0, 0],
+                            [0, 0, 0, 1, 1],
+                            [0, 0, 0, 1, 1],
+                        ]
+                    ]
+                ),
+            ),
+            0,
+            0,
+            [
+                mock_coco_annotation(
+                    category_id=0,
+                    bbox=(0, 0, 5, 5),
+                    area=5 * 5,
+                    segmentation={
+                        "size": [5, 5],
+                        "counts": [0, 3, 2, 3, 2, 3, 5, 2, 3, 2],
+                    },
+                    iscrowd=True,
+                )
+            ],
+            DoesNotRaise(),
+        ),  # segmentation mask with 2 components, no holes in mask, expects RLE mask
+        (
+            Detections(
+                xyxy=np.array([[0, 0, 5, 5]], dtype=np.float32),
+                class_id=np.array([0], dtype=int),
+                mask=np.array(
+                    [
+                        [
+                            [0, 1, 1, 1, 1],
+                            [0, 1, 1, 1, 1],
+                            [1, 1, 0, 0, 1],
+                            [1, 1, 0, 0, 1],
+                            [1, 1, 1, 1, 1],
+                        ]
+                    ]
+                ),
+            ),
+            0,
+            0,
+            [
+                mock_coco_annotation(
+                    category_id=0,
+                    bbox=(0, 0, 5, 5),
+                    area=5 * 5,
+                    segmentation={
+                        "size": [5, 5],
+                        "counts": [2, 10, 2, 3, 2, 6],
+                    },
+                    iscrowd=True,
+                )
+            ],
+            DoesNotRaise(),
+        ),  # seg mask in single component, with holes in mask, expects RLE mask
+    ],
+)
+def test_detections_to_coco_annotations(
+    detections: Detections,
+    image_id: int,
+    annotation_id: int,
+    expected_result: List[Dict],
+    exception: Exception,
+) -> None:
+    with exception:
+        result, _ = detections_to_coco_annotations(
+            detections=detections,
+            image_id=image_id,
+            annotation_id=annotation_id,
+        )
+        assert result == expected_result
--- a/test/dataset/test_utils.py
+++ b/test/dataset/test_utils.py
@ -2,13 +2,17 @@ from contextlib import ExitStack as DoesNotRaise
 from test.test_utils import mock_detections
 from typing import Dict, List, Optional, Tuple, TypeVar

+import numpy as np
+import numpy.typing as npt
 import pytest

 from supervision import Detections
 from supervision.dataset.utils import (
    build_class_index_mapping,
    map_detections_class_id,
+    mask_to_rle,
    merge_class_lists,
+    rle_to_mask,
    train_test_split,
 )

@ -229,3 +233,131 @@ def test_map_detections_class_id(
            source_to_target_mapping=source_to_target_mapping, detections=detections
        )
        assert result == expected_result
+
+
+@pytest.mark.parametrize(
+    "mask, expected_rle, exception",
+    [
+        (
+            np.zeros((3, 3)).astype(bool),
+            [9],
+            DoesNotRaise(),
+        ),  # mask with background only (mask with only False values)
+        (
+            np.ones((3, 3)).astype(bool),
+            [0, 9],
+            DoesNotRaise(),
+        ),  # mask with foreground only (mask with only True values)
+        (
+            np.array(
+                [
+                    [0, 0, 0, 0, 0],
+                    [0, 1, 1, 1, 0],
+                    [0, 1, 0, 1, 0],
+                    [0, 1, 1, 1, 0],
+                    [0, 0, 0, 0, 0],
+                ]
+            ).astype(bool),
+            [6, 3, 2, 1, 1, 1, 2, 3, 6],
+            DoesNotRaise(),
+        ),  # mask where foreground object has hole
+        (
+            np.array(
+                [
+                    [1, 0, 1, 0, 1],
+                    [1, 0, 1, 0, 1],
+                    [1, 0, 1, 0, 1],
+                    [1, 0, 1, 0, 1],
+                    [1, 0, 1, 0, 1],
+                ]
+            ).astype(bool),
+            [0, 5, 5, 5, 5, 5],
+            DoesNotRaise(),
+        ),  # mask where foreground consists of 3 separate components
+        (
+            np.array([[[]]]).astype(bool),
+            None,
+            pytest.raises(AssertionError),
+        ),  # raises AssertionError because mask dimentionality is not 2D
+        (
+            np.array([[]]).astype(bool),
+            None,
+            pytest.raises(AssertionError),
+        ),  # raises AssertionError because mask is empty
+    ],
+)
+def test_mask_to_rle(
+    mask: npt.NDArray[np.bool_], expected_rle: List[int], exception: Exception
+) -> None:
+    with exception:
+        result = mask_to_rle(mask=mask)
+        assert result == expected_rle
+
+
+@pytest.mark.parametrize(
+    "rle, resolution_wh, expected_mask, exception",
+    [
+        (
+            np.array([9]),
+            [3, 3],
+            np.zeros((3, 3)).astype(bool),
+            DoesNotRaise(),
+        ),  # mask with background only (mask with only False values); rle as array
+        (
+            [9],
+            [3, 3],
+            np.zeros((3, 3)).astype(bool),
+            DoesNotRaise(),
+        ),  # mask with background only (mask with only False values); rle as list
+        (
+            np.array([0, 9]),
+            [3, 3],
+            np.ones((3, 3)).astype(bool),
+            DoesNotRaise(),
+        ),  # mask with foreground only (mask with only True values)
+        (
+            np.array([6, 3, 2, 1, 1, 1, 2, 3, 6]),
+            [5, 5],
+            np.array(
+                [
+                    [0, 0, 0, 0, 0],
+                    [0, 1, 1, 1, 0],
+                    [0, 1, 0, 1, 0],
+                    [0, 1, 1, 1, 0],
+                    [0, 0, 0, 0, 0],
+                ]
+            ).astype(bool),
+            DoesNotRaise(),
+        ),  # mask where foreground object has hole
+        (
+            np.array([0, 5, 5, 5, 5, 5]),
+            [5, 5],
+            np.array(
+                [
+                    [1, 0, 1, 0, 1],
+                    [1, 0, 1, 0, 1],
+                    [1, 0, 1, 0, 1],
+                    [1, 0, 1, 0, 1],
+                    [1, 0, 1, 0, 1],
+                ]
+            ).astype(bool),
+            DoesNotRaise(),
+        ),  # mask where foreground consists of 3 separate components
+        (
+            np.array([0, 5, 5, 5, 5, 5]),
+            [2, 2],
+            None,
+            pytest.raises(AssertionError),
+        ),  # raises AssertionError because number of pixels in RLE does not match
+        # number of pixels in expected mask (width x height).
+    ],
+)
+def test_rle_to_mask(
+    rle: npt.NDArray[np.int_],
+    resolution_wh: Tuple[int, int],
+    expected_mask: npt.NDArray[np.bool_],
+    exception: Exception,
+) -> None:
+    with exception:
+        result = rle_to_mask(rle=rle, resolution_wh=resolution_wh)
+        assert np.all(result == expected_mask)
--- a/test/detection/test_core.py
+++ b/test/detection/test_core.py
@ -30,6 +30,84 @@ DETECTIONS = Detections(
 )


+# Merge test
+TEST_MASK = np.zeros((1000, 1000), dtype=bool)
+TEST_MASK[300:351, 200:251] = True
+TEST_DET_1 = Detections(
+    xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40], [50, 50, 60, 60]]),
+    mask=np.array([TEST_MASK, TEST_MASK, TEST_MASK]),
+    confidence=np.array([0.1, 0.2, 0.3]),
+    class_id=np.array([1, 2, 3]),
+    tracker_id=np.array([1, 2, 3]),
+    data={
+        "some_key": [1, 2, 3],
+        "other_key": [["1", "2"], ["3", "4"], ["5", "6"]],
+    },
+)
+TEST_DET_2 = Detections(
+    xyxy=np.array([[70, 70, 80, 80], [90, 90, 100, 100]]),
+    mask=np.array([TEST_MASK, TEST_MASK]),
+    confidence=np.array([0.4, 0.5]),
+    class_id=np.array([4, 5]),
+    tracker_id=np.array([4, 5]),
+    data={
+        "some_key": [4, 5],
+        "other_key": [["7", "8"], ["9", "10"]],
+    },
+)
+TEST_DET_1_2 = Detections(
+    xyxy=np.array(
+        [
+            [10, 10, 20, 20],
+            [30, 30, 40, 40],
+            [50, 50, 60, 60],
+            [70, 70, 80, 80],
+            [90, 90, 100, 100],
+        ]
+    ),
+    mask=np.array([TEST_MASK, TEST_MASK, TEST_MASK, TEST_MASK, TEST_MASK]),
+    confidence=np.array([0.1, 0.2, 0.3, 0.4, 0.5]),
+    class_id=np.array([1, 2, 3, 4, 5]),
+    tracker_id=np.array([1, 2, 3, 4, 5]),
+    data={
+        "some_key": [1, 2, 3, 4, 5],
+        "other_key": [["1", "2"], ["3", "4"], ["5", "6"], ["7", "8"], ["9", "10"]],
+    },
+)
+TEST_DET_ZERO_LENGTH = Detections(
+    xyxy=np.empty((0, 4), dtype=np.float32),
+    mask=np.empty((0, *TEST_MASK.shape), dtype=bool),
+    confidence=np.empty((0,)),
+    class_id=np.empty((0,)),
+    tracker_id=np.empty((0,)),
+    data={
+        "some_key": [],
+        "other_key": [],
+    },
+)
+TEST_DET_NONE = Detections(
+    xyxy=np.empty((0, 4), dtype=np.float32),
+)
+TEST_DET_DIFFERENT_FIELDS = Detections(
+    xyxy=np.array([[88, 88, 99, 99]]),
+    mask=np.array([np.logical_not(TEST_MASK)]),
+    confidence=None,
+    class_id=None,
+    tracker_id=np.array([9]),
+    data={"some_key": [9], "other_key": [["11", "12"]]},
+)
+TEST_DET_DIFFERENT_DATA = Detections(
+    xyxy=np.array([[88, 88, 99, 99]]),
+    mask=np.array([np.logical_not(TEST_MASK)]),
+    confidence=np.array([0.9]),
+    class_id=np.array([9]),
+    tracker_id=np.array([9]),
+    data={
+        "never_seen_key": [9],
+    },
+)
+
+
@pytest.mark.parametrize(
    "detections, index, expected_result, exception",
    [
@ -148,52 +226,58 @@ def test_getitem(
            DoesNotRaise(),
        ),  # single empty detections
        (
-            [mock_detections(xyxy=[[10, 10, 20, 20]])],
-            mock_detections(xyxy=[[10, 10, 20, 20]]),
+            [Detections.empty(), Detections.empty()],
+            Detections.empty(),
            DoesNotRaise(),
-        ),  # single detection with xyxy field
+        ),  # two empty detections
+        (
+            [TEST_DET_1],
+            TEST_DET_1,
+            DoesNotRaise(),
+        ),  # single detection with fields
+        (
+            [TEST_DET_NONE],
+            TEST_DET_NONE,
+            DoesNotRaise(),
+        ),  # Single weakly-defined detection
+        (
+            [TEST_DET_1, TEST_DET_2],
+            TEST_DET_1_2,
+            DoesNotRaise(),
+        ),  # Fields with same keys
+        # Fields and empty
+        (
+            [TEST_DET_1, Detections.empty()],
+            TEST_DET_1,
+            DoesNotRaise(),
+        ),  # single detection with fields
        (
            [
-                mock_detections(xyxy=[[10, 10, 20, 20]]),
-                mock_detections(xyxy=np.empty((0, 4), dtype=np.float32)),
+                TEST_DET_1,
+                TEST_DET_ZERO_LENGTH,
            ],
-            mock_detections(xyxy=[[10, 10, 20, 20]]),
+            TEST_DET_1,
            DoesNotRaise(),
-        ),  # single detection with xyxy field + empty detection
+        ),  # Single detection and empty-array fields
        (
            [
-                mock_detections(xyxy=[[10, 10, 20, 20]]),
-                mock_detections(xyxy=[[20, 20, 30, 30]]),
+                TEST_DET_1,
+                TEST_DET_NONE,
            ],
-            mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
+            TEST_DET_1,
            DoesNotRaise(),
-        ),  # two detections with xyxy field
+        ),  # Single detection and None fields (+ missing Dict keys)
+        # Errors: Non-zero-length differently defined keys & data
        (
-            [
-                mock_detections(xyxy=[[10, 10, 20, 20]], class_id=[0]),
-                mock_detections(xyxy=[[20, 20, 30, 30]]),
-            ],
-            mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
+            [TEST_DET_1, TEST_DET_DIFFERENT_FIELDS],
+            None,
            pytest.raises(ValueError),
-        ),  # detection with xyxy, class_id fields + detection with xyxy field
+        ),  # Non-empty detections with different fields
        (
-            [
-                mock_detections(xyxy=[[10, 10, 20, 20]], class_id=[0]),
-                mock_detections(xyxy=[[20, 20, 30, 30]], class_id=[1]),
-            ],
-            mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]], class_id=[0, 1]),
-            DoesNotRaise(),
-        ),  # two detections with xyxy, class_id fields
-        (
-            [
-                mock_detections(xyxy=[[10, 10, 20, 20]], data={"test": [1]}),
-                mock_detections(xyxy=[[20, 20, 30, 30]], data={"test": [2]}),
-            ],
-            mock_detections(
-                xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]], data={"test": [1, 2]}
-            ),
-            DoesNotRaise(),
-        ),  # two detections with xyxy, data fields
+            [TEST_DET_1, TEST_DET_DIFFERENT_DATA],
+            None,
+            pytest.raises(ValueError),
+        ),  # Non-empty detections with different data keys
    ],
 )
 def test_merge(
--- a/test/detection/test_lmm.py
+++ b/test/detection/test_lmm.py
@ -0,0 +1,131 @@
+from typing import List, Optional, Tuple
+
+import numpy as np
+import pytest
+
+from supervision.detection.lmm import from_paligemma
+
+
+@pytest.mark.parametrize(
+    "result, resolution_wh, classes, expected_results",
+    [
+        (
+            "",
+            (1000, 1000),
+            None,
+            (np.empty((0, 4)), None, np.empty(0).astype(str)),
+        ),  # empty response
+        (
+            "",
+            (1000, 1000),
+            ["cat", "dog"],
+            (np.empty((0, 4)), None, np.empty(0).astype(str)),
+        ),  # empty response with classes
+        (
+            "\n",
+            (1000, 1000),
+            None,
+            (np.empty((0, 4)), None, np.empty(0).astype(str)),
+        ),  # new line response
+        (
+            "the quick brown fox jumps over the lazy dog.",
+            (1000, 1000),
+            None,
+            (np.empty((0, 4)), None, np.empty(0).astype(str)),
+        ),  # response with no location
+        (
+            "<loc0256><loc0768><loc0768> cat",
+            (1000, 1000),
+            None,
+            (np.empty((0, 4)), None, np.empty(0).astype(str)),
+        ),  # response with missing location
+        (
+            "<loc0256><loc0256><loc0768><loc0768><loc0768> cat",
+            (1000, 1000),
+            None,
+            (np.empty((0, 4)), None, np.empty(0).astype(str)),
+        ),  # response with extra location
+        (
+            "<loc0256><loc0256><loc0768><loc0768>",
+            (1000, 1000),
+            None,
+            (np.empty((0, 4)), None, np.empty(0).astype(str)),
+        ),  # response with no class
+        (
+            "<loc0256><loc0256><loc0768><loc0768> catt",
+            (1000, 1000),
+            ["cat", "dog"],
+            (np.empty((0, 4)), np.empty(0), np.empty(0).astype(str)),
+        ),  # response with invalid class
+        (
+            "<loc0256><loc0256><loc0768><loc0768> cat",
+            (1000, 1000),
+            None,
+            (
+                np.array([[250.0, 250.0, 750.0, 750.0]]),
+                None,
+                np.array(["cat"]).astype(str),
+            ),
+        ),  # correct response; no classes
+        (
+            "<loc0256><loc0256><loc0768><loc0768> black cat",
+            (1000, 1000),
+            None,
+            (
+                np.array([[250.0, 250.0, 750.0, 750.0]]),
+                None,
+                np.array(["black cat"]).astype(np.dtype("U")),
+            ),
+        ),  # correct response; no classes
+        (
+            "<loc0256><loc0256><loc0768><loc0768> cat ;",
+            (1000, 1000),
+            ["cat", "dog"],
+            (
+                np.array([[250.0, 250.0, 750.0, 750.0]]),
+                np.array([0]),
+                np.array(["cat"]).astype(str),
+            ),
+        ),  # correct response; with classes
+        (
+            "<loc0256><loc0256><loc0768><loc0768> cat ; <loc0256><loc0256><loc0768><loc0768> dog",  # noqa: E501
+            (1000, 1000),
+            ["cat", "dog"],
+            (
+                np.array([[250.0, 250.0, 750.0, 750.0], [250.0, 250.0, 750.0, 750.0]]),
+                np.array([0, 1]),
+                np.array(["cat", "dog"]).astype(np.dtype("U")),
+            ),
+        ),  # correct response; with classes
+        (
+            "<loc0256><loc0256><loc0768><loc0768> cat ; <loc0256><loc0256><loc0768> cat",  # noqa: E501
+            (1000, 1000),
+            ["cat", "dog"],
+            (
+                np.array([[250.0, 250.0, 750.0, 750.0]]),
+                np.array([0]),
+                np.array(["cat"]).astype(str),
+            ),
+        ),  # partially correct response; with classes
+        (
+            "<loc0256><loc0256><loc0768><loc0768> cat ; <loc0256><loc0256><loc0768><loc0768><loc0768> cat",  # noqa: E501
+            (1000, 1000),
+            ["cat", "dog"],
+            (
+                np.array([[250.0, 250.0, 750.0, 750.0]]),
+                np.array([0]),
+                np.array(["cat"]).astype(str),
+            ),
+        ),  # partially correct response; with classes
+    ],
+)
+def test_from_paligemma(
+    result: str,
+    resolution_wh: Tuple[int, int],
+    classes: Optional[List[str]],
+    expected_results: Tuple[np.ndarray, Optional[np.ndarray], np.ndarray],
+) -> None:
+    result = from_paligemma(result=result, resolution_wh=resolution_wh, classes=classes)
+    np.testing.assert_array_equal(result[0], expected_results[0])
+    np.testing.assert_array_equal(result[1], expected_results[1])
+    np.testing.assert_array_equal(result[2], expected_results[2])
--- a/test/detection/test_utils.py
+++ b/test/detection/test_utils.py
@ -2,6 +2,7 @@ from contextlib import ExitStack as DoesNotRaise
 from typing import Any, Dict, List, Optional, Tuple

 import numpy as np
+import numpy.typing as npt
 import pytest

 from supervision.config import CLASS_NAME_DATA_FIELD
@ -9,6 +10,8 @@ from supervision.detection.utils import (
    box_non_max_suppression,
    calculate_masks_centroids,
    clip_boxes,
+    contains_holes,
+    contains_multiple_segments,
    filter_polygons_by_area,
    get_data_item,
    mask_non_max_suppression,
@ -911,6 +914,14 @@ def test_calculate_masks_centroids(
            {"test_1": []},
            DoesNotRaise(),
        ),  # single data dict with a single field name and empty list values
+        (
+            [
+                {"test_1": []},
+                {"test_1": []},
+            ],
+            {"test_1": []},
+            DoesNotRaise(),
+        ),  # two data dicts with the same field name and empty list values
        (
            [
                {"test_1": np.array([])},
@ -918,6 +929,14 @@ def test_calculate_masks_centroids(
            {"test_1": np.array([])},
            DoesNotRaise(),
        ),  # single data dict with a single field name and empty np.array values
+        (
+            [
+                {"test_1": np.array([])},
+                {"test_1": np.array([])},
+            ],
+            {"test_1": np.array([])},
+            DoesNotRaise(),
+        ),  # two data dicts with the same field name and empty np.array values
        (
            [
                {"test_1": [1, 2, 3]},
@ -932,7 +951,7 @@ def test_calculate_masks_centroids(
            ],
            {"test_1": [3, 2, 1]},
            DoesNotRaise(),
-        ),  # two data dicts with the same field name and empty and list values
+        ),  # two data dicts with the same field name; one of with empty list as value
        (
            [
                {"test_1": [1, 2, 3]},
@ -1012,6 +1031,49 @@ def test_calculate_masks_centroids(
            None,
            pytest.raises(ValueError),
        ),  # two data dicts with the same field name and different length arrays values
+        (
+            [{}, {"test_1": [1, 2, 3]}],
+            {"test_1": [1, 2, 3]},
+            DoesNotRaise(),
+        ),  # two data dicts; one empty and one non-empty dict
+        (
+            [{"test_1": [], "test_2": []}, {"test_1": [1, 2, 3], "test_2": [1, 2, 3]}],
+            {"test_1": [1, 2, 3], "test_2": [1, 2, 3]},
+            DoesNotRaise(),
+        ),  # two data dicts; one empty and one non-empty dict; same keys
+        (
+            [{"test_1": []}, {"test_1": [1, 2, 3], "test_2": [4, 5, 6]}],
+            None,
+            pytest.raises(ValueError),
+        ),  # two data dicts; one empty and one non-empty dict; different keys
+        (
+            [
+                {
+                    "test_1": [1, 2, 3],
+                    "test_2": [4, 5, 6],
+                    "test_3": [7, 8, 9],
+                },
+                {"test_1": [1, 2, 3], "test_2": [4, 5, 6]},
+            ],
+            None,
+            pytest.raises(ValueError),
+        ),  # two data dicts; one with three keys, one with two keys
+        (
+            [
+                {"test_1": [1, 2, 3]},
+                {"test_1": [1, 2, 3], "test_2": [1, 2, 3]},
+            ],
+            None,
+            pytest.raises(ValueError),
+        ),  # some keys missing in one dict
+        (
+            [
+                {"test_1": [1, 2, 3], "test_2": ["a", "b"]},
+                {"test_1": [4, 5], "test_2": ["c", "d", "e"]},
+            ],
+            None,
+            pytest.raises(ValueError),
+        ),  # different value lengths for the same key
    ],
 )
 def test_merge_data(
@ -1021,6 +1083,9 @@ def test_merge_data(
 ):
    with exception:
        result = merge_data(data_list=data_list)
+        if expected_result is None:
+            assert False, f"Expected an error, but got result {result}"
+
        for key in result:
            if isinstance(result[key], np.ndarray):
                assert np.array_equal(
@ -1203,3 +1268,138 @@ def test_get_data_item(
                assert (
                    result[key] == expected_result[key]
                ), f"Mismatch in non-array data for key {key}"
+
+
+@pytest.mark.parametrize(
+    "mask, expected_result, exception",
+    [
+        (
+            np.array([[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 0, 0], [0, 1, 1, 0]]).astype(
+                bool
+            ),
+            False,
+            DoesNotRaise(),
+        ),  # foreground object in one continuous piece
+        (
+            np.array([[1, 0, 0, 0], [1, 0, 0, 0], [0, 0, 0, 0], [0, 1, 1, 0]]).astype(
+                bool
+            ),
+            False,
+            DoesNotRaise(),
+        ),  # foreground object in 2 seperate elements
+        (
+            np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]).astype(
+                bool
+            ),
+            False,
+            DoesNotRaise(),
+        ),  # no foreground pixels in mask
+        (
+            np.array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]).astype(
+                bool
+            ),
+            False,
+            DoesNotRaise(),
+        ),  # only foreground pixels in mask
+        (
+            np.array([[1, 1, 1, 0], [1, 0, 1, 0], [1, 1, 1, 0], [0, 0, 0, 0]]).astype(
+                bool
+            ),
+            True,
+            DoesNotRaise(),
+        ),  # foreground object has 1 hole
+        (
+            np.array([[1, 1, 1, 0], [1, 0, 1, 1], [1, 1, 0, 1], [0, 1, 1, 1]]).astype(
+                bool
+            ),
+            True,
+            DoesNotRaise(),
+        ),  # foreground object has 2 holes
+    ],
+)
+def test_contains_holes(
+    mask: npt.NDArray[np.bool_], expected_result: bool, exception: Exception
+) -> None:
+    with exception:
+        result = contains_holes(mask)
+        assert result == expected_result
+
+
+@pytest.mark.parametrize(
+    "mask, connectivity, expected_result, exception",
+    [
+        (
+            np.array([[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 0, 0], [0, 1, 1, 0]]).astype(
+                bool
+            ),
+            4,
+            False,
+            DoesNotRaise(),
+        ),  # foreground object in one continuous piece
+        (
+            np.array([[1, 0, 0, 0], [1, 0, 0, 0], [0, 0, 0, 0], [0, 1, 1, 0]]).astype(
+                bool
+            ),
+            4,
+            True,
+            DoesNotRaise(),
+        ),  # foreground object in 2 seperate elements
+        (
+            np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]).astype(
+                bool
+            ),
+            4,
+            False,
+            DoesNotRaise(),
+        ),  # no foreground pixels in mask
+        (
+            np.array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]).astype(
+                bool
+            ),
+            4,
+            False,
+            DoesNotRaise(),
+        ),  # only foreground pixels in mask
+        (
+            np.array([[1, 1, 1, 0], [1, 0, 1, 1], [1, 1, 0, 1], [0, 1, 1, 1]]).astype(
+                bool
+            ),
+            4,
+            False,
+            DoesNotRaise(),
+        ),  # foreground object has 2 holes, but is in single piece
+        (
+            np.array([[1, 1, 0, 0], [1, 1, 0, 1], [1, 0, 1, 1], [0, 0, 1, 1]]).astype(
+                bool
+            ),
+            4,
+            True,
+            DoesNotRaise(),
+        ),  # foreground object in 2 elements with respect to 4-way connectivity
+        (
+            np.array([[1, 1, 0, 0], [1, 1, 0, 1], [1, 0, 1, 1], [0, 0, 1, 1]]).astype(
+                bool
+            ),
+            8,
+            False,
+            DoesNotRaise(),
+        ),  # foreground object in single piece with respect to 8-way connectivity
+        (
+            np.array([[1, 1, 0, 0], [1, 1, 0, 1], [1, 0, 1, 1], [0, 0, 1, 1]]).astype(
+                bool
+            ),
+            5,
+            None,
+            pytest.raises(ValueError),
+        ),  # Incorrect connectivity parameter value, raises ValueError
+    ],
+)
+def test_contains_multiple_segments(
+    mask: npt.NDArray[np.bool_],
+    connectivity: int,
+    expected_result: bool,
+    exception: Exception,
+) -> None:
+    with exception:
+        result = contains_multiple_segments(mask=mask, connectivity=connectivity)
+        assert result == expected_result