diff --git a/.github/workflows/poetry-test.yml b/.github/workflows/poetry-test.yml
new file mode 100644
index 00000000..95e2eb7f
--- /dev/null
+++ b/.github/workflows/poetry-test.yml
@@ -0,0 +1,37 @@
+name: 🔧 Poetry Check and Installation Test Workflow
+on:
+  push:
+    paths:
+      - 'poetry.lock'
+      - 'pyproject.toml'
+  pull_request:
+    paths:
+      - 'poetry.lock'
+      - 'pyproject.toml'
+  workflow_dispatch:
+
+jobs:
+  poetry-tests:
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, windows-latest, macos-latest]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12", "3.13"]
+    runs-on: ${{ matrix.os }}
+    steps:
+      - name: 📥 Checkout the repository
+        uses: actions/checkout@v4
+
+      - name: 🐍 Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: 📦 Install the base dependencies
+        run: python -m pip install --upgrade poetry
+
+      - name: 🔍 Check the correctness of the project config
+        run: poetry check
+
+      - name: 🚀 Do Install the package Test
+        run: poetry install
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 1ce4a267..b33f8e53 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -9,7 +9,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12", "3.13"]
     steps:
       - name: 🛎️ Checkout
         uses: actions/checkout@v4
diff --git a/.github/workflows/welcome.yml b/.github/workflows/welcome.yml
deleted file mode 100644
index 4cb70b93..00000000
--- a/.github/workflows/welcome.yml
+++ /dev/null
@@ -1,18 +0,0 @@
-name: Welcome WorkFlow
-
-on:
-  issues:
-    types: [opened]
-  pull_request_target:
-    types: [opened]
-
-jobs:
-  build:
-    name: 👋 Welcome
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/first-interaction@v1.3.0
-        with:
-          repo-token: ${{ secrets.GITHUB_TOKEN }}
-          issue-message: "Hello there, thank you for opening an Issue ! 🙏🏻 The team was notified and they will get back to you asap."
-          pr-message:  "Hello there, thank you for opening an PR ! 🙏🏻 The team was notified and they will get back to you asap."
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index d8e34a97..de50d5e0 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -7,7 +7,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v5.0.0
     hooks:
       - id: trailing-whitespace
         exclude: test/.*\.py
@@ -32,7 +32,7 @@ repos:
           additional_dependencies: ["bandit[toml]"]
 
   -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.6.8
+      rev: v0.7.2
       hooks:
       -   id: ruff
           args: [--fix, --exit-non-zero-on-fix]
diff --git a/README.md b/README.md
index 9a0cc305..fe569183 100644
--- a/README.md
+++ b/README.md
@@ -34,8 +34,6 @@
 
 **We write your reusable computer vision tools.** Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us! 🤝
 
-[![supervision-hackfest](https://github.com/roboflow/supervision/assets/26109316/c05cc954-b9a6-4ed5-9a52-d0b4b619ff65)](https://github.com/orgs/roboflow/projects)
-
 ## 💻 install
 
 Pip install the supervision package in a
diff --git a/docs/changelog.md b/docs/changelog.md
index e7dbb18e..976a78d0 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -1,5 +1,144 @@
 # CHANGELOG
 
+### 0.24.0 <small>Oct 4, 2024</small>
+
+- Added [F1 score](https://supervision.roboflow.com/0.24.0/metrics/f1_score/#supervision.metrics.f1_score.F1Score) as a new metric for detection and segmentation. [#1521](https://github.com/roboflow/supervision/pull/1521)
+
+```python
+import supervision as sv
+from supervision.metrics import F1Score
+
+predictions = sv.Detections(...)
+targets = sv.Detections(...)
+
+f1_metric = F1Score()
+f1_result = f1_metric.update(predictions, targets).compute()
+
+print(f1_result)
+print(f1_result.f1_50)
+print(f1_result.small_objects.f1_50)
+```
+
+- Added new cookbook: [Small Object Detection with SAHI](https://supervision.roboflow.com/0.24.0/notebooks/small-object-detection-with-sahi/). This cookbook provides a detailed guide on using [`InferenceSlicer`](https://supervision.roboflow.com/0.24.0/detection/tools/inference_slicer/) for small object detection. [#1483](https://github.com/roboflow/supervision/pull/1483)
+
+- Added an [Embedded Workflow](https://roboflow.com/workflows), which allows you to [preview annotators](https://supervision.roboflow.com/0.24.0/detection/annotators/). [#1533](https://github.com/roboflow/supervision/pull/1533)
+
+- Enhanced [`LineZoneAnnotator`](https://supervision.roboflow.com/0.24.0/detection/tools/line_zone/#supervision.detection.line_zone.LineZoneAnnotator), allowing the labels to align with the line, even when it's not horizontal. Also, you can now disable text background, and choose to draw labels off-center which minimizes overlaps for multiple [`LineZone`](https://supervision.roboflow.com/0.24.0/detection/tools/line_zone/#supervision.detection.line_zone.LineZone) labels. [#854](https://github.com/roboflow/supervision/pull/854)
+
+```python
+import supervision as sv
+import cv2
+
+image = cv2.imread("<SOURCE_IMAGE_PATH>")
+
+line_zone = sv.LineZone(
+    start=sv.Point(0, 100),
+    end=sv.Point(50, 200)
+)
+line_zone_annotator = sv.LineZoneAnnotator(
+    text_orient_to_line=True,
+    display_text_box=False,
+    text_centered=False
+)
+
+annotated_frame = line_zone_annotator.annotate(
+    frame=image.copy(), line_counter=line_zone
+)
+
+sv.plot_image(frame)
+```
+
+- Added per-class counting capabilities to [`LineZone`](https://supervision.roboflow.com/0.24.0/detection/tools/line_zone/#supervision.detection.line_zone.LineZone) and introduced [`LineZoneAnnotatorMulticlass`](https://supervision.roboflow.com/0.24.0/detection/tools/line_zone/#supervision.detection.line_zone.LineZoneAnnotatorMulticlass) for visualizing the counts per class. This feature allows tracking of individual classes crossing a line, enhancing the flexibility of use cases like traffic monitoring or crowd analysis. [#1555](https://github.com/roboflow/supervision/pull/1555)
+
+```python
+import supervision as sv
+import cv2
+
+image = cv2.imread("<SOURCE_IMAGE_PATH>")
+
+line_zone = sv.LineZone(
+    start=sv.Point(0, 100),
+    end=sv.Point(50, 200)
+)
+line_zone_annotator = sv.LineZoneAnnotatorMulticlass()
+
+frame = line_zone_annotator.annotate(
+    frame=frame, line_zones=[line_zone]
+)
+
+sv.plot_image(frame)
+```
+
+- Added [`from_easyocr`](https://supervision.roboflow.com/0.24.0/detection/core/#supervision.detection.core.Detections.from_easyocr), allowing integration of OCR results into the supervision framework. [EasyOCR](https://github.com/JaidedAI/EasyOCR) is an open-source optical character recognition (OCR) library that can read text from images. [#1515](https://github.com/roboflow/supervision/pull/1515)
+
+```python
+import supervision as sv
+import easyocr
+import cv2
+
+image = cv2.imread("<SOURCE_IMAGE_PATH>")
+
+reader = easyocr.Reader(["en"])
+result = reader.readtext("<SOURCE_IMAGE_PATH>", paragraph=True)
+detections = sv.Detections.from_easyocr(result)
+
+box_annotator = sv.BoxAnnotator(color_lookup=sv.ColorLookup.INDEX)
+label_annotator = sv.LabelAnnotator(color_lookup=sv.ColorLookup.INDEX)
+
+annotated_image = image.copy()
+annotated_image = box_annotator.annotate(scene=annotated_image, detections=detections)
+annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections)
+
+sv.plot_image(annotated_image)
+```
+
+- Added [`oriented_box_iou_batch`](https://supervision.roboflow.com/0.24.0/detection/utils/#supervision.detection.utils.oriented_box_iou_batch) function to `detection.utils`. This function computes Intersection over Union (IoU) for oriented or rotated bounding boxes (OBB). [#1502](https://github.com/roboflow/supervision/pull/1502)
+
+```python
+import numpy as np
+
+boxes_true = np.array([[[1, 0], [0, 1], [3, 4], [4, 3]]])
+boxes_detection = np.array([[[1, 1], [2, 0], [4, 2], [3, 3]]])
+ious = sv.oriented_box_iou_batch(boxes_true, boxes_detection)
+print("IoU between true and detected boxes:", ious)
+```
+
+- Extended [`PolygonZoneAnnotator`](https://supervision.roboflow.com/0.24.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZoneAnnotator) to allow setting opacity when drawing zones, providing enhanced visualization by filling the zone with adjustable transparency. [#1527](https://github.com/roboflow/supervision/pull/1527)
+
+```python
+import cv2
+from ncnn.model_zoo import get_model
+import supervision as sv
+
+image = cv2.imread("<SOURCE_IMAGE_PATH>")
+model = get_model(
+    "yolov8s",
+    target_size=640,
+    prob_threshold=0.5,
+    nms_threshold=0.45,
+    num_threads=4,
+    use_gpu=True,
+)
+result = model(image)
+detections = sv.Detections.from_ncnn(result)
+```
+
+!!! failure "Removed"
+
+    The `frame_resolution_wh` parameter in [`PolygonZone`](https://supervision.roboflow.com/0.24.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) has been removed.
+
+!!! failure "Removed"
+
+    Supervision installation methods `"headless"` and `"desktop"` were removed, as they are no longer needed. `pip install supervision[headless]` will install the base library and harmlessly warn of non-existent extras.
+
+- Supervision now depends on `opencv-python` rather than `opencv-python-headless`. [#1530](https://github.com/roboflow/supervision/pull/1530)
+
+- Fixed the COCO 101 point Average Precision algorithm to correctly interpolate precision, providing a more precise calculation of average precision without averaging out intermediate values. [#1500](https://github.com/roboflow/supervision/pull/1500)
+
+- Resolved miscellaneous issues highlighted when building documentation. This mostly includes whitespace adjustments and type inconsistencies. Updated documentation for clarity and fixed formatting issues. Added explicit version for `mkdocstrings-python`. [#1549](https://github.com/roboflow/supervision/pull/1549)
+
+- Enabled and fixed Ruff rules for code formatting, including changes like avoiding unnecessary iterable allocations and using Optional for default mutable arguments. [#1526](https://github.com/roboflow/supervision/pull/1526)
+
 ### 0.23.0 <small>Aug 28, 2024</small>
 
 - Added [#930](https://github.com/roboflow/supervision/pull/930): `IconAnnotator`, a [new annotator](https://supervision.roboflow.com/0.23.0/detection/annotators/#supervision.annotators.core.IconAnnotator) that allows drawing icons on each detection. Useful if you want to draw a specific icon for each class.
@@ -117,15 +256,19 @@ for frame in sv.get_video_frames_generator(
 - Fix [#1424](https://github.com/roboflow/supervision/pull/1424): `plot_image` function now clearly indicates that the size is in inches.
 
 !!! failure "Removed"
+
     The `track_buffer`, `track_thresh`, and `match_thresh` parameters in [`ByteTrack`](trackers.md/#supervision.tracker.byte_tracker.core.ByteTrack) are deprecated and were removed as of `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
 
 !!! failure "Removed"
+
     The `triggering_position` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) was removed as of `supervision-0.23.0`. Use `triggering_anchors` instead.
 
 !!! failure "Deprecated"
+
     `overlap_filter_strategy` in `InferenceSlicer.__init__` is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_strategy` instead.
 
 !!! failure "Deprecated"
+
     `overlap_ratio_wh` in `InferenceSlicer.__init__` is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_wh` instead.
 
 ### 0.22.0 <small>Jul 12, 2024</small>
@@ -133,9 +276,11 @@ for frame in sv.get_video_frames_generator(
 - Added [#1326](https://github.com/roboflow/supervision/pull/1326): [`sv.DetectionsDataset`](https://supervision.roboflow.com/0.22.0/datasets/core/#supervision.dataset.core.DetectionDataset) and [`sv.ClassificationDataset`](https://supervision.roboflow.com/0.22.0/datasets/core/#supervision.dataset.core.ClassificationDataset) allowing to load the images into memory only when necessary (lazy loading).
 
 !!! failure "Deprecated"
+
     Constructing `DetectionDataset` with parameter `images` as `Dict[str, np.ndarray]` is deprecated and will be removed in `supervision-0.26.0`. Please pass a list of paths `List[str]` instead.
 
 !!! failure "Deprecated"
+
     The `DetectionDataset.images` property is deprecated and will be removed in `supervision-0.26.0`. Please loop over images with `for path, image, annotation in dataset:`, as that does not require loading all images into memory.
 
 ```python
@@ -192,7 +337,7 @@ annotated_frame = mask_annotator.annotate(scene=image.copy(), detections=detecti
 ```
 
 - Added [#1277](https://github.com/roboflow/supervision/pull/1277): if you provide a font that supports symbols of a language, [`sv.RichLabelAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.LabelAnnotator.annotate) will draw them on your images.
-    - Various other annotators have been revised to ensure proper in-place functionality when used with `numpy` arrays. Additionally, we fixed a bug where `sv.ColorAnnotator` was filling boxes with solid color when used in-place.
+  - Various other annotators have been revised to ensure proper in-place functionality when used with `numpy` arrays. Additionally, we fixed a bug where `sv.ColorAnnotator` was filling boxes with solid color when used in-place.
 
 ```python
 import cv2
@@ -230,9 +375,11 @@ annotated_image = obb_annotator.annotate(scene=image.copy(), detections=detectio
 - Fixed [#1312](https://github.com/roboflow/supervision/pull/1312): Fixed [`CropAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.TraceAnnotator.annotate).
 
 !!! failure "Removed"
+
     `BoxAnnotator` was removed, however `BoundingBoxAnnotator` has been renamed to `BoxAnnotator`. Use a combination of [`BoxAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`LabelAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.LabelAnnotator) to simulate old `BoundingBox` behavior.
 
 !!! failure "Deprecated"
+
     The name `BoundingBoxAnnotator` has been deprecated and will be removed in `supervision-0.26.0`. It has been renamed to [`BoxAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.BoxAnnotator).
 
 - Added [#975](https://github.com/roboflow/supervision/pull/975) 📝 New Cookbooks: serialize detections into [json](https://github.com/roboflow/supervision/blob/de896189b83a1f9434c0a37dd9192ee00d2a1283/docs/notebooks/serialise-detections-to-json.ipynb) and [csv](https://github.com/roboflow/supervision/blob/de896189b83a1f9434c0a37dd9192ee00d2a1283/docs/notebooks/serialise-detections-to-csv.ipynb).
@@ -242,27 +389,35 @@ annotated_image = obb_annotator.annotate(scene=image.copy(), detections=detectio
 - Added [#1340](https://github.com/roboflow/supervision/pull/1340): Two new methods for converting between bounding box formats - [`xywh_to_xyxy`](https://supervision.roboflow.com/0.22.0/detection/utils/#supervision.detection.utils.xywh_to_xyxy) and [`xcycwh_to_xyxy`](https://supervision.roboflow.com/0.22.0/detection/utils/#supervision.detection.utils.xcycwh_to_xyxy)
 
 !!! failure "Removed"
+
     `from_roboflow` method has been removed due to deprecation. Use [from_inference](https://supervision.roboflow.com/0.22.0/detection/core/#supervision.detection.core.Detections.from_inference) instead.
 
 !!! failure "Removed"
+
     `Color.white()` has been removed due to deprecation. Use `color.WHITE` instead.
 
 !!! failure "Removed"
+
     `Color.black()` has been removed due to deprecation. Use `color.BLACK` instead.
 
 !!! failure "Removed"
+
     `Color.red()` has been removed due to deprecation. Use `color.RED` instead.
 
 !!! failure "Removed"
+
     `Color.green()` has been removed due to deprecation. Use `color.GREEN` instead.
 
 !!! failure "Removed"
+
     `Color.blue()` has been removed due to deprecation. Use `color.BLUE` instead.
 
 !!! failure "Removed"
+
     `ColorPalette.default()` has been removed due to deprecation. Use [ColorPalette.DEFAULT](https://supervision.roboflow.com/0.22.0/utils/draw/#supervision.draw.color.ColorPalette.DEFAULT) instead.
 
 !!! failure "Removed"
+
     `FPSMonitor.__call__` has been removed due to deprecation. Use the attribute [FPSMonitor.fps](https://supervision.roboflow.com/0.22.0/utils/video/#supervision.utils.video.FPSMonitor.fps) instead.
 
 ### 0.21.0 <small>Jun 5, 2024</small>
@@ -371,6 +526,7 @@ annotated_image = edge_annotators.annotate(image.copy(), keypoints)
 - Changed [#1109](https://github.com/roboflow/supervision/pull/1109): [`sv.PolygonZone`](/0.20.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) such that the `frame_resolution_wh` argument is no longer required to initialize `sv.PolygonZone`.
 
 !!! failure "Deprecated"
+
     The `frame_resolution_wh` parameter in `sv.PolygonZone` is deprecated and will be removed in `supervision-0.24.0`.
 
 - Changed [#1084](https://github.com/roboflow/supervision/pull/1084): [`sv.get_polygon_center`](/0.20.0/utils/geometry/#supervision.geometry.core.utils.get_polygon_center) to calculate a more accurate polygon centroid.
@@ -476,11 +632,13 @@ annotated_frame = crop_annotator.annotate(
 - Changed [#787](https://github.com/roboflow/supervision/pull/787): [`sv.ByteTrack`](/0.19.0/trackers/#supervision.tracker.ByteTrack) input arguments and docstrings updated to improve readability and ease of use.
 
 !!! failure "Deprecated"
+
     The `track_buffer`, `track_thresh`, and `match_thresh` parameters in `sv.ByteTrack` are deprecated and will be removed in `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
 
 - Changed [#910](https://github.com/roboflow/supervision/pull/910): [`sv.PolygonZone`](/0.19.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) to now accept a list of specific box anchors that must be in zone for a detection to be counted.
 
 !!! failure "Deprecated"
+
     The `triggering_position ` parameter in `sv.PolygonZone` is deprecated and will be removed in `supervision-0.23.0`. Use `triggering_anchors` instead.
 
 - Changed [#875](https://github.com/roboflow/supervision/pull/875): annotators adding support for Pillow images. All supervision Annotators can now accept an image as either a numpy array or a Pillow Image. They automatically detect its type, draw annotations, and return the output in the same format as the input.
@@ -544,6 +702,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 - Changed [#756](https://github.com/roboflow/supervision/pull/756): [`sv.Color`](/0.18.0/draw/color/#color)'s and [`sv.ColorPalette`](/0.18.0/draw/color/#colorpalette)'s method of accessing predefined colors, transitioning from a function-based approach (`sv.Color.red()`) to a more intuitive and conventional property-based method (`sv.Color.RED`).
 
 !!! failure "Deprecated"
+
     `sv.ColorPalette.default()` is deprecated and will be removed in `supervision-0.22.0`. Use `sv.ColorPalette.DEFAULT` instead.
 
 - Changed [#769](https://github.com/roboflow/supervision/pull/769): [`sv.ColorPalette.DEFAULT`](/0.18.0/draw/color/#colorpalette) value, giving users a more extensive set of annotation colors.
@@ -551,6 +710,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 - Changed [#677](https://github.com/roboflow/supervision/pull/677): `sv.Detections.from_roboflow` to [`sv.Detections.from_inference`](/0.18.0/detection/core/#supervision.detection.core.Detections.from_inference) streamlining its functionality to be compatible with both the both [inference](https://github.com/roboflow/inference) pip package and the Robloflow [hosted API](https://docs.roboflow.com/deploy/hosted-api).
 
 !!! failure "Deprecated"
+
     `Detections.from_roboflow()` is deprecated and will be removed in `supervision-0.22.0`. Use `Detections.from_inference` instead.
 
 - Fixed [#735](https://github.com/roboflow/supervision/pull/735): [`sv.LineZone`](/0.18.0/detection/tools/line_zone/#linezone) functionality to accurately update the counter when an object crosses a line from any direction, including from the side. This enhancement enables more precise tracking and analytics, such as calculating individual in/out counts for each lane on the road.
@@ -648,6 +808,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 - Fixed [#430](https://github.com/roboflow/supervision/pull/430): [`sv.ByteTrack`](/0.16.0/trackers/#supervision.tracker.byte_tracker.core.ByteTrack) to return `np.array([], dtype=int)` when `svDetections` is empty.
 
 !!! failure "Deprecated"
+
     `sv.Detections.from_yolov8` and `sv.Classifications.from_yolov8` as those are now replaced by [`sv.Detections.from_ultralytics`](/0.16.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) and [`sv.Classifications.from_ultralytics`](/0.16.0/classification/core/#supervision.classification.core.Classifications.from_ultralytics).
 
 ### 0.15.0 <small>October 5, 2023</small>
@@ -715,6 +876,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 - Added [#281](https://github.com/roboflow/supervision/pull/281): [`sv.Classifications.from_ultralytics`](/0.14.0/classification/core/#supervision.classification.core.Classifications.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use supervision with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.
 
 !!! failure "Deprecated"
+
     [sv.Detections.from_yolov8](/0.14.0/detection/core/#supervision.detection.core.Detections.from_yolov8) and [sv.Classifications.from_yolov8](/0.14.0/classification/core/#supervision.classification.core.Classifications.from_yolov8) are now deprecated and will be removed with `supervision-0.16.0` release.
 
 - Added [#341](https://github.com/roboflow/supervision/pull/341): First supervision usage example script showing how to detect and track objects on video using YOLOv8 + Supervision.
@@ -752,6 +914,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 - Added [#222](https://github.com/roboflow/supervision/pull/222): [`sv.Detections.from_ultralytics`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use `supervision` with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.
 
 !!! failure "Deprecated"
+
     [`sv.Detections.from_yolov8`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_yolov8) is now deprecated and will be removed with `supervision-0.15.0` release.
 
 - Added [#191](https://github.com/roboflow/supervision/pull/191): [`sv.Detections.from_paddledet`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_paddledet) to enable seamless integration with [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) framework.
@@ -761,6 +924,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 ### 0.12.0 <small>July 24, 2023</small>
 
 !!! failure "Python 3.7. Support Terminated"
+
     With the `supervision-0.12.0` release, we are terminating official support for Python 3.7.
 
 - Added [#177](https://github.com/roboflow/supervision/pull/177): initial support for object detection model benchmarking with [`sv.ConfusionMatrix`](/0.12.0/metrics/detection/#confusionmatrix).
diff --git a/docs/deprecated.md b/docs/deprecated.md
index bdd95a9d..d355bf96 100644
--- a/docs/deprecated.md
+++ b/docs/deprecated.md
@@ -7,8 +7,6 @@ status: deprecated
 
 These features are phased out due to better alternatives or potential issues in future versions. Deprecated functionalities are supported for **five subsequent releases**, providing time for users to transition to updated methods.
 
-- The `frame_resolution_wh ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) will be removed in `supervision-0.24.0`.
-
 - Constructing [`DetectionDataset`](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.DetectionDataset) and [`ClassificationDataset`](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.ClassificationDataset) with parameter `images` as `Dict[str, np.ndarray]` will be removed in `supervision-0.26.0`. Please pass a list of paths `List[str]` instead.
 
 - The `DetectionDataset.images` property will be removed in `supervision-0.26.0`. Please loop over images with `for path, image, annotation in dataset:`, as that does not require loading all images into memory.
@@ -21,10 +19,15 @@ These features are phased out due to better alternatives or potential issues in
 
 # Removed
 
+### 0.24.0
+
+- The `frame_resolution_wh ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) has been removed.
+- Supervision installation methods `"headless"` and `"desktop"` were removed, as they are no longer needed. `pip install supervision[headless]` will install the base library and harmlessly warn of non-existent extras.
+
 ### 0.23.0
 
 - The `track_buffer`, `track_thresh`, and `match_thresh` parameters in [`ByteTrack`](trackers.md/#supervision.tracker.byte_tracker.core.ByteTrack) are deprecated and were removed as of `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
-- The `triggering_position ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) was removed as of `supervision-0.23.0`. Use `triggering_anchors ` instead.
+- The `triggering_position ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) was removed as of `supervision-0.23.0`. Use `triggering_anchors` instead.
 
 ### 0.22.0
 
diff --git a/docs/detection/annotators.md b/docs/detection/annotators.md
index 09b4d043..e1071121 100644
--- a/docs/detection/annotators.md
+++ b/docs/detection/annotators.md
@@ -5,9 +5,7 @@ status: new
 
 # Annotators
 
-Supervision provides a variety of annotators to annotate detections on images and videos. You can try them out below, with a [Workflow](https://roboflow.com/workflows) that runs [Microsoft's COCO](https://cocodataset.org/#home) dataset through a Instance Segmentation model and annotates the detections using supervision's annotators.
-
-<div style="height: 400px; width: 100%; border-radius: 8px; overflow: hidden;"><iframe src="https://app.roboflow.com/workflows/embed/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ3b3JrZmxvd0lkIjoiNDdtd2xuWW16S25VNWtOYUZjMG8iLCJ3b3Jrc3BhY2VJZCI6ImtyT1RBYm5jRmhvUU1DZExPbGU0IiwidXNlcklkIjoiRVJNUFBZY3FQMmZWWjB1NkRpNXZaYXJDdlZPMiIsImlhdCI6MTcyNjgzOTM2N30.gj2F6SnmmURAScJe4PTC1raUXsAK5mZyrUIGIJ44NhM" loading="lazy" title="Roboflow Workflow for Supervision Annotators" style="width: 100%; height: 100%; min-height: 400px; border: none;"></iframe></div>
+Annotators accept detections and apply box or mask visualizations to the detections. Annotators have many available styles.
 
 === "Box"
 
@@ -485,6 +483,13 @@ Supervision provides a variety of annotators to annotate detections on images an
 
     </div>
 
+<div class="md-typeset">
+    <h2>Try Supervision Annotators on your own image</h2>
+    Visualize annotators on images with COCO classes such as people, vehicles, animals, household items.
+</div>
+
+<div style="height: 400px; width: 100%; border-radius: 8px; overflow: hidden;"><iframe src="https://app.roboflow.com/workflows/embed/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ3b3JrZmxvd0lkIjoiNDdtd2xuWW16S25VNWtOYUZjMG8iLCJ3b3Jrc3BhY2VJZCI6ImtyT1RBYm5jRmhvUU1DZExPbGU0IiwidXNlcklkIjoiRVJNUFBZY3FQMmZWWjB1NkRpNXZaYXJDdlZPMiIsImlhdCI6MTcyNjgzOTM2N30.gj2F6SnmmURAScJe4PTC1raUXsAK5mZyrUIGIJ44NhM?hideToolbar=true&hideHeader=true&defaultVisual=true" loading="lazy" title="Roboflow Workflow for Supervision Annotators" style="width: 100%; height: 100%; min-height: 400px; border: none;"></iframe></div>
+
 <div class="md-typeset">
     <h2><a href="#supervision.annotators.core.BoxAnnotator">BoxAnnotator</a></h2>
 </div>
diff --git a/docs/detection/tools/line_zone.md b/docs/detection/tools/line_zone.md
index 8bca3cfd..014687f9 100644
--- a/docs/detection/tools/line_zone.md
+++ b/docs/detection/tools/line_zone.md
@@ -1,5 +1,6 @@
 ---
 comments: true
+status: new
 ---
 
 <div class="md-typeset">
diff --git a/docs/detection/tools/polygon_zone.md b/docs/detection/tools/polygon_zone.md
index cbe76c20..1d445d9f 100644
--- a/docs/detection/tools/polygon_zone.md
+++ b/docs/detection/tools/polygon_zone.md
@@ -1,5 +1,6 @@
 ---
 comments: true
+status: new
 ---
 
 <div class="md-typeset">
diff --git a/docs/detection/utils.md b/docs/detection/utils.md
index 25c84475..5f1902b7 100644
--- a/docs/detection/utils.md
+++ b/docs/detection/utils.md
@@ -1,5 +1,6 @@
 ---
 comments: true
+status: new
 ---
 
 # Detection Utils
diff --git a/docs/keypoint/annotators.md b/docs/keypoint/annotators.md
index 30a970ec..32f30626 100644
--- a/docs/keypoint/annotators.md
+++ b/docs/keypoint/annotators.md
@@ -1,6 +1,5 @@
 ---
 comments: true
-status: new
 ---
 
 # Annotators
diff --git a/docs/metrics/common_values.md b/docs/metrics/common_values.md
new file mode 100644
index 00000000..b7600f3f
--- /dev/null
+++ b/docs/metrics/common_values.md
@@ -0,0 +1,20 @@
+---
+comments: true
+status: new
+---
+
+# Common Values
+
+This page contains supplementary values, types and enums that metrics use.
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.core.MetricTarget">MetricTarget</a></h2>
+</div>
+
+:::supervision.metrics.core.MetricTarget
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.core.AveragingMethod">AveragingMethod</a></h2>
+</div>
+
+:::supervision.metrics.core.AveragingMethod
diff --git a/docs/metrics/precision.md b/docs/metrics/precision.md
new file mode 100644
index 00000000..c704452e
--- /dev/null
+++ b/docs/metrics/precision.md
@@ -0,0 +1,18 @@
+---
+comments: true
+status: new
+---
+
+# Precision
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.precision.Precision">Precision</a></h2>
+</div>
+
+:::supervision.metrics.precision.Precision
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.precision.PrecisionResult">PrecisionResult</a></h2>
+</div>
+
+:::supervision.metrics.precision.PrecisionResult
diff --git a/docs/metrics/recall.md b/docs/metrics/recall.md
new file mode 100644
index 00000000..78dde833
--- /dev/null
+++ b/docs/metrics/recall.md
@@ -0,0 +1,18 @@
+---
+comments: true
+status: new
+---
+
+# Recall
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.recall.Recall">Recall</a></h2>
+</div>
+
+:::supervision.metrics.recall.Recall
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.recall.RecallResult">RecallResult</a></h2>
+</div>
+
+:::supervision.metrics.recall.RecallResult
diff --git a/docs/notebooks/small-object-detection-with-sahi.ipynb b/docs/notebooks/small-object-detection-with-sahi.ipynb
index 1654ff3c..db69b085 100644
--- a/docs/notebooks/small-object-detection-with-sahi.ipynb
+++ b/docs/notebooks/small-object-detection-with-sahi.ipynb
@@ -15,7 +15,7 @@
                 "\n",
                 "This cookbook shows how to use [Slicing Aided Hyper Inference (SAHI) ](https://arxiv.org/abs/2202.06934) for small object detection with `supervision`.\n",
                 "\n",
-                "![\"Small Object Detection\"](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/animation.gif \"Small Object Detection\")\n",
+                "![\"Small Object Detection\"](https://media.roboflow.com/supervision/cookbooks/sahi/animation.gif \"Small Object Detection\")\n",
                 "\n",
                 "Click the Open in Colab button to run the cookbook on Google Colab.\n",
                 "\n",
@@ -70,7 +70,7 @@
                 "\n",
                 "Detecting people (or their heads) is a common problem that has been addressed by many researchers in the past. In this project, we\u2019ll use an open-source public dataset and a fine-tuned model to perform inference on images.\n",
                 "\n",
-                "![Roboflow Universe](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/roboflow_universe.png \"Open source model for counting people's heads\")\n",
+                "![Roboflow Universe](https://media.roboflow.com/supervision/cookbooks/sahi/roboflow_universe.png \"Open source model for counting people's heads\")\n",
                 "\n",
                 "Some details about the project [\"people_counterv0 Computer Vision Project\"](https://universe.roboflow.com/sit-cx0ng/people_counterv0):\n",
                 "\n",
@@ -782,9 +782,9 @@
                 "\n",
                 "| Example| Observations   |\n",
                 "|----|----|\n",
-                "| ![Overlapping](https://github.com/ediardo/notebooks/blob/main/sahi/overlapping_1.png?raw=true \"Overlapping\") | False Negative, Incomplete bbox  |\n",
-                "| ![Overlapping](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/overlapping_2.png \"Overlapping\")| Double detection, Incomplete bbox|\n",
-                "| ![Overlapping](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/overlapping_3.png \"Overlapping\")| Incomplete bounding box|\n",
+                "| ![Overlapping](https://media.roboflow.com/supervision/cookbooks/sahi/overlapping_1.png \"Overlapping\") | False Negative, Incomplete bbox  |\n",
+                "| ![Overlapping](https://media.roboflow.com/supervision/cookbooks/sahi/overlapping_2.png \"Overlapping\")| Double detection, Incomplete bbox|\n",
+                "| ![Overlapping](https://media.roboflow.com/supervision/cookbooks/sahi/overlapping_3.png \"Overlapping\")| Incomplete bounding box|\n",
                 "\n",
                 "## Improving Object Detection Near Boundaries with Overlapping\n",
                 "\n",
diff --git a/docs/utils/image.md b/docs/utils/image.md
index 93cc5a45..8e39136a 100644
--- a/docs/utils/image.md
+++ b/docs/utils/image.md
@@ -1,6 +1,5 @@
 ---
 comments: true
-status: new
 ---
 
 # Image Utils
diff --git a/docs/utils/video.md b/docs/utils/video.md
index dfae543d..f9a5821d 100644
--- a/docs/utils/video.md
+++ b/docs/utils/video.md
@@ -1,6 +1,5 @@
 ---
 comments: true
-status: new
 ---
 
 # Video Utils
diff --git a/mkdocs.yml b/mkdocs.yml
index 3cd86759..b30dbcfc 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -66,7 +66,10 @@ nav:
           - Utils: datasets/utils.md
       - Metrics:
           - mAP: metrics/mean_average_precision.md
+          - Precision: metrics/precision.md
+          - Recall: metrics/recall.md
           - F1 Score: metrics/f1_score.md
+          - Common Values: metrics/common_values.md
           - Legacy Metrics: detection/metrics.md
       - Utils:
           - Video: utils/video.md
diff --git a/poetry.lock b/poetry.lock
index 81e11ec2..fddfa23f 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,13 +2,13 @@
 
 [[package]]
 name = "anyio"
-version = "4.5.0"
+version = "4.6.2"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "anyio-4.5.0-py3-none-any.whl", hash = "sha256:fdeb095b7cc5a5563175eedd926ec4ae55413bb4be5770c424af0ba46ccb4a78"},
-    {file = "anyio-4.5.0.tar.gz", hash = "sha256:c5a275fe5ca0afd788001f58fca1e69e29ce706d746e317d660e21f70c530ef9"},
+    {file = "anyio-4.6.2-py3-none-any.whl", hash = "sha256:6caec6b1391f6f6d7b2ef2258d2902d36753149f67478f7df4be8e54d03a8f54"},
+    {file = "anyio-4.6.2.tar.gz", hash = "sha256:f72a7bb3dd0752b3bd8b17a844a019d7fbf6ae218c588f4f9ba1b2f600b12347"},
 ]
 
 [package.dependencies]
@@ -19,7 +19,7 @@ typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -259,13 +259,13 @@ css = ["tinycss2 (>=1.1.0,<1.3)"]
 
 [[package]]
 name = "build"
-version = "1.2.2"
+version = "1.2.2.post1"
 description = "A simple, correct Python build frontend"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "build-1.2.2-py3-none-any.whl", hash = "sha256:277ccc71619d98afdd841a0e96ac9fe1593b823af481d3b0cea748e8894e0613"},
-    {file = "build-1.2.2.tar.gz", hash = "sha256:119b2fb462adef986483438377a13b2f42064a2a3a4161f24a0cca698a07ac8c"},
+    {file = "build-1.2.2.post1-py3-none-any.whl", hash = "sha256:1d61c0887fa860c01971625baae8bdd338e517b836a2f70dd1f7aa3a6b2fc5b5"},
+    {file = "build-1.2.2.post1.tar.gz", hash = "sha256:b36993e92ca9375a219c99e606a122ff365a760a2d4bba0caa09bd5278b608b7"},
 ]
 
 [package.dependencies]
@@ -448,101 +448,116 @@ files = [
 
 [[package]]
 name = "charset-normalizer"
-version = "3.3.2"
+version = "3.4.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
 files = [
-    {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03"},
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 [[package]]
@@ -587,6 +602,68 @@ traitlets = ">=4"
 [package.extras]
 test = ["pytest"]
 
+[[package]]
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+[package.dependencies]
+numpy = ">=1.16"
+
+[package.extras]
+bokeh = ["bokeh", "selenium"]
+docs = ["furo", "sphinx-copybutton"]
+mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.2.0)", "types-Pillow"]
+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
+
 [[package]]
 name = "contourpy"
 version = "1.1.1"
@@ -649,10 +726,7 @@ files = [
 ]
 
 [package.dependencies]
-numpy = [
-    {version = ">=1.16,<2.0", markers = "python_version <= \"3.11\""},
-    {version = ">=1.26.0rc1,<2.0", markers = "python_version >= \"3.12\""},
-]
+numpy = {version = ">=1.16,<2.0", markers = "python_version <= \"3.11\""}
 
 [package.extras]
 bokeh = ["bokeh", "selenium"]
@@ -661,6 +735,90 @@ mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.4.1)", "types-Pill
 test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
 test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
 
+[[package]]
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 [[package]]
 name = "cryptography"
 version = "43.0.1"
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 ]
 
 [[package]]
@@ -799,13 +961,13 @@ files = [
 
 [[package]]
 name = "distlib"
-version = "0.3.8"
+version = "0.3.9"
 description = "Distribution utilities"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
@@ -1021,13 +1183,13 @@ test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit",
 
 [[package]]
 name = "griffe"
-version = "1.3.1"
+version = "1.4.0"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1047,13 +1209,13 @@ files = [
 
 [[package]]
 name = "httpcore"
-version = "1.0.5"
+version = "1.0.6"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1064,7 +1226,7 @@ h11 = ">=0.13,<0.15"
 asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
-trio = ["trio (>=0.22.0,<0.26.0)"]
+trio = ["trio (>=0.22.0,<1.0)"]
 
 [[package]]
 name = "httpx"
@@ -1320,21 +1482,25 @@ test = ["portend", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-c
 
 [[package]]
 name = "jaraco-functools"
-version = "4.0.2"
+version = "4.1.0"
 description = "Functools like those found in stdlib"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 more-itertools = "*"
 
 [package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
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+enabler = ["pytest-enabler (>=2.2)"]
+test = ["jaraco.classes", "pytest (>=6,!=8.1.*)"]
+type = ["pytest-mypy"]
 
 [[package]]
 name = "jedi"
@@ -1972,58 +2138,52 @@ files = [
 
 [[package]]
 name = "matplotlib"
-version = "3.7.5"
+version = "3.7.2"
 description = "Python plotting package"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -2032,10 +2192,10 @@ cycler = ">=0.10"
 fonttools = ">=4.22.0"
 importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
 kiwisolver = ">=1.0.1"
-numpy = ">=1.20,<2"
+numpy = ">=1.20"
 packaging = ">=20.0"
 pillow = ">=6.2.0"
-pyparsing = ">=2.3.1"
+pyparsing = ">=2.3.1,<3.1"
 python-dateutil = ">=2.7"
 
 [[package]]
@@ -2195,13 +2355,13 @@ pyyaml = ">=5.1"
 
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 optional = false
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@@ -2211,13 +2371,13 @@ requests = "*"
 
 [[package]]
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 [[package]]
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 [package.dependencies]
@@ -2390,6 +2555,7 @@ typing-extensions = ">=4.6.0"
 
 [package.extras]
 dmypy = ["psutil (>=4.0)"]
+faster-cache = ["orjson"]
 install-types = ["pip"]
 mypyc = ["setuptools (>=50)"]
 reports = ["lxml"]
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 [[package]]
@@ -2673,12 +2856,12 @@ files = [
 
 [package.dependencies]
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@@ -2755,9 +2938,9 @@ files = [
 
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 [[package]]
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+    {file = "scipy-1.14.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:1729560c906963fc8389f6aac023739ff3983e727b1a4d87696b7bf108316a79"},
+    {file = "scipy-1.14.1-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:4079b90df244709e675cdc8b93bfd8a395d59af40b72e339c2287c91860deb8e"},
+    {file = "scipy-1.14.1-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:e0cf28db0f24a38b2a0ca33a85a54852586e43cf6fd876365c86e0657cfe7d73"},
+    {file = "scipy-1.14.1-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:0c2f95de3b04e26f5f3ad5bb05e74ba7f68b837133a4492414b3afd79dfe540e"},
+    {file = "scipy-1.14.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b99722ea48b7ea25e8e015e8341ae74624f72e5f21fc2abd45f3a93266de4c5d"},
+    {file = "scipy-1.14.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5149e3fd2d686e42144a093b206aef01932a0059c2a33ddfa67f5f035bdfe13e"},
+    {file = "scipy-1.14.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e4f5a7c49323533f9103d4dacf4e4f07078f360743dec7f7596949149efeec06"},
+    {file = "scipy-1.14.1-cp313-cp313-win_amd64.whl", hash = "sha256:baff393942b550823bfce952bb62270ee17504d02a1801d7fd0719534dfb9c84"},
+    {file = "scipy-1.14.1.tar.gz", hash = "sha256:5a275584e726026a5699459aa72f828a610821006228e841b94275c4a7c08417"},
+]
+
+[package.dependencies]
+numpy = ">=1.23.5,<2.3"
+
+[package.extras]
+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
+doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.13.1)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<=7.3.7)", "sphinx-design (>=0.4.0)"]
+test = ["Cython", "array-api-strict (>=2.0)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
 [[package]]
 name = "secretstorage"
 version = "3.3.3"
@@ -4110,13 +4347,13 @@ test = ["pytest", "ruff"]
 
 [[package]]
 name = "tomli"
-version = "2.0.1"
+version = "2.0.2"
 description = "A lil' TOML parser"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
-    {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
+    {file = "tomli-2.0.2-py3-none-any.whl", hash = "sha256:2ebe24485c53d303f690b0ec092806a085f07af5a5aa1464f3931eec36caaa38"},
+    {file = "tomli-2.0.2.tar.gz", hash = "sha256:d46d457a85337051c36524bc5349dd91b1877838e2979ac5ced3e710ed8a60ed"},
 ]
 
 [[package]]
@@ -4141,13 +4378,13 @@ files = [
 
 [[package]]
 name = "tox"
-version = "4.21.0"
+version = "4.23.2"
 description = "tox is a generic virtualenv management and test command line tool"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tox-4.21.0-py3-none-any.whl", hash = "sha256:693ac51378255d34ad7aab6dd2ce9ab6a1cf1924eb930183fde850ad503b681d"},
-    {file = "tox-4.21.0.tar.gz", hash = "sha256:e64dd9847ff3a7ec90368be412d7efe61a39caf043222ffbe9ad638ea435f6f6"},
+    {file = "tox-4.23.2-py3-none-any.whl", hash = "sha256:452bc32bb031f2282881a2118923176445bac783ab97c874b8770ab4c3b76c38"},
+    {file = "tox-4.23.2.tar.gz", hash = "sha256:86075e00e555df6e82e74cfc333917f91ecb47ffbc868dcafbd2672e332f4a2c"},
 ]
 
 [package.dependencies]
@@ -4164,18 +4401,17 @@ typing-extensions = {version = ">=4.12.2", markers = "python_version < \"3.11\""
 virtualenv = ">=20.26.6"
 
 [package.extras]
-docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-argparse-cli (>=1.18.2)", "sphinx-autodoc-typehints (>=2.4.4)", "sphinx-copybutton (>=0.5.2)", "sphinx-inline-tabs (>=2023.4.21)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=24.8)"]
-testing = ["build[virtualenv] (>=1.2.2)", "covdefaults (>=2.3)", "detect-test-pollution (>=1.2)", "devpi-process (>=1.0.2)", "diff-cover (>=9.2)", "distlib (>=0.3.8)", "flaky (>=3.8.1)", "hatch-vcs (>=0.4)", "hatchling (>=1.25)", "psutil (>=6)", "pytest (>=8.3.3)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-xdist (>=3.6.1)", "re-assert (>=1.1)", "setuptools (>=75.1)", "time-machine (>=2.15)", "wheel (>=0.44)"]
+test = ["devpi-process (>=1.0.2)", "pytest (>=8.3.3)", "pytest-mock (>=3.14)"]
 
 [[package]]
 name = "tqdm"
-version = "4.66.5"
+version = "4.66.6"
 description = "Fast, Extensible Progress Meter"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.66.5-py3-none-any.whl", hash = "sha256:90279a3770753eafc9194a0364852159802111925aa30eb3f9d85b0e805ac7cd"},
-    {file = "tqdm-4.66.5.tar.gz", hash = "sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad"},
+    {file = "tqdm-4.66.6-py3-none-any.whl", hash = "sha256:223e8b5359c2efc4b30555531f09e9f2f3589bcd7fdd389271191031b49b7a63"},
+    {file = "tqdm-4.66.6.tar.gz", hash = "sha256:4bdd694238bef1485ce839d67967ab50af8f9272aab687c0d7702a01da0be090"},
 ]
 
 [package.dependencies]
@@ -4240,24 +4476,24 @@ types-setuptools = "*"
 
 [[package]]
 name = "types-python-dateutil"
-version = "2.9.0.20240906"
+version = "2.9.0.20241003"
 description = "Typing stubs for python-dateutil"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-python-dateutil-2.9.0.20240906.tar.gz", hash = "sha256:9706c3b68284c25adffc47319ecc7947e5bb86b3773f843c73906fd598bc176e"},
-    {file = "types_python_dateutil-2.9.0.20240906-py3-none-any.whl", hash = "sha256:27c8cc2d058ccb14946eebcaaa503088f4f6dbc4fb6093d3d456a49aef2753f6"},
+    {file = "types-python-dateutil-2.9.0.20241003.tar.gz", hash = "sha256:58cb85449b2a56d6684e41aeefb4c4280631246a0da1a719bdbe6f3fb0317446"},
+    {file = "types_python_dateutil-2.9.0.20241003-py3-none-any.whl", hash = "sha256:250e1d8e80e7bbc3a6c99b907762711d1a1cdd00e978ad39cb5940f6f0a87f3d"},
 ]
 
 [[package]]
 name = "types-pytz"
-version = "2024.2.0.20240913"
+version = "2024.2.0.20241003"
 description = "Typing stubs for pytz"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "types-pytz-2024.2.0.20240913.tar.gz", hash = "sha256:4433b5df4a6fc587bbed41716d86a5ba5d832b4378e506f40d34bc9c81df2c24"},
-    {file = "types_pytz-2024.2.0.20240913-py3-none-any.whl", hash = "sha256:a1eebf57ebc6e127a99d2fa2ba0a88d2b173784ef9b3defcc2004ab6855a44df"},
+    {file = "types-pytz-2024.2.0.20241003.tar.gz", hash = "sha256:575dc38f385a922a212bac00a7d6d2e16e141132a3c955078f4a4fd13ed6cb44"},
+    {file = "types_pytz-2024.2.0.20241003-py3-none-any.whl", hash = "sha256:3e22df1336c0c6ad1d29163c8fda82736909eb977281cb823c57f8bae07118b7"},
 ]
 
 [[package]]
@@ -4273,13 +4509,13 @@ files = [
 
 [[package]]
 name = "types-requests"
-version = "2.32.0.20240914"
+version = "2.32.0.20241016"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-requests-2.32.0.20240914.tar.gz", hash = "sha256:2850e178db3919d9bf809e434eef65ba49d0e7e33ac92d588f4a5e295fffd405"},
-    {file = "types_requests-2.32.0.20240914-py3-none-any.whl", hash = "sha256:59c2f673eb55f32a99b2894faf6020e1a9f4a402ad0f192bfee0b64469054310"},
+    {file = "types-requests-2.32.0.20241016.tar.gz", hash = "sha256:0d9cad2f27515d0e3e3da7134a1b6f28fb97129d86b867f24d9c726452634d95"},
+    {file = "types_requests-2.32.0.20241016-py3-none-any.whl", hash = "sha256:4195d62d6d3e043a4eaaf08ff8a62184584d2e8684e9d2aa178c7915a7da3747"},
 ]
 
 [package.dependencies]
@@ -4287,13 +4523,13 @@ urllib3 = ">=2"
 
 [[package]]
 name = "types-setuptools"
-version = "75.1.0.20240917"
+version = "75.1.0.20241014"
 description = "Typing stubs for setuptools"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-setuptools-75.1.0.20240917.tar.gz", hash = "sha256:12f12a165e7ed383f31def705e5c0fa1c26215dd466b0af34bd042f7d5331f55"},
-    {file = "types_setuptools-75.1.0.20240917-py3-none-any.whl", hash = "sha256:06f78307e68d1bbde6938072c57b81cf8a99bc84bd6dc7e4c5014730b097dc0c"},
+    {file = "types-setuptools-75.1.0.20241014.tar.gz", hash = "sha256:29b0560a8d4b4a91174be085847002c69abfcb048e20b33fc663005aedf56804"},
+    {file = "types_setuptools-75.1.0.20241014-py3-none-any.whl", hash = "sha256:caab58366741fb99673d0138b6e2d760717f154cfb981b74fea5e8de40f0b703"},
 ]
 
 [[package]]
@@ -4545,4 +4781,4 @@ metrics = ["pandas", "pandas-stubs"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "6619a49f1450ccc15a01215d156afbcf248619374ad2ba0576f48434d9b8720f"
+content-hash = "8f7dad5406a294901e3f489cf0d09e8217a80597ba9cd82695822a3fb5c13034"
diff --git a/pyproject.toml b/pyproject.toml
index 9f443b62..713bd838 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.24.0rc1"
+version = "0.24.0"
 description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
 authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
 maintainers = [
@@ -9,7 +9,7 @@ maintainers = [
 ]
 readme = "README.md"
 license = "MIT"
-packages = [{ include = "supervision" }]
+packages = [{ include = "supervision" }, { include = "supervision/py.typed" }]
 homepage = "https://github.com/roboflow/supervision"
 repository = "https://github.com/roboflow/supervision"
 documentation = "https://supervision.roboflow.com/latest/"
@@ -46,17 +46,32 @@ python = "^3.8"
 numpy = [
     { version = ">=1.21.2,<1.23.3", python = "<=3.10" },
     { version = ">=1.23.3", python = ">3.10" },
+    { version = ">=2.1.0", python = ">=3.13" },
 ]
 scipy = [
     { version = "1.10.0", python = "<3.9" },
     { version = "^1.10.0", python = ">=3.9" },
+    { version = ">=1.14.1", python = ">=3.13" },
+
 ]
+
+# Matplotlib sub-dependency
+# The 'contourpy' package is required by Matplotlib for contour plotting.
+# We need to ensure compatibility with both Python 3.8 and Python 3.13.
+#
+# For Python 3.8 and above, we use version 1.0.7 or higher, as it is the lowest major version that supports Python 3.8.
+# For Python 3.13 and above, we use version 1.3.0 or higher, as it is the first version that explicitly supports Python 3.13.
+contourpy = [
+    { version = ">=1.0.7", python = ">=3.8" },
+    { version = ">=1.3.0", python = ">=3.13" },
+]
+
 matplotlib = ">=3.6.0"
 pyyaml = ">=5.3"
 defusedxml = "^0.7.1"
 pillow = ">=9.4"
 requests = { version = ">=2.26.0,<=2.32.3", optional = true }
-tqdm = { version = ">=4.62.3,<=4.66.5", optional = true }
+tqdm = { version = ">=4.62.3,<=4.66.6", optional = true }
 # pandas: picked lowest major version that supports Python 3.8
 pandas = { version = ">=2.0.0", optional = true }
 pandas-stubs = { version = ">=2.0.0.230412", optional = true }
@@ -92,7 +107,7 @@ mike = "^2.0.0"
 # For Documentation Development use Python 3.10 or above
 # Use Latest mkdocs-jupyter min 0.24.6 for Jupyter Notebook Theme support
 mkdocs-jupyter = "^0.24.3"
-mkdocs-git-committers-plugin-2 = "^2.2.3"
+mkdocs-git-committers-plugin-2 = "^2.4.1"
 mkdocs-git-revision-date-localized-plugin = "^1.2.4"
 
 [tool.poetry.group.typecheck]
diff --git a/supervision/dataset/utils.py b/supervision/dataset/utils.py
index 20b80978..6c30eeab 100644
--- a/supervision/dataset/utils.py
+++ b/supervision/dataset/utils.py
@@ -55,7 +55,7 @@ def merge_class_lists(class_lists: List[List[str]]) -> List[str]:
 
     for class_list in class_lists:
         for class_name in class_list:
-            unique_classes.add(class_name.lower())
+            unique_classes.add(class_name)
 
     return sorted(list(unique_classes))
 
diff --git a/supervision/detection/core.py b/supervision/detection/core.py
index 113948fc..32753a30 100644
--- a/supervision/detection/core.py
+++ b/supervision/detection/core.py
@@ -32,8 +32,10 @@ from supervision.detection.utils import (
     extract_ultralytics_masks,
     get_data_item,
     is_data_equal,
+    is_metadata_equal,
     mask_to_xyxy,
     merge_data,
+    merge_metadata,
     process_roboflow_result,
     xywh_to_xyxy,
 )
@@ -125,6 +127,9 @@ class Detections:
         data (Dict[str, Union[np.ndarray, List]]): A dictionary containing additional
             data where each key is a string representing the data type, and the value
             is either a NumPy array or a list of corresponding data.
+        metadata (Dict[str, Any]): A dictionary containing collection-level metadata
+            that applies to the entire set of detections. This may include information such
+            as the video name, camera parameters, timestamp, or other global metadata.
     """  # noqa: E501 // docs
 
     xyxy: np.ndarray
@@ -133,6 +138,7 @@ class Detections:
     class_id: Optional[np.ndarray] = None
     tracker_id: Optional[np.ndarray] = None
     data: Dict[str, Union[np.ndarray, List]] = field(default_factory=dict)
+    metadata: Dict[str, Any] = field(default_factory=dict)
 
     def __post_init__(self):
         validate_detections_fields(
@@ -185,6 +191,7 @@ class Detections:
                 np.array_equal(self.confidence, other.confidence),
                 np.array_equal(self.tracker_id, other.tracker_id),
                 is_data_equal(self.data, other.data),
+                is_metadata_equal(self.metadata, other.metadata),
             ]
         )
 
@@ -985,6 +992,7 @@ class Detections:
         """
         empty_detections = Detections.empty()
         empty_detections.data = self.data
+        empty_detections.metadata = self.metadata
         return self == empty_detections
 
     @classmethod
@@ -1078,6 +1086,9 @@ class Detections:
 
         data = merge_data([d.data for d in detections_list])
 
+        metadata_list = [detections.metadata for detections in detections_list]
+        metadata = merge_metadata(metadata_list)
+
         return cls(
             xyxy=xyxy,
             mask=mask,
@@ -1085,6 +1096,7 @@ class Detections:
             class_id=class_id,
             tracker_id=tracker_id,
             data=data,
+            metadata=metadata,
         )
 
     def get_anchors_coordinates(self, anchor: Position) -> np.ndarray:
@@ -1198,6 +1210,7 @@ class Detections:
             class_id=self.class_id[index] if self.class_id is not None else None,
             tracker_id=self.tracker_id[index] if self.tracker_id is not None else None,
             data=get_data_item(self.data, index),
+            metadata=self.metadata,
         )
 
     def __setitem__(self, key: str, value: Union[np.ndarray, List]):
@@ -1459,6 +1472,8 @@ def merge_inner_detection_object_pair(
     else:
         winning_detection = detections_2
 
+    metadata = merge_metadata([detections_1.metadata, detections_2.metadata])
+
     return Detections(
         xyxy=merged_xyxy,
         mask=merged_mask,
@@ -1466,6 +1481,7 @@ def merge_inner_detection_object_pair(
         class_id=winning_detection.class_id,
         tracker_id=winning_detection.tracker_id,
         data=winning_detection.data,
+        metadata=metadata,
     )
 
 
diff --git a/supervision/detection/line_zone.py b/supervision/detection/line_zone.py
index da69ed45..422bc9c5 100644
--- a/supervision/detection/line_zone.py
+++ b/supervision/detection/line_zone.py
@@ -771,6 +771,19 @@ class LineZoneAnnotatorMulticlass:
         line_zones: List[LineZone],
         line_zone_labels: Optional[List[str]] = None,
     ) -> np.ndarray:
+        """
+        Draws a table with the number of objects of each class that crossed each line.
+
+        Attributes:
+            frame (np.ndarray): The image on which the table will be drawn.
+            line_zones (List[LineZone]): The line zones to be annotated.
+            line_zone_labels (Optional[List[str]]): The labels, one for each
+                line zone. If not provided, the default labels will be used.
+
+        Returns:
+            (np.ndarray): The image with the table drawn on it.
+
+        """
         if line_zone_labels is None:
             line_zone_labels = [f"Line {i + 1}:" for i in range(len(line_zones))]
         if len(line_zones) != len(line_zone_labels):
diff --git a/supervision/detection/tools/polygon_zone.py b/supervision/detection/tools/polygon_zone.py
index af0d1c0c..f69f3c9f 100644
--- a/supervision/detection/tools/polygon_zone.py
+++ b/supervision/detection/tools/polygon_zone.py
@@ -1,6 +1,5 @@
-import warnings
 from dataclasses import replace
-from typing import Iterable, Optional, Tuple
+from typing import Iterable, Optional
 
 import cv2
 import numpy as np
@@ -12,13 +11,18 @@ from supervision.draw.color import Color
 from supervision.draw.utils import draw_filled_polygon, draw_polygon, draw_text
 from supervision.geometry.core import Position
 from supervision.geometry.utils import get_polygon_center
-from supervision.utils.internal import SupervisionWarnings
 
 
 class PolygonZone:
     """
     A class for defining a polygon-shaped zone within a frame for detecting objects.
 
+    !!! warning
+
+        LineZone uses the `tracker_id`. Read
+        [here](/latest/trackers/) to learn how to plug
+        tracking into your inference pipeline.
+
     Attributes:
         polygon (np.ndarray): A polygon represented by a numpy array of shape
             `(N, 2)`, containing the `x`, `y` coordinates of the points.
@@ -28,22 +32,35 @@ class PolygonZone:
             (default: (sv.Position.BOTTOM_CENTER,)).
         current_count (int): The current count of detected objects within the zone
         mask (np.ndarray): The 2D bool mask for the polygon zone
+
+    Example:
+        ```python
+        import supervision as sv
+        from ultralytics import YOLO
+        import numpy as np
+        import cv2
+
+        image = cv2.imread(<SOURCE_IMAGE_PATH>)
+        model = YOLO("yolo11s")
+        tracker = sv.ByteTrack()
+
+        polygon = np.array([[100, 200], [200, 100], [300, 200], [200, 300]])
+        polygon_zone = sv.PolygonZone(polygon=polygon)
+
+        result = model.infer(image)[0]
+        detections = sv.Detections.from_ultralytics(result)
+        detections = tracker.update_with_detections(detections)
+
+        is_detections_in_zone = polygon_zone.trigger(detections)
+        print(polygon_zone.current_count)
+        ```
     """
 
     def __init__(
         self,
         polygon: npt.NDArray[np.int64],
-        frame_resolution_wh: Optional[Tuple[int, int]] = None,
         triggering_anchors: Iterable[Position] = (Position.BOTTOM_CENTER,),
     ):
-        if frame_resolution_wh is not None:
-            warnings.warn(
-                "The `frame_resolution_wh` parameter is no longer required and will be "
-                "dropped in version supervision-0.24.0. The mask resolution is now "
-                "calculated automatically based on the polygon coordinates.",
-                category=SupervisionWarnings,
-            )
-
         self.polygon = polygon.astype(int)
         self.triggering_anchors = triggering_anchors
         if not list(self.triggering_anchors):
@@ -99,7 +116,7 @@ class PolygonZoneAnnotator:
 
     Attributes:
         zone (PolygonZone): The polygon zone to be annotated
-        color (Color): The color to draw the polygon lines
+        color (Color): The color to draw the polygon lines, default is white
         thickness (int): The thickness of the polygon lines, default is 2
         text_color (Color): The color of the text on the polygon, default is black
         text_scale (float): The scale of the text on the polygon, default is 0.5
@@ -115,7 +132,7 @@ class PolygonZoneAnnotator:
     def __init__(
         self,
         zone: PolygonZone,
-        color: Color,
+        color: Color = Color.WHITE,
         thickness: int = 2,
         text_color: Color = Color.BLACK,
         text_scale: float = 0.5,
diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index 43fcec5a..fc4458fa 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -1,5 +1,5 @@
 from itertools import chain
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import cv2
 import numpy as np
@@ -23,10 +23,9 @@ def polygon_to_mask(polygon: np.ndarray, resolution_wh: Tuple[int, int]) -> np.n
         np.ndarray: The generated 2D mask, where the polygon is marked with
             `1`'s and the rest is filled with `0`'s.
     """
-    width, height = resolution_wh
-    mask = np.zeros((height, width))
-
-    cv2.fillPoly(mask, [polygon], color=1)
+    width, height = map(int, resolution_wh)
+    mask = np.zeros((height, width), dtype=np.uint8)
+    cv2.fillPoly(mask, [polygon.astype(np.int32)], color=1)
     return mask
 
 
@@ -163,9 +162,9 @@ def oriented_box_iou_batch(
     boxes_true = boxes_true.reshape(-1, 4, 2)
     boxes_detection = boxes_detection.reshape(-1, 4, 2)
 
-    max_height = max(boxes_true[:, :, 0].max(), boxes_detection[:, :, 0].max()) + 1
+    max_height = int(max(boxes_true[:, :, 0].max(), boxes_detection[:, :, 0].max()) + 1)
     # adding 1 because we are 0-indexed
-    max_width = max(boxes_true[:, :, 1].max(), boxes_detection[:, :, 1].max()) + 1
+    max_width = int(max(boxes_true[:, :, 1].max(), boxes_detection[:, :, 1].max()) + 1)
 
     mask_true = np.zeros((boxes_true.shape[0], max_height, max_width))
     for i, box_true in enumerate(boxes_true):
@@ -808,12 +807,36 @@ def is_data_equal(data_a: Dict[str, np.ndarray], data_b: Dict[str, np.ndarray])
     )
 
 
+def is_metadata_equal(metadata_a: Dict[str, Any], metadata_b: Dict[str, Any]) -> bool:
+    """
+    Compares the metadata payloads of two Detections instances.
+
+    Args:
+        metadata_a, metadata_b: The metadata payloads of the instances.
+
+    Returns:
+        True if the metadata payloads are equal, False otherwise.
+    """
+    return set(metadata_a.keys()) == set(metadata_b.keys()) and all(
+        np.array_equal(metadata_a[key], metadata_b[key])
+        if (
+            isinstance(metadata_a[key], np.ndarray)
+            and isinstance(metadata_b[key], np.ndarray)
+        )
+        else metadata_a[key] == metadata_b[key]
+        for key in metadata_a
+    )
+
+
 def merge_data(
     data_list: List[Dict[str, Union[npt.NDArray[np.generic], List]]],
 ) -> Dict[str, Union[npt.NDArray[np.generic], List]]:
     """
     Merges the data payloads of a list of Detections instances.
 
+    Warning: Assumes that empty detections were filtered-out before passing data to
+    this function.
+
     Args:
         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
@@ -866,6 +889,45 @@ def merge_data(
     return merged_data
 
 
+def merge_metadata(metadata_list: List[Dict[str, Any]]) -> Dict[str, Any]:
+    """
+    Merge metadata from a list of metadata dictionaries.
+
+    This function combines the metadata dictionaries. If a key appears in more than one
+    dictionary, the values must be identical for the merge to succeed.
+
+    Warning: Assumes that empty detections were filtered-out before passing metadata to
+    this function.
+
+    Args:
+        metadata_list (List[Dict[str, Any]]): A list of metadata dictionaries to merge.
+
+    Returns:
+        Dict[str, Any]: A single merged metadata dictionary.
+
+    Raises:
+        ValueError: If there are conflicting values for the same key or if
+        dictionaries have different keys.
+    """
+    if not metadata_list:
+        return {}
+
+    all_keys_sets = [set(metadata.keys()) for metadata in metadata_list]
+    if not all(keys_set == all_keys_sets[0] for keys_set in all_keys_sets):
+        raise ValueError("All metadata dictionaries must have the same keys to merge.")
+
+    merged_metadata: Dict[str, Any] = {}
+    for metadata in metadata_list:
+        for key, value in metadata.items():
+            if key in merged_metadata:
+                if merged_metadata[key] != value:
+                    raise ValueError(f"Conflicting metadata for key: '{key}'.")
+            else:
+                merged_metadata[key] = value
+
+    return merged_metadata
+
+
 def get_data_item(
     data: Dict[str, Union[np.ndarray, List]],
     index: Union[int, slice, List[int], np.ndarray],
diff --git a/supervision/draw/utils.py b/supervision/draw/utils.py
index 19ce4a25..0c3767ff 100644
--- a/supervision/draw/utils.py
+++ b/supervision/draw/utils.py
@@ -9,7 +9,11 @@ from supervision.geometry.core import Point, Rect
 
 
 def draw_line(
-    scene: np.ndarray, start: Point, end: Point, color: Color, thickness: int = 2
+    scene: np.ndarray,
+    start: Point,
+    end: Point,
+    color: Color = Color.ROBOFLOW,
+    thickness: int = 2,
 ) -> np.ndarray:
     """
     Draws a line on a given scene.
@@ -18,7 +22,7 @@ def draw_line(
         scene (np.ndarray): The scene on which the line will be drawn
         start (Point): The starting point of the line
         end (Point): The end point of the line
-        color (Color): The color of the line
+        color (Color): The color of the line, defaults to Color.ROBOFLOW
         thickness (int): The thickness of the line
 
     Returns:
@@ -35,7 +39,7 @@ def draw_line(
 
 
 def draw_rectangle(
-    scene: np.ndarray, rect: Rect, color: Color, thickness: int = 2
+    scene: np.ndarray, rect: Rect, color: Color = Color.ROBOFLOW, thickness: int = 2
 ) -> np.ndarray:
     """
     Draws a rectangle on an image.
@@ -60,7 +64,7 @@ def draw_rectangle(
 
 
 def draw_filled_rectangle(
-    scene: np.ndarray, rect: Rect, color: Color, opacity: float = 1
+    scene: np.ndarray, rect: Rect, color: Color = Color.ROBOFLOW, opacity: float = 1
 ) -> np.ndarray:
     """
     Draws a filled rectangle on an image.
@@ -151,14 +155,17 @@ def draw_rounded_rectangle(
 
 
 def draw_polygon(
-    scene: np.ndarray, polygon: np.ndarray, color: Color, thickness: int = 2
+    scene: np.ndarray,
+    polygon: np.ndarray,
+    color: Color = Color.ROBOFLOW,
+    thickness: int = 2,
 ) -> np.ndarray:
     """Draw a polygon on a scene.
 
     Parameters:
         scene (np.ndarray): The scene to draw the polygon on.
         polygon (np.ndarray): The polygon to be drawn, given as a list of vertices.
-        color (Color): The color of the polygon.
+        color (Color): The color of the polygon. Defaults to Color.ROBOFLOW.
         thickness (int): The thickness of the polygon lines, by default 2.
 
     Returns:
@@ -171,14 +178,17 @@ def draw_polygon(
 
 
 def draw_filled_polygon(
-    scene: np.ndarray, polygon: np.ndarray, color: Color, opacity: float = 1
+    scene: np.ndarray,
+    polygon: np.ndarray,
+    color: Color = Color.ROBOFLOW,
+    opacity: float = 1,
 ) -> np.ndarray:
     """Draw a filled polygon on a scene.
 
     Parameters:
         scene (np.ndarray): The scene to draw the polygon on.
         polygon (np.ndarray): The polygon to be drawn, given as a list of vertices.
-        color (Color): The color of the polygon.
+        color (Color): The color of the polygon. Defaults to Color.ROBOFLOW.
         opacity (float): The opacity of polygon when drawn on the scene.
 
     Returns:
diff --git a/supervision/geometry/utils.py b/supervision/geometry/utils.py
index 8a0ca35c..2247adc5 100644
--- a/supervision/geometry/utils.py
+++ b/supervision/geometry/utils.py
@@ -16,6 +16,9 @@ def get_polygon_center(polygon: np.ndarray) -> Point:
         Point: The center of the polygon, represented as a
             Point object with x and y attributes.
 
+    Raises:
+        ValueError: If the polygon has no vertices.
+
     Examples:
         ```python
         import numpy as np
@@ -30,6 +33,9 @@ def get_polygon_center(polygon: np.ndarray) -> Point:
     # This is one of the 3 candidate algorithms considered for centroid calculation.
     # For a more detailed discussion, see PR #1084 and commit eb33176
 
+    if len(polygon) == 0:
+        raise ValueError("Polygon must have at least one vertex.")
+
     shift_polygon = np.roll(polygon, -1, axis=0)
     signed_areas = np.cross(polygon, shift_polygon) / 2
     if signed_areas.sum() == 0:
diff --git a/supervision/metrics/__init__.py b/supervision/metrics/__init__.py
index 17a6cd48..90fc17b4 100644
--- a/supervision/metrics/__init__.py
+++ b/supervision/metrics/__init__.py
@@ -1,5 +1,4 @@
 from supervision.metrics.core import (
-    CLASS_ID_NONE,
     AveragingMethod,
     Metric,
     MetricTarget,
@@ -9,6 +8,8 @@ from supervision.metrics.mean_average_precision import (
     MeanAveragePrecision,
     MeanAveragePrecisionResult,
 )
+from supervision.metrics.precision import Precision, PrecisionResult
+from supervision.metrics.recall import Recall, RecallResult
 from supervision.metrics.utils.object_size import (
     ObjectSizeCategory,
     get_detection_size_category,
diff --git a/supervision/metrics/core.py b/supervision/metrics/core.py
index 1440fd43..def5999a 100644
--- a/supervision/metrics/core.py
+++ b/supervision/metrics/core.py
@@ -4,9 +4,6 @@ from abc import ABC, abstractmethod
 from enum import Enum
 from typing import Any
 
-CLASS_ID_NONE = -1
-"""Used by metrics module as class ID, when none is present"""
-
 
 class Metric(ABC):
     """
@@ -40,9 +37,10 @@ class MetricTarget(Enum):
     """
     Specifies what type of detection is used to compute the metric.
 
-    * BOXES: xyxy bounding boxes
-    * MASKS: Binary masks
-    * ORIENTED_BOUNDING_BOXES: Oriented bounding boxes (OBB)
+    Attributes:
+        BOXES: xyxy bounding boxes
+        MASKS: Binary masks
+        ORIENTED_BOUNDING_BOXES: Oriented bounding boxes (OBB)
     """
 
     BOXES = "boxes"
@@ -57,15 +55,16 @@ class AveragingMethod(Enum):
     Suppose, before returning the final result, a metric is computed for each class.
     How do you combine those to get the final number?
 
-    * MACRO: Calculate the metric for each class and average the results. The simplest
-        averaging method, but it does not take class imbalance into account.
-    * MICRO: Calculate the metric globally by counting the total true positives, false
-        positives, and false negatives. Micro averaging is useful when you want to give
-        more importance to classes with more samples. It's also more appropriate if you
-        have an imbalance in the number of instances per class.
-    * WEIGHTED: Calculate the metric for each class and average the results, weighted by
-        the number of true instances of each class. Use weighted averaging if you want
-        to take class imbalance into account.
+    Attributes:
+        MACRO: Calculate the metric for each class and average the results. The simplest
+            averaging method, but it does not take class imbalance into account.
+        MICRO: Calculate the metric globally by counting the total true positives, false
+            positives, and false negatives. Micro averaging is useful when you want to
+            give more importance to classes with more samples. It's also more
+            appropriate if you have an imbalance in the number of instances per class.
+        WEIGHTED: Calculate the metric for each class and average the results, weighted
+            by the number of true instances of each class. Use weighted averaging if
+            you want to take class imbalance into account.
     """
 
     MACRO = "macro"
diff --git a/supervision/metrics/f1_score.py b/supervision/metrics/f1_score.py
index 2ca5bca5..cc8c87a2 100644
--- a/supervision/metrics/f1_score.py
+++ b/supervision/metrics/f1_score.py
@@ -9,7 +9,11 @@ from matplotlib import pyplot as plt
 
 from supervision.config import ORIENTED_BOX_COORDINATES
 from supervision.detection.core import Detections
-from supervision.detection.utils import box_iou_batch, mask_iou_batch
+from supervision.detection.utils import (
+    box_iou_batch,
+    mask_iou_batch,
+    oriented_box_iou_batch,
+)
 from supervision.draw.color import LEGACY_COLOR_PALETTE
 from supervision.metrics.core import AveragingMethod, Metric, MetricTarget
 from supervision.metrics.utils.object_size import (
@@ -23,23 +27,55 @@ if TYPE_CHECKING:
 
 
 class F1Score(Metric):
+    """
+    F1 Score is a metric used to evaluate object detection models. It is the harmonic
+    mean of precision and recall, calculated at different IoU thresholds.
+
+    In simple terms, F1 Score is a measure of a model's balance between precision and
+    recall (accuracy and completeness), calculated as:
+
+    `F1 = 2 * (precision * recall) / (precision + recall)`
+
+    Example:
+        ```python
+        import supervision as sv
+        from supervision.metrics import F1Score
+
+        predictions = sv.Detections(...)
+        targets = sv.Detections(...)
+
+        f1_metric = F1Score()
+        f1_result = f1_metric.update(predictions, targets).compute()
+
+        print(f1_result)
+        print(f1_result.f1_50)
+        print(f1_result.small_objects.f1_50)
+        ```
+    """
+
     def __init__(
         self,
         metric_target: MetricTarget = MetricTarget.BOXES,
         averaging_method: AveragingMethod = AveragingMethod.WEIGHTED,
     ):
-        self._metric_target = metric_target
-        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-            raise NotImplementedError(
-                "F1 score is not implemented for oriented bounding boxes."
-            )
+        """
+        Initialize the F1Score metric.
 
+        Args:
+            metric_target (MetricTarget): The type of detection data to use.
+            averaging_method (AveragingMethod): The averaging method used to compute the
+                F1 scores. Determines how the F1 scores are aggregated across classes.
+        """
         self._metric_target = metric_target
         self.averaging_method = averaging_method
+
         self._predictions_list: List[Detections] = []
         self._targets_list: List[Detections] = []
 
     def reset(self) -> None:
+        """
+        Reset the metric to its initial state, clearing all stored data.
+        """
         self._predictions_list = []
         self._targets_list = []
 
@@ -48,6 +84,16 @@ class F1Score(Metric):
         predictions: Union[Detections, List[Detections]],
         targets: Union[Detections, List[Detections]],
     ) -> F1Score:
+        """
+        Add new predictions and targets to the metric, but do not compute the result.
+
+        Args:
+            predictions (Union[Detections, List[Detections]]): The predicted detections.
+            targets (Union[Detections, List[Detections]]): The target detections.
+
+        Returns:
+            (F1Score): The updated metric instance.
+        """
         if not isinstance(predictions, list):
             predictions = [predictions]
         if not isinstance(targets, list):
@@ -65,6 +111,13 @@ class F1Score(Metric):
         return self
 
     def compute(self) -> F1ScoreResult:
+        """
+        Calculate the F1 score metric based on the stored predictions and ground-truth
+        data, at different IoU thresholds.
+
+        Returns:
+            (F1ScoreResult): The F1 score metric result.
+        """
         result = self._compute(self._predictions_list, self._targets_list)
 
         small_predictions, small_targets = self._filter_predictions_and_targets_by_size(
@@ -112,8 +165,12 @@ class F1Score(Metric):
                         iou = box_iou_batch(target_contents, prediction_contents)
                     elif self._metric_target == MetricTarget.MASKS:
                         iou = mask_iou_batch(target_contents, prediction_contents)
+                    elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+                        iou = oriented_box_iou_batch(
+                            target_contents, prediction_contents
+                        )
                     else:
-                        raise NotImplementedError(
+                        raise ValueError(
                             "Unsupported metric target for IoU calculation"
                         )
 
@@ -312,12 +369,22 @@ class F1Score(Metric):
             return (
                 detections.mask
                 if detections.mask is not None
-                else np.empty((0, 0, 0), dtype=bool)
+                else self._make_empty_content()
             )
         if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-            if obb := detections.data.get(ORIENTED_BOX_COORDINATES):
-                return np.ndarray(obb, dtype=np.float32)
-            return np.empty((0, 8), dtype=np.float32)
+            obb = detections.data.get(ORIENTED_BOX_COORDINATES)
+            if obb is not None and len(obb) > 0:
+                return np.array(obb, dtype=np.float32)
+            return self._make_empty_content()
+        raise ValueError(f"Invalid metric target: {self._metric_target}")
+
+    def _make_empty_content(self) -> np.ndarray:
+        if self._metric_target == MetricTarget.BOXES:
+            return np.empty((0, 4), dtype=np.float32)
+        if self._metric_target == MetricTarget.MASKS:
+            return np.empty((0, 0, 0), dtype=bool)
+        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+            return np.empty((0, 4, 2), dtype=np.float32)
         raise ValueError(f"Invalid metric target: {self._metric_target}")
 
     def _filter_detections_by_size(
@@ -373,7 +440,6 @@ class F1ScoreResult:
     The results of the F1 score metric calculation.
 
     Defaults to `0` if no detections or targets were provided.
-    Provides a custom `__str__` method for pretty printing.
 
     Attributes:
         metric_target (MetricTarget): the type of data used for the metric -
diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index 04a5fe9d..ba37837b 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -9,7 +9,11 @@ from matplotlib import pyplot as plt
 
 from supervision.config import ORIENTED_BOX_COORDINATES
 from supervision.detection.core import Detections
-from supervision.detection.utils import box_iou_batch, mask_iou_batch
+from supervision.detection.utils import (
+    box_iou_batch,
+    mask_iou_batch,
+    oriented_box_iou_batch,
+)
 from supervision.draw.color import LEGACY_COLOR_PALETTE
 from supervision.metrics.core import Metric, MetricTarget
 from supervision.metrics.utils.object_size import (
@@ -23,6 +27,27 @@ if TYPE_CHECKING:
 
 
 class MeanAveragePrecision(Metric):
+    """
+    Mean Average Precision (mAP) is a metric used to evaluate object detection models.
+    It is the average of the precision-recall curves at different IoU thresholds.
+
+    Example:
+        ```python
+        import supervision as sv
+        from supervision.metrics import MeanAveragePrecision
+
+        predictions = sv.Detections(...)
+        targets = sv.Detections(...)
+
+        map_metric = MeanAveragePrecision()
+        map_result = map_metric.update(predictions, targets).compute()
+
+        print(map_result)
+        print(map_result.map50_95)
+        map_result.plot()
+        ```
+    """
+
     def __init__(
         self,
         metric_target: MetricTarget = MetricTarget.BOXES,
@@ -36,17 +61,15 @@ class MeanAveragePrecision(Metric):
             class_agnostic (bool): Whether to treat all data as a single class.
         """
         self._metric_target = metric_target
-        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-            raise NotImplementedError(
-                "Mean Average Precision is not implemented for oriented bounding boxes."
-            )
-
         self._class_agnostic = class_agnostic
 
         self._predictions_list: List[Detections] = []
         self._targets_list: List[Detections] = []
 
     def reset(self) -> None:
+        """
+        Reset the metric to its initial state, clearing all stored data.
+        """
         self._predictions_list = []
         self._targets_list = []
 
@@ -76,6 +99,15 @@ class MeanAveragePrecision(Metric):
                 f" targets ({len(targets)}) during the update must be the same."
             )
 
+        if self._class_agnostic:
+            predictions = deepcopy(predictions)
+            targets = deepcopy(targets)
+
+            for prediction in predictions:
+                prediction.class_id[:] = -1
+            for target in targets:
+                target.class_id[:] = -1
+
         self._predictions_list.extend(predictions)
         self._targets_list.extend(targets)
 
@@ -86,26 +118,10 @@ class MeanAveragePrecision(Metric):
     ) -> MeanAveragePrecisionResult:
         """
         Calculate Mean Average Precision based on predicted and ground-truth
-            detections at different thresholds.
+        detections at different thresholds.
 
         Returns:
-            (MeanAveragePrecisionResult): New instance of MeanAveragePrecision.
-
-        Example:
-            ```python
-            import supervision as sv
-            from supervision.metrics import MeanAveragePrecision
-
-            predictions = sv.Detections(...)
-            targets = sv.Detections(...)
-
-            map_metric = MeanAveragePrecision()
-            map_result = map_metric.update(predictions, targets).compute()
-
-            print(map_result)
-            print(map_result.map50_95)
-            map_result.plot()
-            ```
+            (MeanAveragePrecisionResult): The Mean Average Precision result.
         """
         result = self._compute(self._predictions_list, self._targets_list)
 
@@ -172,14 +188,19 @@ class MeanAveragePrecision(Metric):
                         iou = box_iou_batch(target_contents, prediction_contents)
                     elif self._metric_target == MetricTarget.MASKS:
                         iou = mask_iou_batch(target_contents, prediction_contents)
+                    elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+                        iou = oriented_box_iou_batch(
+                            target_contents, prediction_contents
+                        )
                     else:
-                        raise NotImplementedError(
+                        raise ValueError(
                             "Unsupported metric target for IoU calculation"
                         )
 
                     matches = self._match_detection_batch(
                         predictions.class_id, targets.class_id, iou, iou_thresholds
                     )
+
                     stats.append(
                         (
                             matches,
@@ -203,6 +224,7 @@ class MeanAveragePrecision(Metric):
 
         return MeanAveragePrecisionResult(
             metric_target=self._metric_target,
+            is_class_agnostic=self._class_agnostic,
             mAP_scores=mAP_scores,
             iou_thresholds=iou_thresholds,
             matched_classes=unique_classes,
@@ -230,7 +252,7 @@ class MeanAveragePrecision(Metric):
         for r, p in zip(recall[::-1], precision[::-1]):
             precision_levels[recall_levels <= r] = p
 
-        average_precision = (1 / 100 * precision_levels).sum()
+        average_precision = (1 / 101 * precision_levels).sum()
         return average_precision
 
     @staticmethod
@@ -332,8 +354,9 @@ class MeanAveragePrecision(Metric):
                 else self._make_empty_content()
             )
         if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-            if obb := detections.data.get(ORIENTED_BOX_COORDINATES):
-                return np.ndarray(obb, dtype=np.float32)
+            obb = detections.data.get(ORIENTED_BOX_COORDINATES)
+            if obb is not None and len(obb) > 0:
+                return np.array(obb, dtype=np.float32)
             return self._make_empty_content()
         raise ValueError(f"Invalid metric target: {self._metric_target}")
 
@@ -343,7 +366,7 @@ class MeanAveragePrecision(Metric):
         if self._metric_target == MetricTarget.MASKS:
             return np.empty((0, 0, 0), dtype=bool)
         if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
-            return np.empty((0, 8), dtype=np.float32)
+            return np.empty((0, 4, 2), dtype=np.float32)
         raise ValueError(f"Invalid metric target: {self._metric_target}")
 
     def _filter_detections_by_size(
@@ -383,6 +406,8 @@ class MeanAveragePrecisionResult:
     Attributes:
         metric_target (MetricTarget): the type of data used for the metric -
             boxes, masks or oriented bounding boxes.
+        class_agnostic (bool): When computing class-agnostic results, class ID
+            is set to `-1`.
         mAP_map50_95 (float): the mAP score at IoU thresholds from `0.5` to `0.95`.
         mAP_map50 (float): the mAP score at IoU threshold of `0.5`.
         mAP_map75 (float): the mAP score at IoU threshold of `0.75`.
@@ -402,6 +427,7 @@ class MeanAveragePrecisionResult:
     """
 
     metric_target: MetricTarget
+    is_class_agnostic: bool
 
     @property
     def map50_95(self) -> float:
@@ -436,6 +462,7 @@ class MeanAveragePrecisionResult:
         out_str = (
             f"{self.__class__.__name__}:\n"
             f"Metric target: {self.metric_target}\n"
+            f"Class agnostic: {self.is_class_agnostic}\n"
             f"mAP @ 50:95: {self.map50_95:.4f}\n"
             f"mAP @ 50:    {self.map50:.4f}\n"
             f"mAP @ 75:    {self.map75:.4f}\n"
diff --git a/supervision/metrics/precision.py b/supervision/metrics/precision.py
new file mode 100644
index 00000000..fa6cf2b1
--- /dev/null
+++ b/supervision/metrics/precision.py
@@ -0,0 +1,616 @@
+from __future__ import annotations
+
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, List, Optional, Tuple, Union
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+from supervision.config import ORIENTED_BOX_COORDINATES
+from supervision.detection.core import Detections
+from supervision.detection.utils import (
+    box_iou_batch,
+    mask_iou_batch,
+    oriented_box_iou_batch,
+)
+from supervision.draw.color import LEGACY_COLOR_PALETTE
+from supervision.metrics.core import AveragingMethod, Metric, MetricTarget
+from supervision.metrics.utils.object_size import (
+    ObjectSizeCategory,
+    get_detection_size_category,
+)
+from supervision.metrics.utils.utils import ensure_pandas_installed
+
+if TYPE_CHECKING:
+    import pandas as pd
+
+
+class Precision(Metric):
+    """
+    Precision is a metric used to evaluate object detection models. It is the ratio of
+    true positive detections to the total number of predicted detections. We calculate
+    it at different IoU thresholds.
+
+    In simple terms, Precision is a measure of a model's accuracy, calculated as:
+
+    `Precision = TP / (TP + FP)`
+
+    Here, `TP` is the number of true positives (correct detections), and `FP` is the
+    number of false positive detections (detected, but incorrectly).
+
+    Example:
+        ```python
+        import supervision as sv
+        from supervision.metrics import Precision
+
+        predictions = sv.Detections(...)
+        targets = sv.Detections(...)
+
+        precision_metric = Precision()
+        precision_result = precision_metric.update(predictions, targets).compute()
+
+        print(precision_result)
+        print(precision_result.precision_at_50)
+        print(precision_result.small_objects.precision_at_50)
+        ```
+    """
+
+    def __init__(
+        self,
+        metric_target: MetricTarget = MetricTarget.BOXES,
+        averaging_method: AveragingMethod = AveragingMethod.WEIGHTED,
+    ):
+        """
+        Initialize the Precision metric.
+
+        Args:
+            metric_target (MetricTarget): The type of detection data to use.
+            averaging_method (AveragingMethod): The averaging method used to compute the
+                precision. Determines how the precision is aggregated across classes.
+        """
+        self._metric_target = metric_target
+        self.averaging_method = averaging_method
+
+        self._predictions_list: List[Detections] = []
+        self._targets_list: List[Detections] = []
+
+    def reset(self) -> None:
+        """
+        Reset the metric to its initial state, clearing all stored data.
+        """
+        self._predictions_list = []
+        self._targets_list = []
+
+    def update(
+        self,
+        predictions: Union[Detections, List[Detections]],
+        targets: Union[Detections, List[Detections]],
+    ) -> Precision:
+        """
+        Add new predictions and targets to the metric, but do not compute the result.
+
+        Args:
+            predictions (Union[Detections, List[Detections]]): The predicted detections.
+            targets (Union[Detections, List[Detections]]): The target detections.
+
+        Returns:
+            (Precision): The updated metric instance.
+        """
+        if not isinstance(predictions, list):
+            predictions = [predictions]
+        if not isinstance(targets, list):
+            targets = [targets]
+
+        if len(predictions) != len(targets):
+            raise ValueError(
+                f"The number of predictions ({len(predictions)}) and"
+                f" targets ({len(targets)}) during the update must be the same."
+            )
+
+        self._predictions_list.extend(predictions)
+        self._targets_list.extend(targets)
+
+        return self
+
+    def compute(self) -> PrecisionResult:
+        """
+        Calculate the precision metric based on the stored predictions and ground-truth
+        data, at different IoU thresholds.
+
+        Returns:
+            (PrecisionResult): The precision metric result.
+        """
+        result = self._compute(self._predictions_list, self._targets_list)
+
+        small_predictions, small_targets = self._filter_predictions_and_targets_by_size(
+            self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL
+        )
+        result.small_objects = self._compute(small_predictions, small_targets)
+
+        medium_predictions, medium_targets = (
+            self._filter_predictions_and_targets_by_size(
+                self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM
+            )
+        )
+        result.medium_objects = self._compute(medium_predictions, medium_targets)
+
+        large_predictions, large_targets = self._filter_predictions_and_targets_by_size(
+            self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE
+        )
+        result.large_objects = self._compute(large_predictions, large_targets)
+
+        return result
+
+    def _compute(
+        self, predictions_list: List[Detections], targets_list: List[Detections]
+    ) -> PrecisionResult:
+        iou_thresholds = np.linspace(0.5, 0.95, 10)
+        stats = []
+
+        for predictions, targets in zip(predictions_list, targets_list):
+            prediction_contents = self._detections_content(predictions)
+            target_contents = self._detections_content(targets)
+
+            if len(targets) > 0:
+                if len(predictions) == 0:
+                    stats.append(
+                        (
+                            np.zeros((0, iou_thresholds.size), dtype=bool),
+                            np.zeros((0,), dtype=np.float32),
+                            np.zeros((0,), dtype=int),
+                            targets.class_id,
+                        )
+                    )
+
+                else:
+                    if self._metric_target == MetricTarget.BOXES:
+                        iou = box_iou_batch(target_contents, prediction_contents)
+                    elif self._metric_target == MetricTarget.MASKS:
+                        iou = mask_iou_batch(target_contents, prediction_contents)
+                    elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+                        iou = oriented_box_iou_batch(
+                            target_contents, prediction_contents
+                        )
+                    else:
+                        raise ValueError(
+                            "Unsupported metric target for IoU calculation"
+                        )
+
+                    matches = self._match_detection_batch(
+                        predictions.class_id, targets.class_id, iou, iou_thresholds
+                    )
+                    stats.append(
+                        (
+                            matches,
+                            predictions.confidence,
+                            predictions.class_id,
+                            targets.class_id,
+                        )
+                    )
+
+        if not stats:
+            return PrecisionResult(
+                metric_target=self._metric_target,
+                averaging_method=self.averaging_method,
+                precision_scores=np.zeros(iou_thresholds.shape[0]),
+                precision_per_class=np.zeros((0, iou_thresholds.shape[0])),
+                iou_thresholds=iou_thresholds,
+                matched_classes=np.array([], dtype=int),
+                small_objects=None,
+                medium_objects=None,
+                large_objects=None,
+            )
+
+        concatenated_stats = [np.concatenate(items, 0) for items in zip(*stats)]
+        precision_scores, precision_per_class, unique_classes = (
+            self._compute_precision_for_classes(*concatenated_stats)
+        )
+
+        return PrecisionResult(
+            metric_target=self._metric_target,
+            averaging_method=self.averaging_method,
+            precision_scores=precision_scores,
+            precision_per_class=precision_per_class,
+            iou_thresholds=iou_thresholds,
+            matched_classes=unique_classes,
+            small_objects=None,
+            medium_objects=None,
+            large_objects=None,
+        )
+
+    def _compute_precision_for_classes(
+        self,
+        matches: np.ndarray,
+        prediction_confidence: np.ndarray,
+        prediction_class_ids: np.ndarray,
+        true_class_ids: np.ndarray,
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        sorted_indices = np.argsort(-prediction_confidence)
+        matches = matches[sorted_indices]
+        prediction_class_ids = prediction_class_ids[sorted_indices]
+        unique_classes, class_counts = np.unique(true_class_ids, return_counts=True)
+
+        # Shape: PxTh,P,C,C -> CxThx3
+        confusion_matrix = self._compute_confusion_matrix(
+            matches, prediction_class_ids, unique_classes, class_counts
+        )
+
+        # Shape: CxThx3 -> CxTh
+        precision_per_class = self._compute_precision(confusion_matrix)
+
+        # Shape: CxTh -> Th
+        if self.averaging_method == AveragingMethod.MACRO:
+            precision_scores = np.mean(precision_per_class, axis=0)
+        elif self.averaging_method == AveragingMethod.MICRO:
+            confusion_matrix_merged = confusion_matrix.sum(0)
+            precision_scores = self._compute_precision(confusion_matrix_merged)
+        elif self.averaging_method == AveragingMethod.WEIGHTED:
+            class_counts = class_counts.astype(np.float32)
+            precision_scores = np.average(
+                precision_per_class, axis=0, weights=class_counts
+            )
+
+        return precision_scores, precision_per_class, unique_classes
+
+    @staticmethod
+    def _match_detection_batch(
+        predictions_classes: np.ndarray,
+        target_classes: np.ndarray,
+        iou: np.ndarray,
+        iou_thresholds: np.ndarray,
+    ) -> np.ndarray:
+        num_predictions, num_iou_levels = (
+            predictions_classes.shape[0],
+            iou_thresholds.shape[0],
+        )
+        correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)
+        correct_class = target_classes[:, None] == predictions_classes
+
+        for i, iou_level in enumerate(iou_thresholds):
+            matched_indices = np.where((iou >= iou_level) & correct_class)
+
+            if matched_indices[0].shape[0]:
+                combined_indices = np.stack(matched_indices, axis=1)
+                iou_values = iou[matched_indices][:, None]
+                matches = np.hstack([combined_indices, iou_values])
+
+                if matched_indices[0].shape[0] > 1:
+                    matches = matches[matches[:, 2].argsort()[::-1]]
+                    matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+                    matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+
+                correct[matches[:, 1].astype(int), i] = True
+
+        return correct
+
+    @staticmethod
+    def _compute_confusion_matrix(
+        sorted_matches: np.ndarray,
+        sorted_prediction_class_ids: np.ndarray,
+        unique_classes: np.ndarray,
+        class_counts: np.ndarray,
+    ) -> np.ndarray:
+        """
+        Compute the confusion matrix for each class and IoU threshold.
+
+        Assumes the matches and prediction_class_ids are sorted by confidence
+        in descending order.
+
+        Arguments:
+            sorted_matches: np.ndarray, bool, shape (P, Th), that is True
+                if the prediction is a true positive at the given IoU threshold.
+            sorted_prediction_class_ids: np.ndarray, int, shape (P,), containing
+                the class id for each prediction.
+            unique_classes: np.ndarray, int, shape (C,), containing the unique
+                class ids.
+            class_counts: np.ndarray, int, shape (C,), containing the number
+                of true instances for each class.
+
+        Returns:
+            np.ndarray, shape (C, Th, 3), containing the true positives, false
+                positives, and false negatives for each class and IoU threshold.
+        """
+
+        num_thresholds = sorted_matches.shape[1]
+        num_classes = unique_classes.shape[0]
+
+        confusion_matrix = np.zeros((num_classes, num_thresholds, 3))
+        for class_idx, class_id in enumerate(unique_classes):
+            is_class = sorted_prediction_class_ids == class_id
+            num_true = class_counts[class_idx]
+            num_predictions = is_class.sum()
+
+            if num_predictions == 0:
+                true_positives = np.zeros(num_thresholds)
+                false_positives = np.zeros(num_thresholds)
+                false_negatives = np.full(num_thresholds, num_true)
+            elif num_true == 0:
+                true_positives = np.zeros(num_thresholds)
+                false_positives = np.full(num_thresholds, num_predictions)
+                false_negatives = np.zeros(num_thresholds)
+            else:
+                true_positives = sorted_matches[is_class].sum(0)
+                false_positives = (1 - sorted_matches[is_class]).sum(0)
+                false_negatives = num_true - true_positives
+            confusion_matrix[class_idx] = np.stack(
+                [true_positives, false_positives, false_negatives], axis=1
+            )
+
+        return confusion_matrix
+
+    @staticmethod
+    def _compute_precision(confusion_matrix: np.ndarray) -> np.ndarray:
+        """
+        Broadcastable function, computing the precision from the confusion matrix.
+
+        Arguments:
+            confusion_matrix: np.ndarray, shape (N, ..., 3), where the last dimension
+                contains the true positives, false positives, and false negatives.
+
+        Returns:
+            np.ndarray, shape (N, ...), containing the precision for each element.
+        """
+        if not confusion_matrix.shape[-1] == 3:
+            raise ValueError(
+                f"Confusion matrix must have shape (..., 3), got "
+                f"{confusion_matrix.shape}"
+            )
+        true_positives = confusion_matrix[..., 0]
+        false_positives = confusion_matrix[..., 1]
+
+        denominator = true_positives + false_positives
+        precision = np.where(denominator == 0, 0, true_positives / denominator)
+
+        return precision
+
+    def _detections_content(self, detections: Detections) -> np.ndarray:
+        """Return boxes, masks or oriented bounding boxes from detections."""
+        if self._metric_target == MetricTarget.BOXES:
+            return detections.xyxy
+        if self._metric_target == MetricTarget.MASKS:
+            return (
+                detections.mask
+                if detections.mask is not None
+                else self._make_empty_content()
+            )
+        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+            obb = detections.data.get(ORIENTED_BOX_COORDINATES)
+            if obb is not None and len(obb) > 0:
+                return np.array(obb, dtype=np.float32)
+            return self._make_empty_content()
+        raise ValueError(f"Invalid metric target: {self._metric_target}")
+
+    def _make_empty_content(self) -> np.ndarray:
+        if self._metric_target == MetricTarget.BOXES:
+            return np.empty((0, 4), dtype=np.float32)
+        if self._metric_target == MetricTarget.MASKS:
+            return np.empty((0, 0, 0), dtype=bool)
+        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+            return np.empty((0, 4, 2), dtype=np.float32)
+        raise ValueError(f"Invalid metric target: {self._metric_target}")
+
+    def _filter_detections_by_size(
+        self, detections: Detections, size_category: ObjectSizeCategory
+    ) -> Detections:
+        """Return a copy of detections with contents filtered by object size."""
+        new_detections = deepcopy(detections)
+        if detections.is_empty() or size_category == ObjectSizeCategory.ANY:
+            return new_detections
+
+        sizes = get_detection_size_category(new_detections, self._metric_target)
+        size_mask = sizes == size_category.value
+
+        new_detections.xyxy = new_detections.xyxy[size_mask]
+        if new_detections.mask is not None:
+            new_detections.mask = new_detections.mask[size_mask]
+        if new_detections.class_id is not None:
+            new_detections.class_id = new_detections.class_id[size_mask]
+        if new_detections.confidence is not None:
+            new_detections.confidence = new_detections.confidence[size_mask]
+        if new_detections.tracker_id is not None:
+            new_detections.tracker_id = new_detections.tracker_id[size_mask]
+        if new_detections.data is not None:
+            for key, value in new_detections.data.items():
+                new_detections.data[key] = np.array(value)[size_mask]
+
+        return new_detections
+
+    def _filter_predictions_and_targets_by_size(
+        self,
+        predictions_list: List[Detections],
+        targets_list: List[Detections],
+        size_category: ObjectSizeCategory,
+    ) -> Tuple[List[Detections], List[Detections]]:
+        """
+        Filter predictions and targets by object size category.
+        """
+        new_predictions_list = []
+        new_targets_list = []
+        for predictions, targets in zip(predictions_list, targets_list):
+            new_predictions_list.append(
+                self._filter_detections_by_size(predictions, size_category)
+            )
+            new_targets_list.append(
+                self._filter_detections_by_size(targets, size_category)
+            )
+        return new_predictions_list, new_targets_list
+
+
+@dataclass
+class PrecisionResult:
+    """
+    The results of the precision metric calculation.
+
+    Defaults to `0` if no detections or targets were provided.
+
+    Attributes:
+        metric_target (MetricTarget): the type of data used for the metric -
+            boxes, masks or oriented bounding boxes.
+        averaging_method (AveragingMethod): the averaging method used to compute the
+            precision. Determines how the precision is aggregated across classes.
+        precision_at_50 (float): the precision at IoU threshold of `0.5`.
+        precision_at_75 (float): the precision at IoU threshold of `0.75`.
+        precision_scores (np.ndarray): the precision scores at each IoU threshold.
+            Shape: `(num_iou_thresholds,)`
+        precision_per_class (np.ndarray): the precision scores per class and
+            IoU threshold. Shape: `(num_target_classes, num_iou_thresholds)`
+        iou_thresholds (np.ndarray): the IoU thresholds used in the calculations.
+        matched_classes (np.ndarray): the class IDs of all matched classes.
+            Corresponds to the rows of `precision_per_class`.
+        small_objects (Optional[PrecisionResult]): the Precision metric results
+            for small objects.
+        medium_objects (Optional[PrecisionResult]): the Precision metric results
+            for medium objects.
+        large_objects (Optional[PrecisionResult]): the Precision metric results
+            for large objects.
+    """
+
+    metric_target: MetricTarget
+    averaging_method: AveragingMethod
+
+    @property
+    def precision_at_50(self) -> float:
+        return self.precision_scores[0]
+
+    @property
+    def precision_at_75(self) -> float:
+        return self.precision_scores[5]
+
+    precision_scores: np.ndarray
+    precision_per_class: np.ndarray
+    iou_thresholds: np.ndarray
+    matched_classes: np.ndarray
+
+    small_objects: Optional[PrecisionResult]
+    medium_objects: Optional[PrecisionResult]
+    large_objects: Optional[PrecisionResult]
+
+    def __str__(self) -> str:
+        """
+        Format as a pretty string.
+
+        Example:
+            ```python
+            print(precision_result)
+            ```
+        """
+        out_str = (
+            f"{self.__class__.__name__}:\n"
+            f"Metric target:    {self.metric_target}\n"
+            f"Averaging method: {self.averaging_method}\n"
+            f"P @ 50:     {self.precision_at_50:.4f}\n"
+            f"P @ 75:     {self.precision_at_75:.4f}\n"
+            f"P @ thresh: {self.precision_scores}\n"
+            f"IoU thresh: {self.iou_thresholds}\n"
+            f"Precision per class:\n"
+        )
+        if self.precision_per_class.size == 0:
+            out_str += "  No results\n"
+        for class_id, precision_of_class in zip(
+            self.matched_classes, self.precision_per_class
+        ):
+            out_str += f"  {class_id}: {precision_of_class}\n"
+
+        indent = "  "
+        if self.small_objects is not None:
+            indented = indent + str(self.small_objects).replace("\n", f"\n{indent}")
+            out_str += f"\nSmall objects:\n{indented}"
+        if self.medium_objects is not None:
+            indented = indent + str(self.medium_objects).replace("\n", f"\n{indent}")
+            out_str += f"\nMedium objects:\n{indented}"
+        if self.large_objects is not None:
+            indented = indent + str(self.large_objects).replace("\n", f"\n{indent}")
+            out_str += f"\nLarge objects:\n{indented}"
+
+        return out_str
+
+    def to_pandas(self) -> "pd.DataFrame":
+        """
+        Convert the result to a pandas DataFrame.
+
+        Returns:
+            (pd.DataFrame): The result as a DataFrame.
+        """
+        ensure_pandas_installed()
+        import pandas as pd
+
+        pandas_data = {
+            "P@50": self.precision_at_50,
+            "P@75": self.precision_at_75,
+        }
+
+        if self.small_objects is not None:
+            small_objects_df = self.small_objects.to_pandas()
+            for key, value in small_objects_df.items():
+                pandas_data[f"small_objects_{key}"] = value
+        if self.medium_objects is not None:
+            medium_objects_df = self.medium_objects.to_pandas()
+            for key, value in medium_objects_df.items():
+                pandas_data[f"medium_objects_{key}"] = value
+        if self.large_objects is not None:
+            large_objects_df = self.large_objects.to_pandas()
+            for key, value in large_objects_df.items():
+                pandas_data[f"large_objects_{key}"] = value
+
+        return pd.DataFrame(pandas_data, index=[0])
+
+    def plot(self):
+        """
+        Plot the precision results.
+        """
+
+        labels = ["Precision@50", "Precision@75"]
+        values = [self.precision_at_50, self.precision_at_75]
+        colors = [LEGACY_COLOR_PALETTE[0]] * 2
+
+        if self.small_objects is not None:
+            small_objects = self.small_objects
+            labels += ["Small: P@50", "Small: P@75"]
+            values += [small_objects.precision_at_50, small_objects.precision_at_75]
+            colors += [LEGACY_COLOR_PALETTE[3]] * 2
+
+        if self.medium_objects is not None:
+            medium_objects = self.medium_objects
+            labels += ["Medium: P@50", "Medium: P@75"]
+            values += [medium_objects.precision_at_50, medium_objects.precision_at_75]
+            colors += [LEGACY_COLOR_PALETTE[2]] * 2
+
+        if self.large_objects is not None:
+            large_objects = self.large_objects
+            labels += ["Large: P@50", "Large: P@75"]
+            values += [large_objects.precision_at_50, large_objects.precision_at_75]
+            colors += [LEGACY_COLOR_PALETTE[4]] * 2
+
+        plt.rcParams["font.family"] = "monospace"
+
+        _, ax = plt.subplots(figsize=(10, 6))
+        ax.set_ylim(0, 1)
+        ax.set_ylabel("Value", fontweight="bold")
+        title = (
+            f"Precision, by Object Size"
+            f"\n(target: {self.metric_target.value},"
+            f" averaging: {self.averaging_method.value})"
+        )
+        ax.set_title(title, fontweight="bold")
+
+        x_positions = range(len(labels))
+        bars = ax.bar(x_positions, values, color=colors, align="center")
+
+        ax.set_xticks(x_positions)
+        ax.set_xticklabels(labels, rotation=45, ha="right")
+
+        for bar in bars:
+            y_value = bar.get_height()
+            ax.text(
+                bar.get_x() + bar.get_width() / 2,
+                y_value + 0.02,
+                f"{y_value:.2f}",
+                ha="center",
+                va="bottom",
+            )
+
+        plt.rcParams["font.family"] = "sans-serif"
+
+        plt.tight_layout()
+        plt.show()
diff --git a/supervision/metrics/recall.py b/supervision/metrics/recall.py
new file mode 100644
index 00000000..1848502b
--- /dev/null
+++ b/supervision/metrics/recall.py
@@ -0,0 +1,614 @@
+from __future__ import annotations
+
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, List, Optional, Tuple, Union
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+from supervision.config import ORIENTED_BOX_COORDINATES
+from supervision.detection.core import Detections
+from supervision.detection.utils import (
+    box_iou_batch,
+    mask_iou_batch,
+    oriented_box_iou_batch,
+)
+from supervision.draw.color import LEGACY_COLOR_PALETTE
+from supervision.metrics.core import AveragingMethod, Metric, MetricTarget
+from supervision.metrics.utils.object_size import (
+    ObjectSizeCategory,
+    get_detection_size_category,
+)
+from supervision.metrics.utils.utils import ensure_pandas_installed
+
+if TYPE_CHECKING:
+    import pandas as pd
+
+
+class Recall(Metric):
+    """
+    Recall is a metric used to evaluate object detection models. It is the ratio of
+    true positive detections to the total number of ground truth instances. We calculate
+    it at different IoU thresholds.
+
+    In simple terms, Recall is a measure of a model's completeness, calculated as:
+
+    `Recall = TP / (TP + FN)`
+
+    Here, `TP` is the number of true positives (correct detections), and `FN` is the
+    number of false negatives (missed detections).
+
+    Example:
+        ```python
+        import supervision as sv
+        from supervision.metrics import Recall
+
+        predictions = sv.Detections(...)
+        targets = sv.Detections(...)
+
+        recall_metric = Recall()
+        recall_result = recall_metric.update(predictions, targets).compute()
+
+        print(recall_result)
+        print(recall_result.recall_at_50)
+        print(recall_result.small_objects.recall_at_50)
+        ```
+    """
+
+    def __init__(
+        self,
+        metric_target: MetricTarget = MetricTarget.BOXES,
+        averaging_method: AveragingMethod = AveragingMethod.WEIGHTED,
+    ):
+        """
+        Initialize the Recall metric.
+
+        Args:
+            metric_target (MetricTarget): The type of detection data to use.
+            averaging_method (AveragingMethod): The averaging method used to compute the
+                recall. Determines how the recall is aggregated across classes.
+        """
+        self._metric_target = metric_target
+        self.averaging_method = averaging_method
+
+        self._predictions_list: List[Detections] = []
+        self._targets_list: List[Detections] = []
+
+    def reset(self) -> None:
+        """
+        Reset the metric to its initial state, clearing all stored data.
+        """
+        self._predictions_list = []
+        self._targets_list = []
+
+    def update(
+        self,
+        predictions: Union[Detections, List[Detections]],
+        targets: Union[Detections, List[Detections]],
+    ) -> Recall:
+        """
+        Add new predictions and targets to the metric, but do not compute the result.
+
+        Args:
+            predictions (Union[Detections, List[Detections]]): The predicted detections.
+            targets (Union[Detections, List[Detections]]): The target detections.
+
+        Returns:
+            (Recall): The updated metric instance.
+        """
+        if not isinstance(predictions, list):
+            predictions = [predictions]
+        if not isinstance(targets, list):
+            targets = [targets]
+
+        if len(predictions) != len(targets):
+            raise ValueError(
+                f"The number of predictions ({len(predictions)}) and"
+                f" targets ({len(targets)}) during the update must be the same."
+            )
+
+        self._predictions_list.extend(predictions)
+        self._targets_list.extend(targets)
+
+        return self
+
+    def compute(self) -> RecallResult:
+        """
+        Calculate the precision metric based on the stored predictions and ground-truth
+        data, at different IoU thresholds.
+
+        Returns:
+            (RecallResult): The precision metric result.
+        """
+        result = self._compute(self._predictions_list, self._targets_list)
+
+        small_predictions, small_targets = self._filter_predictions_and_targets_by_size(
+            self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL
+        )
+        result.small_objects = self._compute(small_predictions, small_targets)
+
+        medium_predictions, medium_targets = (
+            self._filter_predictions_and_targets_by_size(
+                self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM
+            )
+        )
+        result.medium_objects = self._compute(medium_predictions, medium_targets)
+
+        large_predictions, large_targets = self._filter_predictions_and_targets_by_size(
+            self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE
+        )
+        result.large_objects = self._compute(large_predictions, large_targets)
+
+        return result
+
+    def _compute(
+        self, predictions_list: List[Detections], targets_list: List[Detections]
+    ) -> RecallResult:
+        iou_thresholds = np.linspace(0.5, 0.95, 10)
+        stats = []
+
+        for predictions, targets in zip(predictions_list, targets_list):
+            prediction_contents = self._detections_content(predictions)
+            target_contents = self._detections_content(targets)
+
+            if len(targets) > 0:
+                if len(predictions) == 0:
+                    stats.append(
+                        (
+                            np.zeros((0, iou_thresholds.size), dtype=bool),
+                            np.zeros((0,), dtype=np.float32),
+                            np.zeros((0,), dtype=int),
+                            targets.class_id,
+                        )
+                    )
+
+                else:
+                    if self._metric_target == MetricTarget.BOXES:
+                        iou = box_iou_batch(target_contents, prediction_contents)
+                    elif self._metric_target == MetricTarget.MASKS:
+                        iou = mask_iou_batch(target_contents, prediction_contents)
+                    elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+                        iou = oriented_box_iou_batch(
+                            target_contents, prediction_contents
+                        )
+                    else:
+                        raise ValueError(
+                            "Unsupported metric target for IoU calculation"
+                        )
+
+                    matches = self._match_detection_batch(
+                        predictions.class_id, targets.class_id, iou, iou_thresholds
+                    )
+                    stats.append(
+                        (
+                            matches,
+                            predictions.confidence,
+                            predictions.class_id,
+                            targets.class_id,
+                        )
+                    )
+
+        if not stats:
+            return RecallResult(
+                metric_target=self._metric_target,
+                averaging_method=self.averaging_method,
+                recall_scores=np.zeros(iou_thresholds.shape[0]),
+                recall_per_class=np.zeros((0, iou_thresholds.shape[0])),
+                iou_thresholds=iou_thresholds,
+                matched_classes=np.array([], dtype=int),
+                small_objects=None,
+                medium_objects=None,
+                large_objects=None,
+            )
+
+        concatenated_stats = [np.concatenate(items, 0) for items in zip(*stats)]
+        recall_scores, recall_per_class, unique_classes = (
+            self._compute_recall_for_classes(*concatenated_stats)
+        )
+
+        return RecallResult(
+            metric_target=self._metric_target,
+            averaging_method=self.averaging_method,
+            recall_scores=recall_scores,
+            recall_per_class=recall_per_class,
+            iou_thresholds=iou_thresholds,
+            matched_classes=unique_classes,
+            small_objects=None,
+            medium_objects=None,
+            large_objects=None,
+        )
+
+    def _compute_recall_for_classes(
+        self,
+        matches: np.ndarray,
+        prediction_confidence: np.ndarray,
+        prediction_class_ids: np.ndarray,
+        true_class_ids: np.ndarray,
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        sorted_indices = np.argsort(-prediction_confidence)
+        matches = matches[sorted_indices]
+        prediction_class_ids = prediction_class_ids[sorted_indices]
+        unique_classes, class_counts = np.unique(true_class_ids, return_counts=True)
+
+        # Shape: PxTh,P,C,C -> CxThx3
+        confusion_matrix = self._compute_confusion_matrix(
+            matches, prediction_class_ids, unique_classes, class_counts
+        )
+
+        # Shape: CxThx3 -> CxTh
+        recall_per_class = self._compute_recall(confusion_matrix)
+
+        # Shape: CxTh -> Th
+        if self.averaging_method == AveragingMethod.MACRO:
+            recall_scores = np.mean(recall_per_class, axis=0)
+        elif self.averaging_method == AveragingMethod.MICRO:
+            confusion_matrix_merged = confusion_matrix.sum(0)
+            recall_scores = self._compute_recall(confusion_matrix_merged)
+        elif self.averaging_method == AveragingMethod.WEIGHTED:
+            class_counts = class_counts.astype(np.float32)
+            recall_scores = np.average(recall_per_class, axis=0, weights=class_counts)
+
+        return recall_scores, recall_per_class, unique_classes
+
+    @staticmethod
+    def _match_detection_batch(
+        predictions_classes: np.ndarray,
+        target_classes: np.ndarray,
+        iou: np.ndarray,
+        iou_thresholds: np.ndarray,
+    ) -> np.ndarray:
+        num_predictions, num_iou_levels = (
+            predictions_classes.shape[0],
+            iou_thresholds.shape[0],
+        )
+        correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)
+        correct_class = target_classes[:, None] == predictions_classes
+
+        for i, iou_level in enumerate(iou_thresholds):
+            matched_indices = np.where((iou >= iou_level) & correct_class)
+
+            if matched_indices[0].shape[0]:
+                combined_indices = np.stack(matched_indices, axis=1)
+                iou_values = iou[matched_indices][:, None]
+                matches = np.hstack([combined_indices, iou_values])
+
+                if matched_indices[0].shape[0] > 1:
+                    matches = matches[matches[:, 2].argsort()[::-1]]
+                    matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+                    matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+
+                correct[matches[:, 1].astype(int), i] = True
+
+        return correct
+
+    @staticmethod
+    def _compute_confusion_matrix(
+        sorted_matches: np.ndarray,
+        sorted_prediction_class_ids: np.ndarray,
+        unique_classes: np.ndarray,
+        class_counts: np.ndarray,
+    ) -> np.ndarray:
+        """
+        Compute the confusion matrix for each class and IoU threshold.
+
+        Assumes the matches and prediction_class_ids are sorted by confidence
+        in descending order.
+
+        Arguments:
+            sorted_matches: np.ndarray, bool, shape (P, Th), that is True
+                if the prediction is a true positive at the given IoU threshold.
+            sorted_prediction_class_ids: np.ndarray, int, shape (P,), containing
+                the class id for each prediction.
+            unique_classes: np.ndarray, int, shape (C,), containing the unique
+                class ids.
+            class_counts: np.ndarray, int, shape (C,), containing the number
+                of true instances for each class.
+
+        Returns:
+            np.ndarray, shape (C, Th, 3), containing the true positives, false
+                positives, and false negatives for each class and IoU threshold.
+        """
+
+        num_thresholds = sorted_matches.shape[1]
+        num_classes = unique_classes.shape[0]
+
+        confusion_matrix = np.zeros((num_classes, num_thresholds, 3))
+        for class_idx, class_id in enumerate(unique_classes):
+            is_class = sorted_prediction_class_ids == class_id
+            num_true = class_counts[class_idx]
+            num_predictions = is_class.sum()
+
+            if num_predictions == 0:
+                true_positives = np.zeros(num_thresholds)
+                false_positives = np.zeros(num_thresholds)
+                false_negatives = np.full(num_thresholds, num_true)
+            elif num_true == 0:
+                true_positives = np.zeros(num_thresholds)
+                false_positives = np.full(num_thresholds, num_predictions)
+                false_negatives = np.zeros(num_thresholds)
+            else:
+                true_positives = sorted_matches[is_class].sum(0)
+                false_positives = (1 - sorted_matches[is_class]).sum(0)
+                false_negatives = num_true - true_positives
+            confusion_matrix[class_idx] = np.stack(
+                [true_positives, false_positives, false_negatives], axis=1
+            )
+
+        return confusion_matrix
+
+    @staticmethod
+    def _compute_recall(confusion_matrix: np.ndarray) -> np.ndarray:
+        """
+        Broadcastable function, computing the recall from the confusion matrix.
+
+        Arguments:
+            confusion_matrix: np.ndarray, shape (N, ..., 3), where the last dimension
+                contains the true positives, false positives, and false negatives.
+
+        Returns:
+            np.ndarray, shape (N, ...), containing the recall for each element.
+        """
+        if not confusion_matrix.shape[-1] == 3:
+            raise ValueError(
+                f"Confusion matrix must have shape (..., 3), got "
+                f"{confusion_matrix.shape}"
+            )
+        true_positives = confusion_matrix[..., 0]
+        false_negatives = confusion_matrix[..., 2]
+
+        denominator = true_positives + false_negatives
+        recall = np.where(denominator == 0, 0, true_positives / denominator)
+
+        return recall
+
+    def _detections_content(self, detections: Detections) -> np.ndarray:
+        """Return boxes, masks or oriented bounding boxes from detections."""
+        if self._metric_target == MetricTarget.BOXES:
+            return detections.xyxy
+        if self._metric_target == MetricTarget.MASKS:
+            return (
+                detections.mask
+                if detections.mask is not None
+                else self._make_empty_content()
+            )
+        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+            obb = detections.data.get(ORIENTED_BOX_COORDINATES)
+            if obb is not None and len(obb) > 0:
+                return np.array(obb, dtype=np.float32)
+            return self._make_empty_content()
+        raise ValueError(f"Invalid metric target: {self._metric_target}")
+
+    def _make_empty_content(self) -> np.ndarray:
+        if self._metric_target == MetricTarget.BOXES:
+            return np.empty((0, 4), dtype=np.float32)
+        if self._metric_target == MetricTarget.MASKS:
+            return np.empty((0, 0, 0), dtype=bool)
+        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+            return np.empty((0, 4, 2), dtype=np.float32)
+        raise ValueError(f"Invalid metric target: {self._metric_target}")
+
+    def _filter_detections_by_size(
+        self, detections: Detections, size_category: ObjectSizeCategory
+    ) -> Detections:
+        """Return a copy of detections with contents filtered by object size."""
+        new_detections = deepcopy(detections)
+        if detections.is_empty() or size_category == ObjectSizeCategory.ANY:
+            return new_detections
+
+        sizes = get_detection_size_category(new_detections, self._metric_target)
+        size_mask = sizes == size_category.value
+
+        new_detections.xyxy = new_detections.xyxy[size_mask]
+        if new_detections.mask is not None:
+            new_detections.mask = new_detections.mask[size_mask]
+        if new_detections.class_id is not None:
+            new_detections.class_id = new_detections.class_id[size_mask]
+        if new_detections.confidence is not None:
+            new_detections.confidence = new_detections.confidence[size_mask]
+        if new_detections.tracker_id is not None:
+            new_detections.tracker_id = new_detections.tracker_id[size_mask]
+        if new_detections.data is not None:
+            for key, value in new_detections.data.items():
+                new_detections.data[key] = np.array(value)[size_mask]
+
+        return new_detections
+
+    def _filter_predictions_and_targets_by_size(
+        self,
+        predictions_list: List[Detections],
+        targets_list: List[Detections],
+        size_category: ObjectSizeCategory,
+    ) -> Tuple[List[Detections], List[Detections]]:
+        """
+        Filter predictions and targets by object size category.
+        """
+        new_predictions_list = []
+        new_targets_list = []
+        for predictions, targets in zip(predictions_list, targets_list):
+            new_predictions_list.append(
+                self._filter_detections_by_size(predictions, size_category)
+            )
+            new_targets_list.append(
+                self._filter_detections_by_size(targets, size_category)
+            )
+        return new_predictions_list, new_targets_list
+
+
+@dataclass
+class RecallResult:
+    """
+    The results of the recall metric calculation.
+
+    Defaults to `0` if no detections or targets were provided.
+
+    Attributes:
+        metric_target (MetricTarget): the type of data used for the metric -
+            boxes, masks or oriented bounding boxes.
+        averaging_method (AveragingMethod): the averaging method used to compute the
+            recall. Determines how the recall is aggregated across classes.
+        recall_at_50 (float): the recall at IoU threshold of `0.5`.
+        recall_at_75 (float): the recall at IoU threshold of `0.75`.
+        recall_scores (np.ndarray): the recall scores at each IoU threshold.
+            Shape: `(num_iou_thresholds,)`
+        recall_per_class (np.ndarray): the recall scores per class and IoU threshold.
+            Shape: `(num_target_classes, num_iou_thresholds)`
+        iou_thresholds (np.ndarray): the IoU thresholds used in the calculations.
+        matched_classes (np.ndarray): the class IDs of all matched classes.
+            Corresponds to the rows of `recall_per_class`.
+        small_objects (Optional[RecallResult]): the Recall metric results
+            for small objects.
+        medium_objects (Optional[RecallResult]): the Recall metric results
+            for medium objects.
+        large_objects (Optional[RecallResult]): the Recall metric results
+            for large objects.
+    """
+
+    metric_target: MetricTarget
+    averaging_method: AveragingMethod
+
+    @property
+    def recall_at_50(self) -> float:
+        return self.recall_scores[0]
+
+    @property
+    def recall_at_75(self) -> float:
+        return self.recall_scores[5]
+
+    recall_scores: np.ndarray
+    recall_per_class: np.ndarray
+    iou_thresholds: np.ndarray
+    matched_classes: np.ndarray
+
+    small_objects: Optional[RecallResult]
+    medium_objects: Optional[RecallResult]
+    large_objects: Optional[RecallResult]
+
+    def __str__(self) -> str:
+        """
+        Format as a pretty string.
+
+        Example:
+            ```python
+            print(recall_result)
+            ```
+        """
+        out_str = (
+            f"{self.__class__.__name__}:\n"
+            f"Metric target:    {self.metric_target}\n"
+            f"Averaging method: {self.averaging_method}\n"
+            f"R @ 50:     {self.recall_at_50:.4f}\n"
+            f"R @ 75:     {self.recall_at_75:.4f}\n"
+            f"R @ thresh: {self.recall_scores}\n"
+            f"IoU thresh: {self.iou_thresholds}\n"
+            f"Recall per class:\n"
+        )
+        if self.recall_per_class.size == 0:
+            out_str += "  No results\n"
+        for class_id, recall_of_class in zip(
+            self.matched_classes, self.recall_per_class
+        ):
+            out_str += f"  {class_id}: {recall_of_class}\n"
+
+        indent = "  "
+        if self.small_objects is not None:
+            indented = indent + str(self.small_objects).replace("\n", f"\n{indent}")
+            out_str += f"\nSmall objects:\n{indented}"
+        if self.medium_objects is not None:
+            indented = indent + str(self.medium_objects).replace("\n", f"\n{indent}")
+            out_str += f"\nMedium objects:\n{indented}"
+        if self.large_objects is not None:
+            indented = indent + str(self.large_objects).replace("\n", f"\n{indent}")
+            out_str += f"\nLarge objects:\n{indented}"
+
+        return out_str
+
+    def to_pandas(self) -> "pd.DataFrame":
+        """
+        Convert the result to a pandas DataFrame.
+
+        Returns:
+            (pd.DataFrame): The result as a DataFrame.
+        """
+        ensure_pandas_installed()
+        import pandas as pd
+
+        pandas_data = {
+            "R@50": self.recall_at_50,
+            "R@75": self.recall_at_75,
+        }
+
+        if self.small_objects is not None:
+            small_objects_df = self.small_objects.to_pandas()
+            for key, value in small_objects_df.items():
+                pandas_data[f"small_objects_{key}"] = value
+        if self.medium_objects is not None:
+            medium_objects_df = self.medium_objects.to_pandas()
+            for key, value in medium_objects_df.items():
+                pandas_data[f"medium_objects_{key}"] = value
+        if self.large_objects is not None:
+            large_objects_df = self.large_objects.to_pandas()
+            for key, value in large_objects_df.items():
+                pandas_data[f"large_objects_{key}"] = value
+
+        return pd.DataFrame(pandas_data, index=[0])
+
+    def plot(self):
+        """
+        Plot the recall results.
+        """
+
+        labels = ["Recall@50", "Recall@75"]
+        values = [self.recall_at_50, self.recall_at_75]
+        colors = [LEGACY_COLOR_PALETTE[0]] * 2
+
+        if self.small_objects is not None:
+            small_objects = self.small_objects
+            labels += ["Small: R@50", "Small: R@75"]
+            values += [small_objects.recall_at_50, small_objects.recall_at_75]
+            colors += [LEGACY_COLOR_PALETTE[3]] * 2
+
+        if self.medium_objects is not None:
+            medium_objects = self.medium_objects
+            labels += ["Medium: R@50", "Medium: R@75"]
+            values += [medium_objects.recall_at_50, medium_objects.recall_at_75]
+            colors += [LEGACY_COLOR_PALETTE[2]] * 2
+
+        if self.large_objects is not None:
+            large_objects = self.large_objects
+            labels += ["Large: R@50", "Large: R@75"]
+            values += [large_objects.recall_at_50, large_objects.recall_at_75]
+            colors += [LEGACY_COLOR_PALETTE[4]] * 2
+
+        plt.rcParams["font.family"] = "monospace"
+
+        _, ax = plt.subplots(figsize=(10, 6))
+        ax.set_ylim(0, 1)
+        ax.set_ylabel("Value", fontweight="bold")
+        title = (
+            f"Recall, by Object Size"
+            f"\n(target: {self.metric_target.value},"
+            f" averaging: {self.averaging_method.value})"
+        )
+        ax.set_title(title, fontweight="bold")
+
+        x_positions = range(len(labels))
+        bars = ax.bar(x_positions, values, color=colors, align="center")
+
+        ax.set_xticks(x_positions)
+        ax.set_xticklabels(labels, rotation=45, ha="right")
+
+        for bar in bars:
+            y_value = bar.get_height()
+            ax.text(
+                bar.get_x() + bar.get_width() / 2,
+                y_value + 0.02,
+                f"{y_value:.2f}",
+                ha="center",
+                va="bottom",
+            )
+
+        plt.rcParams["font.family"] = "sans-serif"
+
+        plt.tight_layout()
+        plt.show()
diff --git a/supervision/py.typed b/supervision/py.typed
new file mode 100644
index 00000000..e69de29b
diff --git a/supervision/tracker/byte_tracker/basetrack.py b/supervision/tracker/byte_tracker/basetrack.py
deleted file mode 100644
index 806f7538..00000000
--- a/supervision/tracker/byte_tracker/basetrack.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from collections import OrderedDict
-from enum import Enum
-
-import numpy as np
-
-
-class TrackState(Enum):
-    New = 0
-    Tracked = 1
-    Lost = 2
-    Removed = 3
-
-
-class BaseTrack:
-    _count = 0
-
-    def __init__(self):
-        self.track_id = 0
-        self.is_activated = False
-        self.state = TrackState.New
-
-        self.history = OrderedDict()
-        self.features = []
-        self.curr_feature = None
-        self.score = 0
-        self.start_frame = 0
-        self.frame_id = 0
-        self.time_since_update = 0
-
-        # multi-camera
-        self.location = (np.inf, np.inf)
-
-    @property
-    def end_frame(self) -> int:
-        return self.frame_id
-
-    @staticmethod
-    def next_id() -> int:
-        BaseTrack._count += 1
-        return BaseTrack._count
-
-    @staticmethod
-    def reset_counter():
-        BaseTrack._count = 0
-        BaseTrack.track_id = 0
-        BaseTrack.start_frame = 0
-        BaseTrack.frame_id = 0
-        BaseTrack.time_since_update = 0
-
-    def activate(self, *args):
-        raise NotImplementedError
-
-    def predict(self):
-        raise NotImplementedError
-
-    def update(self, *args, **kwargs):
-        raise NotImplementedError
-
-    def mark_lost(self):
-        self.state = TrackState.Lost
-
-    def mark_removed(self):
-        self.state = TrackState.Removed
diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index 89e1e2f2..cb46af73 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -5,186 +5,9 @@ import numpy as np
 from supervision.detection.core import Detections
 from supervision.detection.utils import box_iou_batch
 from supervision.tracker.byte_tracker import matching
-from supervision.tracker.byte_tracker.basetrack import BaseTrack, TrackState
 from supervision.tracker.byte_tracker.kalman_filter import KalmanFilter
-
-
-class STrack(BaseTrack):
-    shared_kalman = KalmanFilter()
-    _external_count = 0
-
-    def __init__(self, tlwh, score, class_ids, minimum_consecutive_frames):
-        # wait activate
-        self._tlwh = np.asarray(tlwh, dtype=np.float32)
-        self.kalman_filter = None
-        self.mean, self.covariance = None, None
-        self.is_activated = False
-
-        self.score = score
-        self.class_ids = class_ids
-        self.tracklet_len = 0
-
-        self.external_track_id = -1
-
-        self.minimum_consecutive_frames = minimum_consecutive_frames
-
-    def predict(self):
-        mean_state = self.mean.copy()
-        if self.state != TrackState.Tracked:
-            mean_state[7] = 0
-        self.mean, self.covariance = self.kalman_filter.predict(
-            mean_state, self.covariance
-        )
-
-    @staticmethod
-    def multi_predict(stracks):
-        if len(stracks) > 0:
-            multi_mean = []
-            multi_covariance = []
-            for i, st in enumerate(stracks):
-                multi_mean.append(st.mean.copy())
-                multi_covariance.append(st.covariance)
-                if st.state != TrackState.Tracked:
-                    multi_mean[i][7] = 0
-
-            multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(
-                np.asarray(multi_mean), np.asarray(multi_covariance)
-            )
-            for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
-                stracks[i].mean = mean
-                stracks[i].covariance = cov
-
-    def activate(self, kalman_filter, frame_id):
-        """Start a new tracklet"""
-        self.kalman_filter = kalman_filter
-        self.internal_track_id = self.next_id()
-        self.mean, self.covariance = self.kalman_filter.initiate(
-            self.tlwh_to_xyah(self._tlwh)
-        )
-
-        self.tracklet_len = 0
-        self.state = TrackState.Tracked
-        if frame_id == 1:
-            self.is_activated = True
-
-        if self.minimum_consecutive_frames == 1:
-            self.external_track_id = self.next_external_id()
-
-        self.frame_id = frame_id
-        self.start_frame = frame_id
-
-    def re_activate(self, new_track, frame_id, new_id=False):
-        self.mean, self.covariance = self.kalman_filter.update(
-            self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
-        )
-        self.tracklet_len = 0
-        self.state = TrackState.Tracked
-
-        self.frame_id = frame_id
-        if new_id:
-            self.internal_track_id = self.next_id()
-        self.score = new_track.score
-
-    def update(self, new_track, frame_id):
-        """
-        Update a matched track
-        :type new_track: STrack
-        :type frame_id: int
-        :type update_feature: bool
-        :return:
-        """
-        self.frame_id = frame_id
-        self.tracklet_len += 1
-
-        new_tlwh = new_track.tlwh
-        self.mean, self.covariance = self.kalman_filter.update(
-            self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh)
-        )
-        self.state = TrackState.Tracked
-        if self.tracklet_len == self.minimum_consecutive_frames:
-            self.is_activated = True
-            if self.external_track_id == -1:
-                self.external_track_id = self.next_external_id()
-
-        self.score = new_track.score
-
-    @property
-    def tlwh(self):
-        """Get current position in bounding box format `(top left x, top left y,
-        width, height)`.
-        """
-        if self.mean is None:
-            return self._tlwh.copy()
-        ret = self.mean[:4].copy()
-        ret[2] *= ret[3]
-        ret[:2] -= ret[2:] / 2
-        return ret
-
-    @property
-    def tlbr(self):
-        """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
-        `(top left, bottom right)`.
-        """
-        ret = self.tlwh.copy()
-        ret[2:] += ret[:2]
-        return ret
-
-    @staticmethod
-    def tlwh_to_xyah(tlwh):
-        """Convert bounding box to format `(center x, center y, aspect ratio,
-        height)`, where the aspect ratio is `width / height`.
-        """
-        ret = np.asarray(tlwh).copy()
-        ret[:2] += ret[2:] / 2
-        ret[2] /= ret[3]
-        return ret
-
-    def to_xyah(self):
-        return self.tlwh_to_xyah(self.tlwh)
-
-    @staticmethod
-    def next_external_id():
-        STrack._external_count += 1
-        return STrack._external_count
-
-    @staticmethod
-    def reset_external_counter():
-        STrack._external_count = 0
-
-    @staticmethod
-    def tlbr_to_tlwh(tlbr):
-        ret = np.asarray(tlbr).copy()
-        ret[2:] -= ret[:2]
-        return ret
-
-    @staticmethod
-    def tlwh_to_tlbr(tlwh):
-        ret = np.asarray(tlwh).copy()
-        ret[2:] += ret[:2]
-        return ret
-
-    def __repr__(self):
-        return "OT_{}_({}-{})".format(
-            self.internal_track_id, self.start_frame, self.end_frame
-        )
-
-
-def detections2boxes(detections: Detections) -> np.ndarray:
-    """
-    Convert Supervision Detections to numpy tensors for further computation.
-    Args:
-        detections (Detections): Detections/Targets in the format of sv.Detections.
-    Returns:
-        (np.ndarray): Detections as numpy tensors as in
-            `(x_min, y_min, x_max, y_max, confidence, class_id)` order.
-    """
-    return np.hstack(
-        (
-            detections.xyxy,
-            detections.confidence[:, np.newaxis],
-            detections.class_id[:, np.newaxis],
-        )
-    )
+from supervision.tracker.byte_tracker.single_object_track import STrack, TrackState
+from supervision.tracker.byte_tracker.utils import IdCounter
 
 
 class ByteTrack:
@@ -230,11 +53,17 @@ class ByteTrack:
         self.max_time_lost = int(frame_rate / 30.0 * lost_track_buffer)
         self.minimum_consecutive_frames = minimum_consecutive_frames
         self.kalman_filter = KalmanFilter()
+        self.shared_kalman = KalmanFilter()
 
         self.tracked_tracks: List[STrack] = []
         self.lost_tracks: List[STrack] = []
         self.removed_tracks: List[STrack] = []
 
+        # Warning, possible bug: If you also set internal_id to start at 1,
+        # all traces will be connected across objects.
+        self.internal_id_counter = IdCounter()
+        self.external_id_counter = IdCounter(start_id=1)
+
     def update_with_detections(self, detections: Detections) -> Detections:
         """
         Updates the tracker with the provided detections and returns the updated
@@ -274,8 +103,12 @@ class ByteTrack:
             )
             ```
         """
-
-        tensors = detections2boxes(detections=detections)
+        tensors = np.hstack(
+            (
+                detections.xyxy,
+                detections.confidence[:, np.newaxis],
+            )
+        )
         tracks = self.update_with_tensors(tensors=tensors)
 
         if len(tracks) > 0:
@@ -301,7 +134,7 @@ class ByteTrack:
 
             return detections
 
-    def reset(self):
+    def reset(self) -> None:
         """
         Resets the internal state of the ByteTrack tracker.
 
@@ -311,11 +144,11 @@ class ByteTrack:
         ensuring the tracker starts with a clean state for each new video.
         """
         self.frame_id = 0
-        self.tracked_tracks: List[STrack] = []
-        self.lost_tracks: List[STrack] = []
-        self.removed_tracks: List[STrack] = []
-        BaseTrack.reset_counter()
-        STrack.reset_external_counter()
+        self.internal_id_counter.reset()
+        self.external_id_counter.reset()
+        self.tracked_tracks = []
+        self.lost_tracks = []
+        self.removed_tracks = []
 
     def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
         """
@@ -333,7 +166,6 @@ class ByteTrack:
         lost_stracks = []
         removed_stracks = []
 
-        class_ids = tensors[:, 5]
         scores = tensors[:, 4]
         bboxes = tensors[:, :4]
 
@@ -347,14 +179,18 @@ class ByteTrack:
         scores_keep = scores[remain_inds]
         scores_second = scores[inds_second]
 
-        class_ids_keep = class_ids[remain_inds]
-        class_ids_second = class_ids[inds_second]
-
         if len(dets) > 0:
             """Detections"""
             detections = [
-                STrack(STrack.tlbr_to_tlwh(tlbr), s, c, self.minimum_consecutive_frames)
-                for (tlbr, s, c) in zip(dets, scores_keep, class_ids_keep)
+                STrack(
+                    STrack.tlbr_to_tlwh(tlbr),
+                    score_keep,
+                    self.minimum_consecutive_frames,
+                    self.shared_kalman,
+                    self.internal_id_counter,
+                    self.external_id_counter,
+                )
+                for (tlbr, score_keep) in zip(dets, scores_keep)
             ]
         else:
             detections = []
@@ -372,7 +208,7 @@ class ByteTrack:
         """ Step 2: First association, with high score detection boxes"""
         strack_pool = joint_tracks(tracked_stracks, self.lost_tracks)
         # Predict the current location with KF
-        STrack.multi_predict(strack_pool)
+        STrack.multi_predict(strack_pool, self.shared_kalman)
         dists = matching.iou_distance(strack_pool, detections)
 
         dists = matching.fuse_score(dists, detections)
@@ -387,7 +223,7 @@ class ByteTrack:
                 track.update(detections[idet], self.frame_id)
                 activated_starcks.append(track)
             else:
-                track.re_activate(det, self.frame_id, new_id=False)
+                track.re_activate(det, self.frame_id)
                 refind_stracks.append(track)
 
         """ Step 3: Second association, with low score detection boxes"""
@@ -395,8 +231,15 @@ class ByteTrack:
         if len(dets_second) > 0:
             """Detections"""
             detections_second = [
-                STrack(STrack.tlbr_to_tlwh(tlbr), s, c, self.minimum_consecutive_frames)
-                for (tlbr, s, c) in zip(dets_second, scores_second, class_ids_second)
+                STrack(
+                    STrack.tlbr_to_tlwh(tlbr),
+                    score_second,
+                    self.minimum_consecutive_frames,
+                    self.shared_kalman,
+                    self.internal_id_counter,
+                    self.external_id_counter,
+                )
+                for (tlbr, score_second) in zip(dets_second, scores_second)
             ]
         else:
             detections_second = []
@@ -416,13 +259,13 @@ class ByteTrack:
                 track.update(det, self.frame_id)
                 activated_starcks.append(track)
             else:
-                track.re_activate(det, self.frame_id, new_id=False)
+                track.re_activate(det, self.frame_id)
                 refind_stracks.append(track)
 
         for it in u_track:
             track = r_tracked_stracks[it]
             if not track.state == TrackState.Lost:
-                track.mark_lost()
+                track.state = TrackState.Lost
                 lost_stracks.append(track)
 
         """Deal with unconfirmed tracks, usually tracks with only one beginning frame"""
@@ -438,7 +281,7 @@ class ByteTrack:
             activated_starcks.append(unconfirmed[itracked])
         for it in u_unconfirmed:
             track = unconfirmed[it]
-            track.mark_removed()
+            track.state = TrackState.Removed
             removed_stracks.append(track)
 
         """ Step 4: Init new stracks"""
@@ -450,8 +293,8 @@ class ByteTrack:
             activated_starcks.append(track)
         """ Step 5: Update state"""
         for track in self.lost_tracks:
-            if self.frame_id - track.end_frame > self.max_time_lost:
-                track.mark_removed()
+            if self.frame_id - track.frame_id > self.max_time_lost:
+                track.state = TrackState.Removed
                 removed_stracks.append(track)
 
         self.tracked_tracks = [
@@ -497,7 +340,7 @@ def joint_tracks(
     return result
 
 
-def sub_tracks(track_list_a: List, track_list_b: List) -> List[int]:
+def sub_tracks(track_list_a: List[STrack], track_list_b: List[STrack]) -> List[int]:
     """
     Returns a list of tracks from track_list_a after removing any tracks
     that share the same internal_track_id with tracks in track_list_b.
@@ -518,7 +361,9 @@ def sub_tracks(track_list_a: List, track_list_b: List) -> List[int]:
     return list(tracks.values())
 
 
-def remove_duplicate_tracks(tracks_a: List, tracks_b: List) -> Tuple[List, List]:
+def remove_duplicate_tracks(
+    tracks_a: List[STrack], tracks_b: List[STrack]
+) -> Tuple[List[STrack], List[STrack]]:
     pairwise_distance = matching.iou_distance(tracks_a, tracks_b)
     matching_pairs = np.where(pairwise_distance < 0.15)
 
diff --git a/supervision/tracker/byte_tracker/matching.py b/supervision/tracker/byte_tracker/matching.py
index 24abe224..eb774d4c 100644
--- a/supervision/tracker/byte_tracker/matching.py
+++ b/supervision/tracker/byte_tracker/matching.py
@@ -1,10 +1,15 @@
-from typing import List, Tuple
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, List, Tuple
 
 import numpy as np
 from scipy.optimize import linear_sum_assignment
 
 from supervision.detection.utils import box_iou_batch
 
+if TYPE_CHECKING:
+    from supervision.tracker.byte_tracker.core import STrack
+
 
 def indices_to_matches(
     cost_matrix: np.ndarray, indices: np.ndarray, thresh: float
@@ -20,7 +25,7 @@ def indices_to_matches(
 
 def linear_assignment(
     cost_matrix: np.ndarray, thresh: float
-) -> [np.ndarray, Tuple[int], Tuple[int, int]]:
+) -> Tuple[np.ndarray, Tuple[int], Tuple[int, int]]:
     if cost_matrix.size == 0:
         return (
             np.empty((0, 2), dtype=int),
@@ -35,7 +40,7 @@ def linear_assignment(
     return indices_to_matches(cost_matrix, indices, thresh)
 
 
-def iou_distance(atracks: List, btracks: List) -> np.ndarray:
+def iou_distance(atracks: List[STrack], btracks: List[STrack]) -> np.ndarray:
     if (len(atracks) > 0 and isinstance(atracks[0], np.ndarray)) or (
         len(btracks) > 0 and isinstance(btracks[0], np.ndarray)
     ):
@@ -53,11 +58,11 @@ def iou_distance(atracks: List, btracks: List) -> np.ndarray:
     return cost_matrix
 
 
-def fuse_score(cost_matrix: np.ndarray, detections: List) -> np.ndarray:
+def fuse_score(cost_matrix: np.ndarray, stracks: List[STrack]) -> np.ndarray:
     if cost_matrix.size == 0:
         return cost_matrix
     iou_sim = 1 - cost_matrix
-    det_scores = np.array([det.score for det in detections])
+    det_scores = np.array([strack.score for strack in stracks])
     det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)
     fuse_sim = iou_sim * det_scores
     fuse_cost = 1 - fuse_sim
diff --git a/supervision/tracker/byte_tracker/single_object_track.py b/supervision/tracker/byte_tracker/single_object_track.py
new file mode 100644
index 00000000..3b9bfdf2
--- /dev/null
+++ b/supervision/tracker/byte_tracker/single_object_track.py
@@ -0,0 +1,178 @@
+from __future__ import annotations
+
+from enum import Enum
+from typing import List
+
+import numpy as np
+import numpy.typing as npt
+
+from supervision.tracker.byte_tracker.kalman_filter import KalmanFilter
+from supervision.tracker.byte_tracker.utils import IdCounter
+
+
+class TrackState(Enum):
+    New = 0
+    Tracked = 1
+    Lost = 2
+    Removed = 3
+
+
+class STrack:
+    def __init__(
+        self,
+        tlwh: npt.NDArray[np.float32],
+        score: npt.NDArray[np.float32],
+        minimum_consecutive_frames: int,
+        shared_kalman: KalmanFilter,
+        internal_id_counter: IdCounter,
+        external_id_counter: IdCounter,
+    ):
+        self.state = TrackState.New
+        self.is_activated = False
+        self.start_frame = 0
+        self.frame_id = 0
+
+        self._tlwh = np.asarray(tlwh, dtype=np.float32)
+        self.kalman_filter = None
+        self.shared_kalman = shared_kalman
+        self.mean, self.covariance = None, None
+        self.is_activated = False
+
+        self.score = score
+        self.tracklet_len = 0
+
+        self.minimum_consecutive_frames = minimum_consecutive_frames
+
+        self.internal_id_counter = internal_id_counter
+        self.external_id_counter = external_id_counter
+        self.internal_track_id = self.internal_id_counter.NO_ID
+        self.external_track_id = self.external_id_counter.NO_ID
+
+    def predict(self) -> None:
+        mean_state = self.mean.copy()
+        if self.state != TrackState.Tracked:
+            mean_state[7] = 0
+        self.mean, self.covariance = self.kalman_filter.predict(
+            mean_state, self.covariance
+        )
+
+    @staticmethod
+    def multi_predict(stracks: List[STrack], shared_kalman: KalmanFilter) -> None:
+        if len(stracks) > 0:
+            multi_mean = []
+            multi_covariance = []
+            for i, st in enumerate(stracks):
+                multi_mean.append(st.mean.copy())
+                multi_covariance.append(st.covariance)
+                if st.state != TrackState.Tracked:
+                    multi_mean[i][7] = 0
+
+            multi_mean, multi_covariance = shared_kalman.multi_predict(
+                np.asarray(multi_mean), np.asarray(multi_covariance)
+            )
+            for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
+                stracks[i].mean = mean
+                stracks[i].covariance = cov
+
+    def activate(self, kalman_filter: KalmanFilter, frame_id: int) -> None:
+        """Start a new tracklet"""
+        self.kalman_filter = kalman_filter
+        self.internal_track_id = self.internal_id_counter.new_id()
+        self.mean, self.covariance = self.kalman_filter.initiate(
+            self.tlwh_to_xyah(self._tlwh)
+        )
+
+        self.tracklet_len = 0
+        self.state = TrackState.Tracked
+        if frame_id == 1:
+            self.is_activated = True
+
+        if self.minimum_consecutive_frames == 1:
+            self.external_track_id = self.external_id_counter.new_id()
+
+        self.frame_id = frame_id
+        self.start_frame = frame_id
+
+    def re_activate(self, new_track: STrack, frame_id: int) -> None:
+        self.mean, self.covariance = self.kalman_filter.update(
+            self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
+        )
+        self.tracklet_len = 0
+        self.state = TrackState.Tracked
+
+        self.frame_id = frame_id
+        self.score = new_track.score
+
+    def update(self, new_track: STrack, frame_id: int) -> None:
+        """
+        Update a matched track
+        :type new_track: STrack
+        :type frame_id: int
+        :type update_feature: bool
+        :return:
+        """
+        self.frame_id = frame_id
+        self.tracklet_len += 1
+
+        new_tlwh = new_track.tlwh
+        self.mean, self.covariance = self.kalman_filter.update(
+            self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh)
+        )
+        self.state = TrackState.Tracked
+        if self.tracklet_len == self.minimum_consecutive_frames:
+            self.is_activated = True
+            if self.external_track_id == self.external_id_counter.NO_ID:
+                self.external_track_id = self.external_id_counter.new_id()
+
+        self.score = new_track.score
+
+    @property
+    def tlwh(self) -> npt.NDArray[np.float32]:
+        """Get current position in bounding box format `(top left x, top left y,
+        width, height)`.
+        """
+        if self.mean is None:
+            return self._tlwh.copy()
+        ret = self.mean[:4].copy()
+        ret[2] *= ret[3]
+        ret[:2] -= ret[2:] / 2
+        return ret
+
+    @property
+    def tlbr(self) -> npt.NDArray[np.float32]:
+        """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
+        `(top left, bottom right)`.
+        """
+        ret = self.tlwh.copy()
+        ret[2:] += ret[:2]
+        return ret
+
+    @staticmethod
+    def tlwh_to_xyah(tlwh) -> npt.NDArray[np.float32]:
+        """Convert bounding box to format `(center x, center y, aspect ratio,
+        height)`, where the aspect ratio is `width / height`.
+        """
+        ret = np.asarray(tlwh).copy()
+        ret[:2] += ret[2:] / 2
+        ret[2] /= ret[3]
+        return ret
+
+    def to_xyah(self) -> npt.NDArray[np.float32]:
+        return self.tlwh_to_xyah(self.tlwh)
+
+    @staticmethod
+    def tlbr_to_tlwh(tlbr) -> npt.NDArray[np.float32]:
+        ret = np.asarray(tlbr).copy()
+        ret[2:] -= ret[:2]
+        return ret
+
+    @staticmethod
+    def tlwh_to_tlbr(tlwh) -> npt.NDArray[np.float32]:
+        ret = np.asarray(tlwh).copy()
+        ret[2:] += ret[:2]
+        return ret
+
+    def __repr__(self) -> str:
+        return "OT_{}_({}-{})".format(
+            self.internal_track_id, self.start_frame, self.frame_id
+        )
diff --git a/supervision/tracker/byte_tracker/utils.py b/supervision/tracker/byte_tracker/utils.py
new file mode 100644
index 00000000..cd2a1036
--- /dev/null
+++ b/supervision/tracker/byte_tracker/utils.py
@@ -0,0 +1,18 @@
+class IdCounter:
+    def __init__(self, start_id: int = 0):
+        self.start_id = start_id
+        if self.start_id <= self.NO_ID:
+            raise ValueError(f"start_id must be greater than {self.NO_ID}")
+        self.reset()
+
+    def reset(self) -> None:
+        self._id = self.start_id
+
+    def new_id(self) -> int:
+        returned_id = self._id
+        self._id += 1
+        return returned_id
+
+    @property
+    def NO_ID(self) -> int:
+        return -1
diff --git a/supervision/utils/video.py b/supervision/utils/video.py
index 2e502cf2..9d67dbfb 100644
--- a/supervision/utils/video.py
+++ b/supervision/utils/video.py
@@ -65,8 +65,9 @@ class VideoSink:
 
     Attributes:
         target_path (str): The path to the output file where the video will be saved.
-        video_info (VideoInfo): Information about the video resolution, fps,
-            and total frame count.
+        video_info (Optional[VideoInfo]): Information about the output video resolution,
+            fps, and total frame count. If not provided, the information will be inferred
+            from the video path.
         codec (str): FOURCC code for video format
 
     Example:
@@ -82,8 +83,16 @@ class VideoSink:
         ```
     """  # noqa: E501 // docs
 
-    def __init__(self, target_path: str, video_info: VideoInfo, codec: str = "mp4v"):
+    def __init__(
+        self,
+        target_path: str,
+        video_info: Optional[VideoInfo] = None,
+        codec: str = "mp4v",
+    ):
         self.target_path = target_path
+
+        if video_info is None:
+            video_info = VideoInfo.from_video_path(target_path)
         self.video_info = video_info
         self.__codec = codec
         self.__writer = None
diff --git a/test/tracker/__init__.py b/test/tracker/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/test/tracker/test_byte_tracker.py b/test/tracker/test_byte_tracker.py
new file mode 100644
index 00000000..98efeb09
--- /dev/null
+++ b/test/tracker/test_byte_tracker.py
@@ -0,0 +1,40 @@
+from typing import List
+
+import numpy as np
+import pytest
+
+import supervision as sv
+
+
+@pytest.mark.parametrize(
+    "detections, expected_results",
+    [
+        (
+            [
+                sv.Detections(
+                    xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]),
+                    class_id=np.array([1, 1]),
+                    confidence=np.array([1, 1]),
+                ),
+                sv.Detections(
+                    xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]),
+                    class_id=np.array([1, 1]),
+                    confidence=np.array([1, 1]),
+                ),
+            ],
+            sv.Detections(
+                xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]),
+                class_id=np.array([1, 1]),
+                confidence=np.array([1, 1]),
+                tracker_id=np.array([1, 2]),
+            ),
+        ),
+    ],
+)
+def test_byte_tracker(
+    detections: List[sv.Detections],
+    expected_results: sv.Detections,
+) -> None:
+    byte_tracker = sv.ByteTrack()
+    tracked_detections = [byte_tracker.update_with_detections(d) for d in detections]
+    assert tracked_detections[-1] == expected_results
diff --git a/test/utils/test_internal.py b/test/utils/test_internal.py
index eee614e6..872822a7 100644
--- a/test/utils/test_internal.py
+++ b/test/utils/test_internal.py
@@ -121,7 +121,15 @@ class MockDataclass:
         (
             Detections.empty(),
             False,
-            {"xyxy", "class_id", "confidence", "mask", "tracker_id", "data"},
+            {
+                "xyxy",
+                "class_id",
+                "confidence",
+                "mask",
+                "tracker_id",
+                "data",
+                "metadata",
+            },
             DoesNotRaise(),
         ),
         (
@@ -134,6 +142,7 @@ class MockDataclass:
                 "mask",
                 "tracker_id",
                 "data",
+                "metadata",
                 "area",
                 "box_area",
             },
@@ -149,6 +158,7 @@ class MockDataclass:
                 "mask",
                 "tracker_id",
                 "data",
+                "metadata",
             },
             DoesNotRaise(),
         ),
@@ -169,13 +179,22 @@ class MockDataclass:
                 "mask",
                 "tracker_id",
                 "data",
+                "metadata",
             },
             DoesNotRaise(),
         ),
         (
             Detections.empty(),
             False,
-            {"xyxy", "class_id", "confidence", "mask", "tracker_id", "data"},
+            {
+                "xyxy",
+                "class_id",
+                "confidence",
+                "mask",
+                "tracker_id",
+                "data",
+                "metadata",
+            },
             DoesNotRaise(),
         ),
     ],
diff --git a/tox.ini b/tox.ini
index 46886c13..3f44d215 100644
--- a/tox.ini
+++ b/tox.ini
@@ -1,5 +1,5 @@
 [tox]
-envlist = py38,py39,py310,py311,py312
+envlist = py38,py39,py310,py311,py312,py313
 
 [testenv]
 changedir = test