Merge pull request #1351 from roboflow/develop

`supervision-0.22.0` release
bump version from 0.22.0rc1 to 0.22.0
2024-07-12 19:59:08 +03:00 · 2024-07-12 18:53:53 +02:00 · 2024-07-12 19:49:17 +03:00 · 2024-07-12 18:47:41 +02:00 · 2024-07-12 19:10:25 +03:00 · 2024-07-12 18:59:06 +03:00
52 changed files with 5653 additions and 2229 deletions
@@ -45,7 +45,7 @@ repos:


  -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.5.0
+      rev: v0.5.1
      hooks:
      -   id: ruff
          args: [--fix, --exit-non-zero-on-fix]
@@ -4,6 +4,25 @@ Thank you for your interest in contributing to Supervision!

 We are actively improving this library to reduce the amount of work you need to do to solve common computer vision problems.

+## Code of Conduct
+
+Please read and adhere to our [Code of Conduct](CODE_OF_CONDUCT.md). This document outlines the expected behavior for all participants in our project.
+
+## Table of Contents
+
+- [Contribution Guidelines](#contribution-guidelines)
+  - [Contributing Features](#contributing-features-)
+- [How to Contribute Changes](#how-to-contribute-changes)
+- [Installation for Contributors](#installation-for-contributors)
+- [Code Style and Quality](#-code-style-and-quality)
+  - [Pre-commit tool](#pre-commit-tool)
+  - [Docstrings](#docstrings)
+  - [Type checking](#type-checking)
+- [Documentation](#-documentation)
+- [Cookbooks](#-cookbooks)
+- [Tests](#-tests)
+- [License](#-license)
+
 ## Contribution Guidelines

 We welcome contributions to:
@@ -32,49 +51,49 @@ First, fork this repository to your own GitHub account. Click "fork" in the top

 Then, run `git clone` to download the project code to your computer.

+You should also set up `roboflow/supervision` as an "upstream" remote (that is, tell git that the reference Supervision repository was the source of your fork of it):
+
+```bash
+git remote add upstream https://github.com/roboflow/supervision.git
+git fetch upstream
+```
+
 Move to a new branch using the `git checkout` command:

 ```bash
-git checkout -b <your_branch_name>
+git checkout -b <scope>/<your_branch_name> upstream/develop
 ```

-The name you choose for your branch should describe the change you want to make (i.e. `line-counter-docs`).
+The name you choose for your branch should describe the change you want to make and start with an appropriate prefix:
+
+- `feat/`: for new features (e.g., `feat/line-counter`)
+- `fix/`: for bug fixes (e.g., `fix/memory-leak`)
+- `docs/`: for documentation changes (e.g., `docs/update-readme`)
+- `chore/`: for routine tasks, maintenance, or tooling changes (e.g., `chore/update-dependencies`)
+- `test/`: for adding or modifying tests (e.g., `test/add-unit-tests`)
+- `refactor/`: for code refactoring (e.g., `refactor/simplify-algorithm`)

 Make any changes you want to the project code, then run the following commands to commit your changes:

 ```bash
-git add .
-git commit -m "Your commit message"
-git push -u origin main
+git add -A
+git commit -m "feat: add line counter functionality"
+git push -u origin <your_branch_name>
 ```

-## 🎨 Code quality
+Use conventional commit messages to clearly describe your changes. The format is:

-### Pre-commit tool
+<type>[optional scope]: <description>

-This project uses the [pre-commit](https://pre-commit.com/) tool to maintain code quality and consistency. Before submitting a pull request or making any commits, it is important to run the pre-commit tool to ensure that your changes meet the project's guidelines.
-
-Furthermore, we have integrated a pre-commit GitHub Action into our workflow. This means that with every pull request opened, the pre-commit checks will be automatically enforced, streamlining the code review process and ensuring that all contributions adhere to our quality standards.
-
-To run the pre-commit tool, follow these steps:
-
-1. Install pre-commit by running the following command: `poetry install`. It will not only install pre-commit but also install all the deps and dev-deps of project
-
-2. Once pre-commit is installed, navigate to the project's root directory.
-
-3. Run the command `pre-commit run --all-files`. This will execute the pre-commit hooks configured for this project against the modified files. If any issues are found, the pre-commit tool will provide feedback on how to resolve them. Make the necessary changes and re-run the pre-commit command until all issues are resolved.
-
-4. You can also install pre-commit as a git hook by executing `pre-commit install`. Every time you do a `git commit` pre-commit run automatically for you.
-
-### Docstrings
-
-All new functions and classes in `supervision` should include docstrings. This is a prerequisite for any new functions and classes to be added to the library.
-
-`supervision` adheres to the [Google Python docstring style](https://google.github.io/styleguide/pyguide.html#383-functions-and-methods). Please refer to the style guide while writing docstrings for your contribution.
-
-### Type checking
-
-So far, **there is no type checking with mypy**. See [issue](https://github.com/roboflow-ai/template-python/issues/4).
+Common types include:
+- feat: A new feature
+- fix: A bug fix
+- docs: Documentation only changes
+- style: Changes that do not affect the meaning of the code (white-space, formatting, etc)
+- refactor: A code change that neither fixes a bug nor adds a feature
+- perf: A code change that improves performance
+- test: Adding missing tests or correcting existing tests
+- chore: Changes to the build process or auxiliary tools and libraries

 Then, go back to your fork of the `supervision` repository, click "Pull Requests", and click "New Pull Request".

@@ -104,7 +123,75 @@ All pull requests will be reviewed by the maintainers of the project. We will pr

 PRs must pass all tests and linting requirements before they can be merged.

-## 📝 documentation
+## Installation for Contributors
+
+Before starting your work on the project, set up your development environment:
+
+1. Clone your fork of the project:
+   ```bash
+   git clone https://github.com/YOUR_USERNAME/supervision.git
+   cd supervision
+   ```
+   Replace `YOUR_USERNAME` with your GitHub username.
+
+2. Create and activate a virtual environment:
+   ```bash
+   python3 -m venv .venv
+   source .venv/bin/activate
+   ```
+
+3. Install Poetry:
+
+   Using pip:
+   ```bash
+   pip install -U pip setuptools
+   pip install poetry
+   ```
+
+   Or using pipx (recommended for global installation):
+   ```bash
+   pipx install poetry
+   ```
+
+4. Install project dependencies:
+   ```bash
+   poetry install
+   ```
+
+5. Run pytest to verify the setup:
+   ```bash
+   poetry run pytest
+   ```
+
+## 🎨 Code Style and Quality
+
+### Pre-commit tool
+
+This project uses the [pre-commit](https://pre-commit.com/) tool to maintain code quality and consistency. Before submitting a pull request or making any commits, it is important to run the pre-commit tool to ensure that your changes meet the project's guidelines.
+
+Furthermore, we have integrated a pre-commit GitHub Action into our workflow. This means that with every pull request opened, the pre-commit checks will be automatically enforced, streamlining the code review process and ensuring that all contributions adhere to our quality standards.
+
+To run the pre-commit tool, follow these steps:
+
+1. Install pre-commit by running the following command: `poetry install`. It will not only install pre-commit but also install all the deps and dev-deps of project
+
+2. Once pre-commit is installed, navigate to the project's root directory.
+
+3. Run the command `pre-commit run --all-files`. This will execute the pre-commit hooks configured for this project against the modified files. If any issues are found, the pre-commit tool will provide feedback on how to resolve them. Make the necessary changes and re-run the pre-commit command until all issues are resolved.
+
+4. You can also install pre-commit as a git hook by executing `pre-commit install`. Every time you do a `git commit` pre-commit run automatically for you.
+
+### Docstrings
+
+All new functions and classes in `supervision` should include docstrings. This is a prerequisite for any new functions and classes to be added to the library.
+
+`supervision` adheres to the [Google Python docstring style](https://google.github.io/styleguide/pyguide.html#383-functions-and-methods). Please refer to the style guide while writing docstrings for your contribution.
+
+### Type checking
+
+So far, **there is no type checking with mypy**. See [issue](https://github.com/roboflow-ai/template-python/issues/4).
+
+## 📝 Documentation

 The `supervision` documentation is stored in a folder called `docs`. The project documentation is built using `mkdocs`.

@@ -112,13 +199,13 @@ To run the documentation, install the project requirements with `poetry install

 You can learn more about mkdocs on the [mkdocs website](https://www.mkdocs.org/).

-## 🧑‍🍳 cookbooks
+## 🧑‍🍳 Cookbooks

 We are always looking for new examples and cookbooks to add to the `supervision`
 documentation. If you have a use case that you think would be helpful to others, please
 submit a PR with your example. Here are some guidelines for submitting a new example:

- Create a new notebook in the [`docs/nodebooks`](https://github.com/roboflow/supervision/tree/develop/docs/notebooks) folder.
+- Create a new notebook in the [`docs/notebooks`](https://github.com/roboflow/supervision/tree/develop/docs/notebooks) folder.
 - Add a link to the new notebook in [`docs/theme/cookbooks.html`](https://github.com/roboflow/supervision/blob/develop/docs/theme/cookbooks.html). Make sure to add the path to the new notebook, as well as a title, labels, author and supervision version.
 - Use the [Count Objects Crossing the Line](https://supervision.roboflow.com/develop/notebooks/count-objects-crossing-the-line/) example as a template for your new example.
 - Freeze the version of `supervision` you are using.
@@ -126,10 +213,10 @@ submit a PR with your example. Here are some guidelines for submitting a new exa
 - Notebook should be self-contained. If you rely on external data ( videos, images, etc.) or libraries, include download and installation commands in the notebook.
 - Annotate the code with appropriate comments, including links to the documentation describing each of the tools you have used.

-## 🧪 tests
+## 🧪 Tests

 [`pytests`](https://docs.pytest.org/en/7.1.x/) is used to run our tests.

-## 📄 license
+## 📄 License

 By contributing, you agree that your contributions will be licensed under an [MIT license](https://github.com/roboflow/supervision/blob/develop/LICENSE.md).
@@ -95,8 +95,8 @@ import supervision as sv
 image = cv2.imread(...)
 detections = sv.Detections(...)

-bounding_box_annotator = sv.BoundingBoxAnnotator()
-annotated_frame = bounding_box_annotator.annotate(
+box_annotator = sv.BoxAnnotator()
+annotated_frame = box_annotator.annotate(
    scene=image.copy(),
    detections=detections
 )
@@ -216,7 +216,7 @@ len(dataset)

 ## 🎬 tutorials

-Want to learn how to use Supervision? Explore our [how-to guides](https://supervision.roboflow.com/develop/how_to/detect_and_annotate/), [end-to-end examples](https://github.com/roboflow/supervision/tree/develop/examples), and [cookbooks](https://supervision.roboflow.com/develop/cookbooks/)!
+Want to learn how to use Supervision? Explore our [how-to guides](https://supervision.roboflow.com/develop/how_to/detect_and_annotate/), [end-to-end examples](https://github.com/roboflow/supervision/tree/develop/examples), [cheatsheet](https://roboflow.github.io/cheatsheet-supervision/), and [cookbooks](https://supervision.roboflow.com/develop/cookbooks/)!

 <br/>

@@ -1,8 +1,157 @@
+### 0.22.0 <small>Jul 12, 2024</small>
+
+- Added [#1326](https://github.com/roboflow/supervision/pull/1326): [`sv.DetectionsDataset`](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.DetectionDataset) and [`sv.ClassificationDataset`](https://supervision.roboflow.com/latest/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
+import roboflow
+from roboflow import Roboflow
+import supervision as sv
+
+roboflow.login()
+rf = Roboflow()
+
+project = rf.workspace(<WORKSPACE_ID>).project(<PROJECT_ID>)
+dataset = project.version(<PROJECT_VERSION>).download("coco")
+
+ds_train = sv.DetectionDataset.from_coco(
+    images_directory_path=f"{dataset.location}/train",
+    annotations_path=f"{dataset.location}/train/_annotations.coco.json",
+)
+
+path, image, annotation = ds_train[0]
+    # loads image on demand
+
+for path, image, annotation in ds_train:
+    # loads image on demand
+```
+
+- Added [#1296](https://github.com/roboflow/supervision/pull/1296): [`sv.Detections.from_lmm`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_lmm) now supports parsing results from the [Florence 2](https://huggingface.co/microsoft/Florence-2-large) model, extending the capability to handle outputs from this Large Multimodal Model (LMM). This includes detailed object detection, OCR with region proposals, segmentation, and more. Find out more in our [Colab notebook](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-florence-2-on-detection-dataset.ipynb).
+
+- Added [#1232](https://github.com/roboflow/supervision/pull/1232) to support keypoint detection with Mediapipe. Both [legacy](https://colab.research.google.com/github/googlesamples/mediapipe/blob/main/examples/pose_landmarker/python/%5BMediaPipe_Python_Tasks%5D_Pose_Landmarker.ipynb) and [modern](https://ai.google.dev/edge/mediapipe/solutions/vision/pose_landmarker/python) pipelines are supported. See [`sv.KeyPoints.from_mediapipe`](https://supervision.roboflow.com/latest/keypoint/core/#supervision.keypoint.core.KeyPoints.from_mediapipe) for more.
+
+- Added [#1316](https://github.com/roboflow/supervision/pull/1316): [`sv.KeyPoints.from_mediapipe`](https://supervision.roboflow.com/latest/keypoint/core/#supervision.keypoint.core.KeyPoints.from_mediapipe) extended to support FaceMesh from Mediapipe. This enhancement allows for processing both face landmarks from `FaceLandmarker`, and legacy results from `FaceMesh`.
+
+- Added [#1310](https://github.com/roboflow/supervision/pull/1310): [`sv.KeyPoints.from_detectron2`](https://supervision.roboflow.com/latest/keypoint/core/#supervision.keypoint.core.KeyPoints.from_detectron2) is a new `KeyPoints` method, adding support for extracting keypoints from the popular [Detectron 2](https://github.com/facebookresearch/detectron2) platform.
+
+- Added [#1300](https://github.com/roboflow/supervision/pull/1300): [`sv.Detections.from_detectron2`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_detectron2) now supports segmentation models detectron2. The resulting masks can be used with [`sv.MaskAnnotator`](https://supervision.roboflow.com/latest/annotators/#supervision.annotators.core.MaskAnnotator) for displaying annotations.
+
+```python
+import supervision as sv
+from detectron2 import model_zoo
+from detectron2.engine import DefaultPredictor
+from detectron2.config import get_cfg
+import cv2
+
+image = cv2.imread(<SOURCE_IMAGE_PATH>)
+cfg = get_cfg()
+cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
+cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
+predictor = DefaultPredictor(cfg)
+
+result = predictor(image)
+detections = sv.Detections.from_detectron2(result)
+
+mask_annotator = sv.MaskAnnotator()
+annotated_frame = mask_annotator.annotate(scene=image.copy(), detections=detections)
+```
+
+- 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/latest/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.
+
+```python
+import cv2
+import supervision as sv
+import
+
+image = cv2.imread(<SOURCE_IMAGE_PATH>)
+
+model = get_model(model_id="yolov8n-640")
+results = model.infer(image)[0]
+detections = sv.Detections.from_inference(results)
+
+rich_label_annotator = sv.RichLabelAnnotator(font_path=<TTF_FONT_PATH>)
+annotated_image = rich_label_annotator.annotate(scene=image.copy(), detections=detections)
+```
+
+- Added [#1227](https://github.com/roboflow/supervision/pull/1227): Added support for loading Oriented Bounding Boxes dataset in YOLO format.
+
+```python
+import supervision as sv
+
+train_ds = sv.DetectionDataset.from_yolo(
+    images_directory_path="/content/dataset/train/images",
+    annotations_directory_path="/content/dataset/train/labels",
+    data_yaml_path="/content/dataset/data.yaml",
+    is_obb=True
+)
+
+_, image, detections in train_ds[0]
+
+obb_annotator = OrientedBoxAnnotator()
+annotated_image = obb_annotator.annotate(scene=image.copy(), detections=detections)
+```
+
+- Fixed [#1312](https://github.com/roboflow/supervision/pull/1312): Fixed [`CropAnnotator`](https://supervision.roboflow.com/latest/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/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`LabelAnnotator`](https://supervision.roboflow.com/latest/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/latest/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).
+
+- Added [#1290](https://github.com/roboflow/supervision/pull/1290): Mostly an internal change, our file utility function now support both `str` and `pathlib` paths.
+
+- 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/latest/detection/utils/#supervision.detection.utils.xywh_to_xyxy) and [`xcycwh_to_xyxy`](https://supervision.roboflow.com/latest/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/latest/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/latest/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/latest/utils/video/#supervision.utils.video.FPSMonitor.fps) instead.
+
 ### 0.21.0 <small>Jun 5, 2024</small>

- Added [#500](https://github.com/roboflow/supervision/pull/500): [`sv.Detections.with_nmm`](https://supervision.roboflow.com/develop/detection/core/#supervision.detection.core.Detections.with_nmm) to perform non-maximum merging on the current set of object detections.
+- Added [#500](https://github.com/roboflow/supervision/pull/500): [`sv.Detections.with_nmm`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.with_nmm) to perform non-maximum merging on the current set of object detections.

- Added [#1221](https://github.com/roboflow/supervision/pull/1221): [`sv.Detections.from_lmm`](https://supervision.roboflow.com/develop/detection/core/#supervision.detection.core.Detections.from_lmm) allowing to parse Large Multimodal Model (LMM) text result into [`sv.Detections`](https://supervision.roboflow.com/develop/detection/core/) object. For now `from_lmm` supports only [PaliGemma](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-paligemma-on-detection-dataset.ipynb) result parsing.
+- Added [#1221](https://github.com/roboflow/supervision/pull/1221): [`sv.Detections.from_lmm`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_lmm) allowing to parse Large Multimodal Model (LMM) text result into [`sv.Detections`](https://supervision.roboflow.com/latest/detection/core/) object. For now `from_lmm` supports only [PaliGemma](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-paligemma-on-detection-dataset.ipynb) result parsing.

 ```python
 import supervision as sv
@@ -21,7 +170,7 @@ detections.class_id
 # array([0])
 ```

- Added [#1236](https://github.com/roboflow/supervision/pull/1236): [`sv.VertexLabelAnnotator`](https://supervision.roboflow.com/develop/keypoint/annotators/#supervision.keypoint.annotators.EdgeAnnotator.annotate) allowing to annotate every vertex of a keypoint skeleton with custom text and color.
+- Added [#1236](https://github.com/roboflow/supervision/pull/1236): [`sv.VertexLabelAnnotator`](https://supervision.roboflow.com/latest/keypoint/annotators/#supervision.keypoint.annotators.EdgeAnnotator.annotate) allowing to annotate every vertex of a keypoint skeleton with custom text and color.

 ```python
 import supervision as sv
@@ -164,8 +313,6 @@ with csv_sink:

 - Added [#819](https://github.com/roboflow/supervision/pull/819): [`sv.JSONSink`](/0.19.0/detection/tools/save_detections/#supervision.detection.tools.csv_sink.JSONSink) allowing for the straightforward saving of image, video, or stream inference results in a `.json` file.

-```python
-
 ```python
 import supervision as sv
 from ultralytics import YOLO
@@ -285,7 +432,6 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])

    `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.

 - 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).
@@ -294,7 +440,6 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])

    `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.

 ### 0.17.0 <small>December 06, 2023</small>
@@ -387,13 +532,12 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])

 - Fixed [#477](https://github.com/roboflow/supervision/pull/477): Poetry env definition allowing proper local installation.

- 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.
+- 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>

 - Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.BoundingBoxAnnotator`](/0.15.0/annotators/#supervision.annotators.core.BoundingBoxAnnotator) allowing to annotate images and videos with bounding boxes.
@@ -1,6 +1,5 @@
 ---
 comments: true
-status: new
 ---

 # Datasets Utils
@@ -3,17 +3,25 @@ comments: true
 status: deprecated
 ---

-These features are phased out due to better alternatives or potential issues in future versions. Deprecated functionalities are supported for **three subsequent releases**, providing time for users to transition to updated methods.
+# 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.

- [`Detections.from_froboflow`](detection/core.md/#supervision.detection.core.Detections.from_roboflow) is deprecated and will be removed in `supervision-0.22.0`. Use [`Detections.from_inference`](detection/core.md/#supervision.detection.core.Detections.from_inference) instead.
- The method `Color.white()` is deprecated and will be removed in `supervision-0.22.0`. Use the constant `Color.WHITE` instead.
- The method `Color.black()` is deprecated and will be removed in `supervision-0.22.0`. Use the constant `Color.BLACK` instead.
- The method `Color.red()` is deprecated and will be removed in `supervision-0.22.0`. Use the constant `Color.RED` instead.
- The method `Color.green()` is deprecated and will be removed in `supervision-0.22.0`. Use the constant `Color.GREEN` instead.
- The method `Color.blue()` is deprecated and will be removed in `supervision-0.22.0`. Use the constant `Color.BLUE` instead.
- The method [`ColorPalette.default()`](draw/color.md/#supervision.draw.color.ColorPalette.default) is deprecated and will be removed in `supervision-0.22.0`. Use the constant [`ColorPalette.DEFAULT`](draw/color.md/#supervision.draw.color.ColorPalette.DEFAULT) instead.
- `BoxAnnotator` is deprecated and will be removed in `supervision-0.22.0`. Use [`BoundingBoxAnnotator`](detection/annotators.md/#supervision.annotators.core.BoundingBoxAnnotator) and [`LabelAnnotator`](detection/annotators.md/#supervision.annotators.core.LabelAnnotator) instead.
- The method [`FPSMonitor.__call__`](utils/video.md/#supervision.utils.video.FPSMonitor.__call__) is deprecated and will be removed in `supervision-0.22.0`. Use the attribute [`FPSMonitor.fps`](utils/video.md/#supervision.utils.video.FPSMonitor.fps) instead.
 - The `track_buffer`, `track_thresh`, and `match_thresh` parameters in [`ByterTrack`](trackers.md/#supervision.tracker.byte_tracker.core.ByteTrack) are deprecated and will be removed in `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) is deprecated and will be removed in `supervision-0.23.0`. Use `triggering_anchors ` instead.
- The `frame_resolution_wh ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) is deprecated and will be removed in `supervision-0.24.0`.
+- The `triggering_position ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) will be removed in `supervision-0.23.0`. Use `triggering_anchors ` instead.
+- 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` and `ClassificationDataset` with parameter `images` as `Dict[str, np.ndarray]` will be removed in `supervision-0.26.0`. Please pass a list of paths `List[str]` instead.
+- The `DetectionDataset.images` property will be removed in `supervision-0.26.0`. Please loop over images with `for path, image, annotation in dataset:`, as that does not require loading all images into memory.
+- `BoundingBoxAnnotator` has been renamed to `BoxAnnotator` after the old implementation of `BoxAnnotator` has been removed. `BoundingBoxAnnotator` will be removed in `supervision-0.26.0`.
+
+# Removed
+
+- [`Detections.from_froboflow`](detection/core.md/#supervision.detection.core.Detections.from_roboflow) is removed as of `supervision-0.22.0`. Use [`Detections.from_inference`](detection/core.md/#supervision.detection.core.Detections.from_inference) instead.
+- The method `Color.white()` was removed as of `supervision-0.22.0`. Use the constant `Color.WHITE` instead.
+- The method `Color.black()` was removed as of `supervision-0.22.0`. Use the constant `Color.BLACK` instead.
+- The method `Color.red()` was removed as of `supervision-0.22.0`. Use the constant `Color.RED` instead.
+- The method `Color.green()` was removed as of `supervision-0.22.0`. Use the constant `Color.GREEN` instead.
+- The method `Color.blue()` was removed as of `supervision-0.22.0`. Use the constant `Color.BLUE` instead.
+- The method [`ColorPalette.default()`](draw/color.md/#supervision.draw.color.ColorPalette.default) was removed as of `supervision-0.22.0`. Use the constant [`ColorPalette.DEFAULT`](draw/color.md/#supervision.draw.color.ColorPalette.DEFAULT) instead.
+- `BoxAnnotator` was removed as of `supervision-0.22.0`, however `BoundingBoxAnnotator` was immediately renamed to `BoxAnnotator`. Use [`BoxAnnotator`](detection/annotators.md/#supervision.annotators.core.BoxAnnotator) and [`LabelAnnotator`](detection/annotators.md/#supervision.annotators.core.LabelAnnotator) instead of the old `BoxAnnotator`.
+- The method [`FPSMonitor.__call__`](utils/video.md/#supervision.utils.video.FPSMonitor.__call__) was removed as of `supervision-0.22.0`. Use the attribute [`FPSMonitor.fps`](utils/video.md/#supervision.utils.video.FPSMonitor.fps) instead.
@@ -1,10 +1,11 @@
 ---
 comments: true
+status: new
 ---

 # Annotators

-=== "BoundingBox"
+=== "Box"

    ```python
    import supervision as sv
@@ -12,8 +13,8 @@ comments: true
    image = ...
    detections = sv.Detections(...)

-    bounding_box_annotator = sv.BoundingBoxAnnotator()
-    annotated_frame = bounding_box_annotator.annotate(
+    box_annotator = sv.BoxAnnotator()
+    annotated_frame = box_annotator.annotate(
        scene=image.copy(),
        detections=detections
    )
@@ -299,7 +300,7 @@ comments: true
    ]

    rich_label_annotator = sv.RichLabelAnnotator(
-        font_path=".../font.ttf",
+        font_path="<TTF_FONT_PATH>",
        text_position=sv.Position.CENTER
    )
    annotated_frame = label_annotator.annotate(
@@ -433,10 +434,10 @@ comments: true
    </div>

 <div class="md-typeset">
-    <h2><a href="#supervision.annotators.core.BoundingBoxAnnotator">BoundingBoxAnnotator</a></h2>
+    <h2><a href="#supervision.annotators.core.BoxAnnotator">BoxAnnotator</a></h2>
 </div>

-:::supervision.annotators.core.BoundingBoxAnnotator
+:::supervision.annotators.core.BoxAnnotator

 <div class="md-typeset">
    <h2><a href="#supervision.annotators.core.RoundBoxAnnotator">RoundBoxAnnotator</a></h2>
@@ -1,6 +1,5 @@
 ---
 comments: true
-status: new
 ---

 # Double Detection Filter
@@ -1,6 +1,5 @@
 ---
 comments: true
-status: new
 ---

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

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

 # Detection Utils
@@ -80,6 +81,18 @@ comments: true
  <h2><a href="#supervision.detection.utils.contains_holes">contains_holes</a></h2>
 </div>

+:::supervision.detection.utils.xywh_to_xyxy
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.xywh_to_xyxy">xywh_to_xyxy</a></h2>
+</div>
+
+:::supervision.detection.utils.xcycwh_to_xyxy
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.xcycwh_to_xyxy">xcycwh_to_xyxy</a></h2>
+</div>
+
 :::supervision.detection.utils.contains_holes

 <div class="md-typeset">
@@ -135,7 +135,7 @@ You can load predictions from other computer vision frameworks and libraries usi

 ## Annotate Image with Detections

-Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoundingBoxAnnotator`](/latest/annotators/#supervision.annotators.core.BoundingBoxAnnotator) and [`sv.LabelAnnotator`](/latest/annotators/#supervision.annotators.core.LabelAnnotator) classes.
+Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoxAnnotator`](/latest/annotators/#supervision.annotators.core.BoxAnnotator) and [`sv.LabelAnnotator`](/latest/annotators/#supervision.annotators.core.LabelAnnotator) classes.

 === "Inference"

@@ -149,10 +149,10 @@ Finally, we can annotate the image with the predictions. Since we are working wi
    results = model.infer(image)[0]
    detections = sv.Detections.from_inference(results)

-    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
    label_annotator = sv.LabelAnnotator()

-    annotated_image = bounding_box_annotator.annotate(
+    annotated_image = box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections)
@@ -170,10 +170,10 @@ Finally, we can annotate the image with the predictions. Since we are working wi
    results = model(image)[0]
    detections = sv.Detections.from_ultralytics(results)

-    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
    label_annotator = sv.LabelAnnotator()

-    annotated_image = bounding_box_annotator.annotate(
+    annotated_image = box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections)
@@ -204,10 +204,10 @@ Finally, we can annotate the image with the predictions. Since we are working wi
        transformers_results=results,
        id2label=model.config.id2label)

-    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
    label_annotator = sv.LabelAnnotator()

-    annotated_image = bounding_box_annotator.annotate(
+    annotated_image = box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections)
@@ -233,7 +233,7 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
    results = model.infer(image)[0]
    detections = sv.Detections.from_inference(results)

-    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
    label_annotator = sv.LabelAnnotator()

    labels = [
@@ -242,7 +242,7 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
        in zip(detections['class_name'], detections.confidence)
    ]

-    annotated_image = bounding_box_annotator.annotate(
+    annotated_image = box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections, labels=labels)
@@ -260,7 +260,7 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
    results = model(image)[0]
    detections = sv.Detections.from_ultralytics(results)

-    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
    label_annotator = sv.LabelAnnotator()

    labels = [
@@ -269,7 +269,7 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
        in zip(detections['class_name'], detections.confidence)
    ]

-    annotated_image = bounding_box_annotator.annotate(
+    annotated_image = box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections, labels=labels)
@@ -300,7 +300,7 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
        transformers_results=results,
        id2label=model.config.id2label)

-    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
    label_annotator = sv.LabelAnnotator()

    labels = [
@@ -309,7 +309,7 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
        in zip(detections['class_name'], detections.confidence)
    ]

-    annotated_image = bounding_box_annotator.annotate(
+    annotated_image = box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections, labels=labels)
@@ -322,7 +322,7 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
 If you are running the segmentation model
 [`sv.MaskAnnotator`](/latest/annotators/#supervision.annotators.core.MaskAnnotator)
 is a drop-in replacement for
-[`sv.BoundingBoxAnnotator`](/latest/annotators/#supervision.annotators.core.BoundingBoxAnnotator)
+[`sv.BoxAnnotator`](/latest/annotators/#supervision.annotators.core.BoxAnnotator)
 that will allow you to draw masks instead of boxes.

 === "Inference"
@@ -1,6 +1,5 @@
 ---
 comments: true
-status: new
 ---

 # Detect Small Objects
@@ -32,10 +31,10 @@ size relative to the image resolution.
    results = model.infer(image)[0]
    detections = sv.Detections.from_inference(results)

-    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
    label_annotator = sv.LabelAnnotator()

-    annotated_image = bounding_box_annotator.annotate(
+    annotated_image = box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections)
@@ -53,10 +52,10 @@ size relative to the image resolution.
    results = model(image)[0]
    detections = sv.Detections.from_ultralytics(results)

-    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
    label_annotator = sv.LabelAnnotator()

-    annotated_image = bounding_box_annotator.annotate(
+    annotated_image = box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections)
@@ -85,7 +84,7 @@ size relative to the image resolution.
        outputs=outputs, target_sizes=target_size)[0]
    detections = sv.Detections.from_transformers(results)

-    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
    label_annotator = sv.LabelAnnotator()

    labels = [
@@ -94,7 +93,7 @@ size relative to the image resolution.
        in detections.class_id
    ]

-    annotated_image = bounding_box_annotator.annotate(
+    annotated_image = box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections, labels=labels)
@@ -120,10 +119,10 @@ is less effective for ultra-high-resolution images (4K and above).
    results = model.infer(image)[0]
    detections = sv.Detections.from_inference(results)

-    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
    label_annotator = sv.LabelAnnotator()

-    annotated_image = bounding_box_annotator.annotate(
+    annotated_image = box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections)
@@ -141,10 +140,10 @@ is less effective for ultra-high-resolution images (4K and above).
    results = model(image, imgsz=1280)[0]
    detections = sv.Detections.from_ultralytics(results)

-    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
    label_annotator = sv.LabelAnnotator()

-    annotated_image = bounding_box_annotator.annotate(
+    annotated_image = box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections)
@@ -180,10 +179,10 @@ objects within each, and aggregating the results.
    slicer = sv.InferenceSlicer(callback = callback)
    detections = slicer(image)

-    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
    label_annotator = sv.LabelAnnotator()

-    annotated_image = bounding_box_annotator.annotate(
+    annotated_image = box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections)
@@ -207,10 +206,10 @@ objects within each, and aggregating the results.
    slicer = sv.InferenceSlicer(callback = callback)
    detections = slicer(image)

-    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
    label_annotator = sv.LabelAnnotator()

-    annotated_image = bounding_box_annotator.annotate(
+    annotated_image = box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections)
@@ -248,7 +247,7 @@ objects within each, and aggregating the results.
    slicer = sv.InferenceSlicer(callback = callback)
    detections = slicer(image)

-    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    box_annotator = sv.BoxAnnotator()
    label_annotator = sv.LabelAnnotator()

    labels = [
@@ -257,7 +256,7 @@ objects within each, and aggregating the results.
        in detections.class_id
    ]

-    annotated_image = bounding_box_annotator.annotate(
+    annotated_image = box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections, labels=labels)
@@ -146,4 +146,13 @@ You can install `supervision` in a

    Master the techniques to selectively filter and focus on objects within a specific zone

+-   **Cheatsheet**
+
+    ***
+
+    Access a quick reference guide to the most common `supervision` functions
+
+    [:octicons-arrow-right-24: Cheatsheet](https://roboflow.github.io/cheatsheet-supervision/)
+
+
 </div>
@@ -1,6 +1,5 @@
 ---
 comments: true
-status: new
 ---

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

 # File Utils
@@ -11,7 +11,7 @@ from tqdm import tqdm

 import supervision as sv

-COLORS = sv.ColorPalette.default()
+COLORS = sv.ColorPalette.DEFAULT


 def load_zones_config(file_path: str) -> List[np.ndarray]:
@@ -9,7 +9,7 @@ from ultralytics import YOLO

 import supervision as sv

-COLORS = sv.ColorPalette.default()
+COLORS = sv.ColorPalette.DEFAULT


 def load_zones_config(file_path: str) -> List[np.ndarray]:
@@ -102,7 +102,7 @@ if __name__ == "__main__":
        resolution_wh=video_info.resolution_wh
    )
    text_scale = sv.calculate_optimal_text_scale(resolution_wh=video_info.resolution_wh)
-    bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=thickness)
+    box_annotator = sv.BoxAnnotator(thickness=thickness)
    label_annotator = sv.LabelAnnotator(
        text_scale=text_scale,
        text_thickness=thickness,
@@ -154,7 +154,7 @@ if __name__ == "__main__":
            annotated_frame = trace_annotator.annotate(
                scene=annotated_frame, detections=detections
            )
-            annotated_frame = bounding_box_annotator.annotate(
+            annotated_frame = box_annotator.annotate(
                scene=annotated_frame, detections=detections
            )
            annotated_frame = label_annotator.annotate(
@@ -80,7 +80,7 @@ if __name__ == "__main__":
        resolution_wh=video_info.resolution_wh
    )
    text_scale = sv.calculate_optimal_text_scale(resolution_wh=video_info.resolution_wh)
-    bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=thickness)
+    box_annotator = sv.BoxAnnotator(thickness=thickness)
    label_annotator = sv.LabelAnnotator(
        text_scale=text_scale,
        text_thickness=thickness,
@@ -132,7 +132,7 @@ if __name__ == "__main__":
            annotated_frame = trace_annotator.annotate(
                scene=annotated_frame, detections=detections
            )
-            annotated_frame = bounding_box_annotator.annotate(
+            annotated_frame = box_annotator.annotate(
                scene=annotated_frame, detections=detections
            )
            annotated_frame = label_annotator.annotate(
@@ -81,7 +81,7 @@ if __name__ == "__main__":
        resolution_wh=video_info.resolution_wh
    )
    text_scale = sv.calculate_optimal_text_scale(resolution_wh=video_info.resolution_wh)
-    bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=thickness)
+    box_annotator = sv.BoxAnnotator(thickness=thickness)
    label_annotator = sv.LabelAnnotator(
        text_scale=text_scale,
        text_thickness=thickness,
@@ -132,7 +132,7 @@ if __name__ == "__main__":
            annotated_frame = trace_annotator.annotate(
                scene=annotated_frame, detections=detections
            )
-            annotated_frame = bounding_box_annotator.annotate(
+            annotated_frame = box_annotator.annotate(
                scene=annotated_frame, detections=detections
            )
            annotated_frame = label_annotator.annotate(
@@ -93,7 +93,7 @@ class VideoProcessor:
        self.zones_in = initiate_polygon_zones(ZONE_IN_POLYGONS, [sv.Position.CENTER])
        self.zones_out = initiate_polygon_zones(ZONE_OUT_POLYGONS, [sv.Position.CENTER])

-        self.bounding_box_annotator = sv.BoundingBoxAnnotator(color=COLORS)
+        self.box_annotator = sv.BoxAnnotator(color=COLORS)
        self.label_annotator = sv.LabelAnnotator(
            color=COLORS, text_color=sv.Color.BLACK
        )
@@ -134,9 +134,7 @@ class VideoProcessor:

        labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]
        annotated_frame = self.trace_annotator.annotate(annotated_frame, detections)
-        annotated_frame = self.bounding_box_annotator.annotate(
-            annotated_frame, detections
-        )
+        annotated_frame = self.box_annotator.annotate(annotated_frame, detections)
        annotated_frame = self.label_annotator.annotate(
            annotated_frame, detections, labels
        )
@@ -90,7 +90,7 @@ class VideoProcessor:
        self.zones_in = initiate_polygon_zones(ZONE_IN_POLYGONS, [sv.Position.CENTER])
        self.zones_out = initiate_polygon_zones(ZONE_OUT_POLYGONS, [sv.Position.CENTER])

-        self.bounding_box_annotator = sv.BoundingBoxAnnotator(color=COLORS)
+        self.box_annotator = sv.BoxAnnotator(color=COLORS)
        self.label_annotator = sv.LabelAnnotator(
            color=COLORS, text_color=sv.Color.BLACK
        )
@@ -131,9 +131,7 @@ class VideoProcessor:

        labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]
        annotated_frame = self.trace_annotator.annotate(annotated_frame, detections)
-        annotated_frame = self.bounding_box_annotator.annotate(
-            annotated_frame, detections
-        )
+        annotated_frame = self.box_annotator.annotate(annotated_frame, detections)
        annotated_frame = self.label_annotator.annotate(
            annotated_frame, detections, labels
        )
@@ -75,6 +75,7 @@ nav:
        - Geometry: utils/geometry.md
    - Assets: assets.md
  - Cookbooks: cookbooks.md
+  - Cheatsheet: https://roboflow.github.io/cheatsheet-supervision/
  - Contribute:
    - Contributing: contributing.md
    - Code of Conduct: code_of_conduct.md
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.

 [[package]]
 name = "anyio"
@@ -336,13 +336,13 @@ test = ["flake8", "isort", "pytest"]

 [[package]]
 name = "certifi"
-version = "2024.6.2"
+version = "2024.7.4"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.6.2-py3-none-any.whl", hash = "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"},
-    {file = "certifi-2024.6.2.tar.gz", hash = "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516"},
+    {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
+    {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
 ]

 [[package]]
@@ -1710,13 +1710,13 @@ files = [

 [[package]]
 name = "jupytext"
-version = "1.16.2"
+version = "1.16.3"
 description = "Jupyter notebooks as Markdown documents, Julia, Python or R scripts"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupytext-1.16.2-py3-none-any.whl", hash = "sha256:197a43fef31dca612b68b311e01b8abd54441c7e637810b16b6cb8f2ab66065e"},
-    {file = "jupytext-1.16.2.tar.gz", hash = "sha256:8627dd9becbbebd79cc4a4ed4727d89d78e606b4b464eab72357b3b029023a14"},
+    {file = "jupytext-1.16.3-py3-none-any.whl", hash = "sha256:870e0d7a716dcb1303df6ad1cec65e3315a20daedd808a55cb3dae2d56e4ed20"},
+    {file = "jupytext-1.16.3.tar.gz", hash = "sha256:1ebac990461dd9f477ff7feec9e3003fa1acc89f3c16ba01b73f79fd76f01a98"},
 ]

 [package.dependencies]
@@ -1728,11 +1728,11 @@ pyyaml = "*"
 tomli = {version = "*", markers = "python_version < \"3.11\""}

 [package.extras]
-dev = ["autopep8", "black", "flake8", "gitpython", "ipykernel", "isort", "jupyter-fs (<0.4.0)", "jupyter-server (!=2.11)", "nbconvert", "pre-commit", "pytest", "pytest-cov (>=2.6.1)", "pytest-randomly", "pytest-xdist", "sphinx-gallery (<0.8)"]
+dev = ["autopep8", "black", "flake8", "gitpython", "ipykernel", "isort", "jupyter-fs (>=1.0)", "jupyter-server (!=2.11)", "nbconvert", "pre-commit", "pytest", "pytest-cov (>=2.6.1)", "pytest-randomly", "pytest-xdist", "sphinx-gallery (<0.8)"]
 docs = ["myst-parser", "sphinx", "sphinx-copybutton", "sphinx-rtd-theme"]
 test = ["pytest", "pytest-randomly", "pytest-xdist"]
 test-cov = ["ipykernel", "jupyter-server (!=2.11)", "nbconvert", "pytest", "pytest-cov (>=2.6.1)", "pytest-randomly", "pytest-xdist"]
-test-external = ["autopep8", "black", "flake8", "gitpython", "ipykernel", "isort", "jupyter-fs (<0.4.0)", "jupyter-server (!=2.11)", "nbconvert", "pre-commit", "pytest", "pytest-randomly", "pytest-xdist", "sphinx-gallery (<0.8)"]
+test-external = ["autopep8", "black", "flake8", "gitpython", "ipykernel", "isort", "jupyter-fs (>=1.0)", "jupyter-server (!=2.11)", "nbconvert", "pre-commit", "pytest", "pytest-randomly", "pytest-xdist", "sphinx-gallery (<0.8)"]
 test-functional = ["pytest", "pytest-randomly", "pytest-xdist"]
 test-integration = ["ipykernel", "jupyter-server (!=2.11)", "nbconvert", "pytest", "pytest-randomly", "pytest-xdist"]
 test-ui = ["calysto-bash"]
@@ -3714,29 +3714,29 @@ files = [

 [[package]]
 name = "ruff"
-version = "0.5.0"
+version = "0.5.1"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "ruff-0.5.1-py3-none-win_amd64.whl", hash = "sha256:b1789bf2cd3d1b5a7d38397cac1398ddf3ad7f73f4de01b1e913e2abc7dfc51d"},
+    {file = "ruff-0.5.1-py3-none-win_arm64.whl", hash = "sha256:2875b7596a740cbbd492f32d24be73e545a4ce0a3daf51e4f4e609962bfd3cd2"},
+    {file = "ruff-0.5.1.tar.gz", hash = "sha256:3164488aebd89b1745b47fd00604fb4358d774465f20d1fcd907f9c0fc1b0655"},
 ]

 [[package]]
@@ -4000,30 +4000,30 @@ files = [

 [[package]]
 name = "tox"
-version = "4.15.1"
+version = "4.16.0"
 description = "tox is a generic virtualenv management and test command line tool"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tox-4.15.1-py3-none-any.whl", hash = "sha256:f00a5dc4222b358e69694e47e3da0227ac41253509bca9f45aa8f012053e8d9d"},
-    {file = "tox-4.15.1.tar.gz", hash = "sha256:53a092527d65e873e39213ebd4bd027a64623320b6b0326136384213f95b7076"},
+    {file = "tox-4.16.0-py3-none-any.whl", hash = "sha256:61e101061b977b46cf00093d4319438055290ad0009f84497a07bf2d2d7a06d0"},
+    {file = "tox-4.16.0.tar.gz", hash = "sha256:43499656f9949edb681c0f907f86fbfee98677af9919d8b11ae5ad77cb800748"},
 ]

 [package.dependencies]
-cachetools = ">=5.3.2"
+cachetools = ">=5.3.3"
 chardet = ">=5.2"
 colorama = ">=0.4.6"
-filelock = ">=3.13.1"
-packaging = ">=23.2"
-platformdirs = ">=4.1"
-pluggy = ">=1.3"
-pyproject-api = ">=1.6.1"
+filelock = ">=3.15.4"
+packaging = ">=24.1"
+platformdirs = ">=4.2.2"
+pluggy = ">=1.5"
+pyproject-api = ">=1.7.1"
 tomli = {version = ">=2.0.1", markers = "python_version < \"3.11\""}
-virtualenv = ">=20.25"
+virtualenv = ">=20.26.3"

 [package.extras]
-docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-argparse-cli (>=1.11.1)", "sphinx-autodoc-typehints (>=1.25.2)", "sphinx-copybutton (>=0.5.2)", "sphinx-inline-tabs (>=2023.4.21)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.11)"]
-testing = ["build[virtualenv] (>=1.0.3)", "covdefaults (>=2.3)", "detect-test-pollution (>=1.2)", "devpi-process (>=1)", "diff-cover (>=8.0.2)", "distlib (>=0.3.8)", "flaky (>=3.7)", "hatch-vcs (>=0.4)", "hatchling (>=1.21)", "psutil (>=5.9.7)", "pytest (>=7.4.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-xdist (>=3.5)", "re-assert (>=1.1)", "time-machine (>=2.13)", "wheel (>=0.42)"]
+docs = ["furo (>=2024.5.6)", "sphinx (>=7.3.7)", "sphinx-argparse-cli (>=1.16)", "sphinx-autodoc-typehints (>=2.2.2)", "sphinx-copybutton (>=0.5.2)", "sphinx-inline-tabs (>=2023.4.21)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.11)"]
+testing = ["build[virtualenv] (>=1.2.1)", "covdefaults (>=2.3)", "detect-test-pollution (>=1.2)", "devpi-process (>=1)", "diff-cover (>=9.1)", "distlib (>=0.3.8)", "flaky (>=3.8.1)", "hatch-vcs (>=0.4)", "hatchling (>=1.25)", "psutil (>=6)", "pytest (>=8.2.2)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-xdist (>=3.6.1)", "re-assert (>=1.1)", "setuptools (>=70.2)", "time-machine (>=2.14.2)", "wheel (>=0.43)"]

 [[package]]
 name = "tqdm"
@@ -1,18 +1,26 @@
 [tool.poetry]
 name = "supervision"
-version = "0.22.0rc1"
+version = "0.22.0"
 description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
 authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
 maintainers = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
 readme = "README.md"
 license = "MIT"
-packages = [{include = "supervision"}]
+packages = [{ include = "supervision" }]
 homepage = "https://github.com/roboflow/supervision"
 repository = "https://github.com/roboflow/supervision"
 documentation = "https://github.com/roboflow/supervision/blob/main/README.md"
-keywords = ["machine-learning", "deep-learning", "vision", "ML", "DL", "AI", "Roboflow"]
+keywords = [
+    "machine-learning",
+    "deep-learning",
+    "vision",
+    "ML",
+    "DL",
+    "AI",
+    "Roboflow",
+]

-classifiers=[
+classifiers = [
    'Development Status :: 4 - Beta',
    'Intended Audience :: Developers',
    'Intended Audience :: Education',
@@ -27,7 +35,7 @@ classifiers=[
    'Typing :: Typed',
    'Operating System :: Microsoft :: Windows',
    'Operating System :: POSIX :: Linux',
-    'Operating System :: MacOS'
+    'Operating System :: MacOS',
 ]

 [tool.poetry.dependencies]
@@ -35,7 +43,7 @@ python = "^3.8"
 numpy = ">=1.21.2"
 scipy = [
    { version = "1.10.0", python = "<3.9" },
-    { version = "^1.10.0", python = ">=3.9" }
+    { version = "^1.10.0", python = ">=3.9" },
 ]
 matplotlib = ">=3.6.0"
 pyyaml = ">=5.3"
@@ -48,7 +56,7 @@ pillow = ">=9.4"

 [tool.poetry.extras]
 desktop = ["opencv-python"]
-assets = ["requests","tqdm"]
+assets = ["requests", "tqdm"]

 [tool.poetry.group.dev.dependencies]
 twine = "^5.1.1"
@@ -65,13 +73,13 @@ jupytext = "^1.16.1"
 nbconvert = "^7.14.2"
 docutils = [
    { version = "^0.20.1", python = "<3.9" },
-    { version = "^0.21.1", python = ">=3.9" }
+    { version = "^0.21.1", python = ">=3.9" },
 ]


 [tool.poetry.group.docs.dependencies]
-mkdocs-material = {extras = ["imaging"], version = "^9.5.5"}
-mkdocstrings = {extras = ["python"], version = ">=0.20,<0.26"}
+mkdocs-material = { extras = ["imaging"], version = "^9.5.5" }
+mkdocstrings = { extras = ["python"], version = ">=0.20,<0.26" }
 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
@@ -79,8 +87,6 @@ mkdocs-jupyter = "^0.24.3"
 mkdocs-git-committers-plugin-2 = "^2.2.3"
 mkdocs-git-revision-date-localized-plugin = "^1.2.4"

-
-
 [tool.isort]
 line_length = 88
 profile = "black"
@@ -93,7 +99,6 @@ tests = ["B201", "B301", "B318", "B314", "B303", "B413", "B412", "B410"]
 check = true
 imports = ["cv2", "supervision"]

-
 [tool.black]
 target-version = ["py38"]
 line-length = 88
@@ -152,7 +157,52 @@ indent-width = 4
 select = ["E", "F"]
 ignore = []
 # Allow autofix for all enabled rules (when `--fix`) is provided.
-fixable = ["A", "B", "C", "D", "E", "F", "G", "I", "N", "Q", "S", "T", "W", "ANN", "ARG", "BLE", "COM", "DJ", "DTZ", "EM", "ERA", "EXE", "FBT", "ICN", "INP", "ISC", "NPY", "PD", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "RET", "RSE", "RUF", "SIM", "SLF", "TCH", "TID", "TRY", "UP", "YTT"]
+fixable = [
+    "A",
+    "B",
+    "C",
+    "D",
+    "E",
+    "F",
+    "G",
+    "I",
+    "N",
+    "Q",
+    "S",
+    "T",
+    "W",
+    "ANN",
+    "ARG",
+    "BLE",
+    "COM",
+    "DJ",
+    "DTZ",
+    "EM",
+    "ERA",
+    "EXE",
+    "FBT",
+    "ICN",
+    "INP",
+    "ISC",
+    "NPY",
+    "PD",
+    "PGH",
+    "PIE",
+    "PL",
+    "PT",
+    "PTH",
+    "PYI",
+    "RET",
+    "RSE",
+    "RUF",
+    "SIM",
+    "SLF",
+    "TCH",
+    "TID",
+    "TRY",
+    "UP",
+    "YTT",
+]
 unfixable = []
 # Allow unused variables when underscore-prefixed.
 dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
@@ -167,7 +217,7 @@ docstring-quotes = "double"
 convention = "google"

 [tool.ruff.lint.per-file-ignores]
-"__init__.py" = ["E402","F401"]
+"__init__.py" = ["E402", "F401"]

 [tool.ruff.lint.mccabe]
 # Flag errors (`C901`) whenever the complexity level exceeds 5.
@@ -190,7 +240,7 @@ line-ending = "auto"
 include-package-data = false

 [tool.setuptools.packages.find]
-exclude = ["docs*", "test*","examples*"]
+exclude = ["docs*", "test*", "examples*"]

 [build-system]
 requires = ["poetry-core"]
@@ -0,0 +1,19 @@
+# Release Process
+
+This doc outlines how supervision is released into production.
+
+It assumes you already have the code changes, as well as a draft of the release notes.
+
+1. Make sure you have all required changes were merged into `develop`.
+2. Create and merge a PR, merging `develop` into `main`, containing:
+   - A commit that updates the project version in `pyproject.toml`.
+   - All changes made during the release.
+3. Tag the commit with the new supervision version.
+   - Upon pushing the tag, the [PyPi](https://pypi.org/project/supervision/) should update to the new version. Check this!
+4. Open and merge a PR, merging `main` into `develop`.
+5. Update the docs by running the [Supervision Release Documentation Workflow 📚](https://github.com/roboflow/supervision/actions/workflows/publish-release-docs.yml) workflow from GitHub.
+    - Select the `main` branch from the dropdown.
+6. Create a release on GitHub.
+   - Go to releases
+   - Assign the release notes to the tag created in step 3.
+   - Publish the release.
@@ -9,6 +9,7 @@ except importlib_metadata.PackageNotFoundError:
 from supervision.annotators.core import (
    BlurAnnotator,
    BoundingBoxAnnotator,
+    BoxAnnotator,
    BoxCornerAnnotator,
    CircleAnnotator,
    ColorAnnotator,
@@ -36,7 +37,6 @@ from supervision.dataset.core import (
    DetectionDataset,
 )
 from supervision.dataset.utils import mask_to_rle, rle_to_mask
-from supervision.detection.annotate import BoxAnnotator
 from supervision.detection.core import Detections
 from supervision.detection.line_zone import LineZone, LineZoneAnnotator
 from supervision.detection.lmm import LMM
@@ -67,6 +67,8 @@ from supervision.detection.utils import (
    polygon_to_mask,
    polygon_to_xyxy,
    scale_boxes,
+    xcycwh_to_xyxy,
+    xywh_to_xyxy,
 )
 from supervision.draw.color import Color, ColorPalette
 from supervision.draw.utils import (
@@ -23,8 +23,94 @@ from supervision.utils.conversion import (
    ensure_pil_image_for_annotation,
 )
 from supervision.utils.image import crop_image, overlay_image, scale_image
+from supervision.utils.internal import deprecated


+class BoxAnnotator(BaseAnnotator):
+    """
+    A class for drawing bounding boxes on an image using provided detections.
+    """
+
+    def __init__(
+        self,
+        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
+        thickness: int = 2,
+        color_lookup: ColorLookup = ColorLookup.CLASS,
+    ):
+        """
+        Args:
+            color (Union[Color, ColorPalette]): The color or color palette to use for
+                annotating detections.
+            thickness (int): Thickness of the bounding box lines.
+            color_lookup (str): Strategy for mapping colors to annotations.
+                Options are `INDEX`, `CLASS`, `TRACK`.
+        """
+        self.color: Union[Color, ColorPalette] = color
+        self.thickness: int = thickness
+        self.color_lookup: ColorLookup = color_lookup
+
+    @ensure_cv2_image_for_annotation
+    def annotate(
+        self,
+        scene: ImageType,
+        detections: Detections,
+        custom_color_lookup: Optional[np.ndarray] = None,
+    ) -> ImageType:
+        """
+        Annotates the given scene with bounding boxes based on the provided detections.
+
+        Args:
+            scene (ImageType): The image where bounding boxes will be drawn. `ImageType`
+            is a flexible type, accepting either `numpy.ndarray` or `PIL.Image.Image`.
+            detections (Detections): Object detections to annotate.
+            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
+                Allows to override the default color mapping strategy.
+
+        Returns:
+            The annotated image, matching the type of `scene` (`numpy.ndarray`
+                or `PIL.Image.Image`)
+
+        Example:
+            ```python
+            import supervision as sv
+
+            image = ...
+            detections = sv.Detections(...)
+
+            box_annotator = sv.BoxAnnotator()
+            annotated_frame = box_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
+            ```
+
+        ![bounding-box-annotator-example](https://media.roboflow.com/
+        supervision-annotator-examples/bounding-box-annotator-example-purple.png)
+        """
+        for detection_idx in range(len(detections)):
+            x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
+            color = resolve_color(
+                color=self.color,
+                detections=detections,
+                detection_idx=detection_idx,
+                color_lookup=self.color_lookup
+                if custom_color_lookup is None
+                else custom_color_lookup,
+            )
+            cv2.rectangle(
+                img=scene,
+                pt1=(x1, y1),
+                pt2=(x2, y2),
+                color=color.as_bgr(),
+                thickness=self.thickness,
+            )
+        return scene
+
+
+@deprecated(
+    "`BoundingBoxAnnotator` is deprecated and has been renamed to `BoxAnnotator`."
+    " `BoundingBoxAnnotator` will be removed in supervision-0.26.0."
+)
 class BoundingBoxAnnotator(BaseAnnotator):
    """
    A class for drawing bounding boxes on an image using provided detections.
@@ -3,8 +3,9 @@ from __future__ import annotations
 import os
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
+from itertools import chain
 from pathlib import Path
-from typing import Dict, Iterator, List, Optional, Tuple
+from typing import Dict, Iterator, List, Optional, Tuple, Union

 import cv2
 import numpy as np
@@ -31,9 +32,10 @@ from supervision.dataset.utils import (
    train_test_split,
 )
 from supervision.detection.core import Detections
+from supervision.utils.internal import deprecated, warn_deprecated
+from supervision.utils.iterables import find_duplicates


-@dataclass
 class BaseDataset(ABC):
    @abstractmethod
    def __len__(self) -> int:
@@ -46,30 +48,95 @@ class BaseDataset(ABC):
        pass


-@dataclass
 class DetectionDataset(BaseDataset):
    """
-    Dataclass containing information about object detection dataset.
+    Contains information about a detection dataset. Handles lazy image loading
+    and annotation retrieval, dataset splitting, conversions into multiple
+    formats.

    Attributes:
        classes (List[str]): List containing dataset class names.
-        images (Dict[str, np.ndarray]): Dictionary mapping image name to image.
+        images (Union[List[str], Dict[str, np.ndarray]]):
+            Accepts a list of image paths, or dictionaries of loaded cv2 images
+            with paths as keys. If you pass a list of paths, the dataset will
+            lazily load images on demand, which is much more memory-efficient.
        annotations (Dict[str, Detections]): Dictionary mapping
-            image name to annotations.
+            image path to annotations. The dictionary keys match
+            match the keys in `images` or entries in the list of
+            image paths.
    """

-    classes: List[str]
-    images: Dict[str, np.ndarray]
-    annotations: Dict[str, Detections]
+    def __init__(
+        self,
+        classes: List[str],
+        images: Union[List[str], Dict[str, np.ndarray]],
+        annotations: Dict[str, Detections],
+    ) -> None:
+        self.classes = classes
+
+        if set(images) != set(annotations):
+            raise ValueError(
+                "The keys of the images and annotations dictionaries must match."
+            )
+        self.annotations = annotations
+
+        # Eliminate duplicates while preserving order
+        self.image_paths = list(dict.fromkeys(images))
+
+        self._images_in_memory: Dict[str, np.ndarray] = {}
+        if isinstance(images, dict):
+            self._images_in_memory = images
+            warn_deprecated(
+                "Passing a `Dict[str, np.ndarray]` into `DetectionDataset` is "
+                "deprecated and will be removed in `supervision-0.26.0`. Use "
+                "a list of paths `List[str]` instead."
+            )
+            # TODO: when supervision-0.26.0 is released, and Dict[str, np.ndarray]
+            #       for images is no longer supported, also simplify the rest of
+            #       the code. E.g. list(images) is no longer needed, and merge can
+            #       be simplified.
+
+    @property
+    @deprecated(
+        "`DetectionDataset.images` property is deprecated and will be removed in "
+        "`supervision-0.26.0`. Iterate with `for path, image, annotation in dataset:` "
+        "instead."
+    )
+    def images(self) -> Dict[str, np.ndarray]:
+        """
+        Load all images to memory and return them as a dictionary.
+
+        !!! warning
+
+            Only use this when you need all images at once.
+            It is much more memory-efficient to initialize dataset with
+            image paths and use `for path, image, annotation in dataset:`.
+        """
+        if self._images_in_memory:
+            return self._images_in_memory
+
+        images = {image_path: cv2.imread(image_path) for image_path in self.image_paths}
+        return images
+
+    def _get_image(self, image_path: str) -> np.ndarray:
+        """Assumes that image is in dataset"""
+        if self._images_in_memory:
+            return self._images_in_memory[image_path]
+        return cv2.imread(image_path)

    def __len__(self) -> int:
-        """
-        Return the number of images in the dataset.
+        return len(self._images_in_memory) or len(self.image_paths)

-        Returns:
-            int: The number of images.
+    def __getitem__(self, i: int) -> Tuple[str, np.ndarray, Detections]:
        """
-        return len(self.images)
+        Returns:
+            Tuple[str, np.ndarray, Detections]: The image path, image data,
+                and its corresponding annotation at index i.
+        """
+        image_path = self.image_paths[i]
+        image = self._get_image(image_path)
+        annotation = self.annotations[image_path]
+        return image_path, image, annotation

    def __iter__(self) -> Iterator[Tuple[str, np.ndarray, Detections]]:
        """
@@ -77,25 +144,33 @@ class DetectionDataset(BaseDataset):

        Yields:
            Iterator[Tuple[str, np.ndarray, Detections]]:
-                An iterator that yields tuples containing the image name,
+                An iterator that yields tuples containing the image path,
                the image data, and its corresponding annotation.
        """
-        for image_name, image in self.images.items():
-            yield image_name, image, self.annotations.get(image_name, None)
+        for i in range(len(self)):
+            image_path, image, annotation = self[i]
+            yield image_path, image, annotation

-    def __eq__(self, other):
+    def __eq__(self, other) -> bool:
        if not isinstance(other, DetectionDataset):
            return False

        if set(self.classes) != set(other.classes):
            return False

-        for key in self.images:
-            if not np.array_equal(self.images[key], other.images[key]):
-                return False
-            if not self.annotations[key] == other.annotations[key]:
+        if self.image_paths != other.image_paths:
+            return False
+
+        if self._images_in_memory or other._images_in_memory:
+            if not np.array_equal(
+                list(self._images_in_memory.values()),
+                list(other._images_in_memory.values()),
+            ):
                return False

+        if self.annotations != other.annotations:
+            return False
+
        return True

    def split(
@@ -127,26 +202,129 @@ class DetectionDataset(BaseDataset):
            ```
        """

-        image_names = list(self.images.keys())
-        train_names, test_names = train_test_split(
-            data=image_names,
+        train_paths, test_paths = train_test_split(
+            data=self.image_paths,
            train_ratio=split_ratio,
            random_state=random_state,
            shuffle=shuffle,
        )

+        train_input: Union[List[str], Dict[str, np.ndarray]]
+        test_input: Union[List[str], Dict[str, np.ndarray]]
+        if self._images_in_memory:
+            train_input = {path: self._images_in_memory[path] for path in train_paths}
+            test_input = {path: self._images_in_memory[path] for path in test_paths}
+        else:
+            train_input = train_paths
+            test_input = test_paths
+        train_annotations = {path: self.annotations[path] for path in train_paths}
+        test_annotations = {path: self.annotations[path] for path in test_paths}
+
        train_dataset = DetectionDataset(
            classes=self.classes,
-            images={name: self.images[name] for name in train_names},
-            annotations={name: self.annotations[name] for name in train_names},
+            images=train_input,
+            annotations=train_annotations,
        )
        test_dataset = DetectionDataset(
            classes=self.classes,
-            images={name: self.images[name] for name in test_names},
-            annotations={name: self.annotations[name] for name in test_names},
+            images=test_input,
+            annotations=test_annotations,
        )
        return train_dataset, test_dataset

+    @classmethod
+    def merge(cls, dataset_list: List[DetectionDataset]) -> DetectionDataset:
+        """
+        Merge a list of `DetectionDataset` objects into a single
+            `DetectionDataset` object.
+
+        This method takes a list of `DetectionDataset` objects and combines
+        their respective fields (`classes`, `images`,
+        `annotations`) into a single `DetectionDataset` object.
+
+        Args:
+            dataset_list (List[DetectionDataset]): A list of `DetectionDataset`
+                objects to merge.
+
+        Returns:
+            (DetectionDataset): A single `DetectionDataset` object containing
+            the merged data from the input list.
+
+        Examples:
+            ```python
+            import supervision as sv
+
+            ds_1 = sv.DetectionDataset(...)
+            len(ds_1)
+            # 100
+            ds_1.classes
+            # ['dog', 'person']
+
+            ds_2 = sv.DetectionDataset(...)
+            len(ds_2)
+            # 200
+            ds_2.classes
+            # ['cat']
+
+            ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
+            len(ds_merged)
+            # 300
+            ds_merged.classes
+            # ['cat', 'dog', 'person']
+            ```
+        """
+
+        def is_in_memory(dataset: DetectionDataset) -> bool:
+            return len(dataset._images_in_memory) > 0 or len(dataset.image_paths) == 0
+
+        def is_lazy(dataset: DetectionDataset) -> bool:
+            return len(dataset._images_in_memory) == 0
+
+        all_in_memory = all([is_in_memory(dataset) for dataset in dataset_list])
+        all_lazy = all([is_lazy(dataset) for dataset in dataset_list])
+        if not all_in_memory and not all_lazy:
+            raise ValueError(
+                "Merging lazy and in-memory DetectionDatasets is not supported."
+            )
+
+        images_in_memory = {}
+        for dataset in dataset_list:
+            images_in_memory.update(dataset._images_in_memory)
+
+        image_paths = list(
+            chain.from_iterable(dataset.image_paths for dataset in dataset_list)
+        )
+        image_paths_unique = list(dict.fromkeys(image_paths))
+        if len(image_paths) != len(image_paths_unique):
+            duplicates = find_duplicates(image_paths)
+            raise ValueError(
+                f"Image paths {duplicates} are not unique across datasets."
+            )
+        image_paths = image_paths_unique
+
+        classes = merge_class_lists(
+            class_lists=[dataset.classes for dataset in dataset_list]
+        )
+
+        annotations = {}
+        for dataset in dataset_list:
+            annotations.update(dataset.annotations)
+        for dataset in dataset_list:
+            class_index_mapping = build_class_index_mapping(
+                source_classes=dataset.classes, target_classes=classes
+            )
+            for image_path in dataset.image_paths:
+                annotations[image_path] = map_detections_class_id(
+                    source_to_target_mapping=class_index_mapping,
+                    detections=annotations[image_path],
+                )
+
+        return cls(
+            classes=classes,
+            images=images_in_memory or image_paths,
+            annotations=annotations,
+        )
+
    def as_pascal_voc(
        self,
        images_directory_path: Optional[str] = None,
@@ -180,25 +358,22 @@ class DetectionDataset(BaseDataset):
        """
        if images_directory_path:
            save_dataset_images(
-                images_directory_path=images_directory_path, images=self.images
+                dataset=self,
+                images_directory_path=images_directory_path,
            )
        if annotations_directory_path:
            Path(annotations_directory_path).mkdir(parents=True, exist_ok=True)
-
-        for image_path, image in self.images.items():
-            detections = self.annotations[image_path]
-
-            if annotations_directory_path:
+            for image_path, image, annotations in self:
                annotation_name = Path(image_path).stem
                annotations_path = os.path.join(
                    annotations_directory_path, f"{annotation_name}.xml"
                )
                image_name = Path(image_path).name
                pascal_voc_xml = detections_to_pascal_voc(
-                    detections=detections,
+                    detections=annotations,
                    classes=self.classes,
                    filename=image_name,
-                    image_shape=image.shape,
+                    image_shape=image.shape,  # type: ignore
                    min_image_area_percentage=min_image_area_percentage,
                    max_image_area_percentage=max_image_area_percentage,
                    approximation_percentage=approximation_percentage,
@@ -251,13 +426,15 @@ class DetectionDataset(BaseDataset):
            ```
        """

-        classes, images, annotations = load_pascal_voc_annotations(
+        classes, image_paths, annotations = load_pascal_voc_annotations(
            images_directory_path=images_directory_path,
            annotations_directory_path=annotations_directory_path,
            force_masks=force_masks,
        )

-        return DetectionDataset(classes=classes, images=images, annotations=annotations)
+        return DetectionDataset(
+            classes=classes, images=image_paths, annotations=annotations
+        )

    @classmethod
    def from_yolo(
@@ -311,14 +488,16 @@ class DetectionDataset(BaseDataset):
            # ['dog', 'person']
            ```
        """
-        classes, images, annotations = load_yolo_annotations(
+        classes, image_paths, annotations = load_yolo_annotations(
            images_directory_path=images_directory_path,
            annotations_directory_path=annotations_directory_path,
            data_yaml_path=data_yaml_path,
            force_masks=force_masks,
            is_obb=is_obb,
        )
-        return DetectionDataset(classes=classes, images=images, annotations=annotations)
+        return DetectionDataset(
+            classes=classes, images=image_paths, annotations=annotations
+        )

    def as_yolo(
        self,
@@ -359,13 +538,12 @@ class DetectionDataset(BaseDataset):
        """
        if images_directory_path is not None:
            save_dataset_images(
-                images_directory_path=images_directory_path, images=self.images
+                dataset=self, images_directory_path=images_directory_path
            )
        if annotations_directory_path is not None:
            save_yolo_annotations(
+                dataset=self,
                annotations_directory_path=annotations_directory_path,
-                images=self.images,
-                annotations=self.annotations,
                min_image_area_percentage=min_image_area_percentage,
                max_image_area_percentage=max_image_area_percentage,
                approximation_percentage=approximation_percentage,
@@ -469,103 +647,134 @@ class DetectionDataset(BaseDataset):
        """
        if images_directory_path is not None:
            save_dataset_images(
-                images_directory_path=images_directory_path, images=self.images
+                dataset=self, images_directory_path=images_directory_path
            )
        if annotations_path is not None:
            save_coco_annotations(
+                dataset=self,
                annotation_path=annotations_path,
-                images=self.images,
-                annotations=self.annotations,
-                classes=self.classes,
                min_image_area_percentage=min_image_area_percentage,
                max_image_area_percentage=max_image_area_percentage,
                approximation_percentage=approximation_percentage,
            )

-    @classmethod
-    def merge(cls, dataset_list: List[DetectionDataset]) -> DetectionDataset:
-        """
-        Merge a list of `DetectionDataset` objects into a single
-            `DetectionDataset` object.
-
-        This method takes a list of `DetectionDataset` objects and combines
-        their respective fields (`classes`, `images`,
-        `annotations`) into a single `DetectionDataset` object.
-
-        Args:
-            dataset_list (List[DetectionDataset]): A list of `DetectionDataset`
-                objects to merge.
-
-        Returns:
-            (DetectionDataset): A single `DetectionDataset` object containing
-            the merged data from the input list.
-
-        Examples:
-            ```python
-            import supervision as sv
-
-            ds_1 = sv.DetectionDataset(...)
-            len(ds_1)
-            # 100
-            ds_1.classes
-            # ['dog', 'person']
-
-            ds_2 = sv.DetectionDataset(...)
-            len(ds_2)
-            # 200
-            ds_2.classes
-            # ['cat']
-
-            ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
-            len(ds_merged)
-            # 300
-            ds_merged.classes
-            # ['cat', 'dog', 'person']
-            ```
-        """
-        merged_images, merged_annotations = {}, {}
-        class_lists = [dataset.classes for dataset in dataset_list]
-        merged_classes = merge_class_lists(class_lists=class_lists)
-
-        for dataset in dataset_list:
-            class_index_mapping = build_class_index_mapping(
-                source_classes=dataset.classes, target_classes=merged_classes
-            )
-            for image_name, image, detections in dataset:
-                if image_name in merged_annotations:
-                    raise ValueError(
-                        f"Image name {image_name} is not unique across datasets."
-                    )
-
-                merged_images[image_name] = image
-                merged_annotations[image_name] = map_detections_class_id(
-                    source_to_target_mapping=class_index_mapping,
-                    detections=detections,
-                )
-
-        return cls(
-            classes=merged_classes, images=merged_images, annotations=merged_annotations
-        )
-

@dataclass
 class ClassificationDataset(BaseDataset):
    """
-    Dataclass containing information about a classification dataset.
+    Contains information about a classification dataset, handles lazy image
+    loading, dataset splitting.

    Attributes:
        classes (List[str]): List containing dataset class names.
-        images (Dict[str, np.ndarray]): Dictionary mapping image name to image.
-        annotations (Dict[str, Detections]): Dictionary mapping
+        images (Union[List[str], Dict[str, np.ndarray]]):
+            List of image paths or dictionary mapping image name to image data.
+        annotations (Dict[str, Classifications]): Dictionary mapping
            image name to annotations.
    """

-    classes: List[str]
-    images: Dict[str, np.ndarray]
-    annotations: Dict[str, Classifications]
+    def __init__(
+        self,
+        classes: List[str],
+        images: Union[List[str], Dict[str, np.ndarray]],
+        annotations: Dict[str, Classifications],
+    ) -> None:
+        self.classes = classes
+
+        if set(images) != set(annotations):
+            raise ValueError(
+                "The keys of the images and annotations dictionaries must match."
+            )
+        self.annotations = annotations
+
+        # Eliminate duplicates while preserving order
+        self.image_paths = list(dict.fromkeys(images))
+
+        self._images_in_memory: Dict[str, np.ndarray] = {}
+        if isinstance(images, dict):
+            self._images_in_memory = images
+            warn_deprecated(
+                "Passing a `Dict[str, np.ndarray]` into `ClassificationDataset` is "
+                "deprecated and will be removed in a future release. Use "
+                "a list of paths `List[str]` instead."
+            )
+
+    @property
+    @deprecated(
+        "`DetectionDataset.images` property is deprecated and will be removed in "
+        "`supervision-0.26.0`. Iterate with `for path, image, annotation in dataset:` "
+        "instead."
+    )
+    def images(self) -> Dict[str, np.ndarray]:
+        """
+        Load all images to memory and return them as a dictionary.
+
+        !!! warning
+
+            Only use this when you need all images at once.
+            It is much more memory-efficient to initialize dataset with
+            image paths and use `for path, image, annotation in dataset:`.
+        """
+        if self._images_in_memory:
+            return self._images_in_memory
+
+        images = {image_path: cv2.imread(image_path) for image_path in self.image_paths}
+        return images
+
+    def _get_image(self, image_path: str) -> np.ndarray:
+        """Assumes that image is in dataset"""
+        if self._images_in_memory:
+            return self._images_in_memory[image_path]
+        return cv2.imread(image_path)

    def __len__(self) -> int:
-        return len(self.images)
+        return len(self._images_in_memory) or len(self.image_paths)
+
+    def __getitem__(self, i: int) -> Tuple[str, np.ndarray, Classifications]:
+        """
+        Returns:
+            Tuple[str, np.ndarray, Classifications]: The image path, image data,
+                and its corresponding annotation at index i.
+        """
+        image_path = self.image_paths[i]
+        image = self._get_image(image_path)
+        annotation = self.annotations[image_path]
+        return image_path, image, annotation
+
+    def __iter__(self) -> Iterator[Tuple[str, np.ndarray, Classifications]]:
+        """
+        Iterate over the images and annotations in the dataset.
+
+        Yields:
+            Iterator[Tuple[str, np.ndarray, Detections]]:
+                An iterator that yields tuples containing the image path,
+                the image data, and its corresponding annotation.
+        """
+        for i in range(len(self)):
+            image_path, image, annotation = self[i]
+            yield image_path, image, annotation
+
+    def __eq__(self, other) -> bool:
+        if not isinstance(other, ClassificationDataset):
+            return False
+
+        if set(self.classes) != set(other.classes):
+            return False
+
+        if self.image_paths != other.image_paths:
+            return False
+
+        if self._images_in_memory or other._images_in_memory:
+            if not np.array_equal(
+                list(self._images_in_memory.values()),
+                list(other._images_in_memory.values()),
+            ):
+                return False
+
+        if self.annotations != other.annotations:
+            return False
+
+        return True

    def split(
        self, split_ratio=0.8, random_state=None, shuffle: bool = True
@@ -595,24 +804,35 @@ class ClassificationDataset(BaseDataset):
            # (700, 300)
            ```
        """
-        image_names = list(self.images.keys())
-        train_names, test_names = train_test_split(
-            data=image_names,
+        train_paths, test_paths = train_test_split(
+            data=self.image_paths,
            train_ratio=split_ratio,
            random_state=random_state,
            shuffle=shuffle,
        )

+        train_input: Union[List[str], Dict[str, np.ndarray]]
+        test_input: Union[List[str], Dict[str, np.ndarray]]
+        if self._images_in_memory:
+            train_input = {path: self._images_in_memory[path] for path in train_paths}
+            test_input = {path: self._images_in_memory[path] for path in test_paths}
+        else:
+            train_input = train_paths
+            test_input = test_paths
+        train_annotations = {path: self.annotations[path] for path in train_paths}
+        test_annotations = {path: self.annotations[path] for path in test_paths}
+
        train_dataset = ClassificationDataset(
            classes=self.classes,
-            images={name: self.images[name] for name in train_names},
-            annotations={name: self.annotations[name] for name in train_names},
+            images=train_input,
+            annotations=train_annotations,
        )
        test_dataset = ClassificationDataset(
            classes=self.classes,
-            images={name: self.images[name] for name in test_names},
-            annotations={name: self.annotations[name] for name in test_names},
+            images=test_input,
+            annotations=test_annotations,
        )
+
        return train_dataset, test_dataset

    def as_folder_structure(self, root_directory_path: str) -> None:
@@ -628,18 +848,16 @@ class ClassificationDataset(BaseDataset):
        for class_name in self.classes:
            os.makedirs(os.path.join(root_directory_path, class_name), exist_ok=True)

-        for image_path in self.images:
-            classification = self.annotations[image_path]
-            image = self.images[image_path]
-            image_name = Path(image_path).name
+        for image_save_path, image, annotation in self:
+            image_name = Path(image_save_path).name
            class_id = (
-                classification.class_id[0]
-                if classification.confidence is None
-                else classification.get_top_k(1)[0][0]
+                annotation.class_id[0]
+                if annotation.confidence is None
+                else annotation.get_top_k(1)[0][0]
            )
            class_name = self.classes[class_id]
-            image_path = os.path.join(root_directory_path, class_name, image_name)
-            cv2.imwrite(image_path, image)
+            image_save_path = os.path.join(root_directory_path, class_name, image_name)
+            cv2.imwrite(image_save_path, image)

    @classmethod
    def from_folder_structure(cls, root_directory_path: str) -> ClassificationDataset:
@@ -672,7 +890,7 @@ class ClassificationDataset(BaseDataset):
        classes = os.listdir(root_directory_path)
        classes = sorted(set(classes))

-        images = {}
+        image_paths = []
        annotations = {}

        for class_name in classes:
@@ -680,13 +898,13 @@ class ClassificationDataset(BaseDataset):

            for image in os.listdir(os.path.join(root_directory_path, class_name)):
                image_path = str(os.path.join(root_directory_path, class_name, image))
-                images[image_path] = cv2.imread(image_path)
+                image_paths.append(image_path)
                annotations[image_path] = Classifications(
                    class_id=np.array([class_id]),
                )

        return cls(
            classes=classes,
-            images=images,
+            images=image_paths,
            annotations=annotations,
        )
@@ -1,9 +1,8 @@
 import os
 from datetime import datetime
 from pathlib import Path
-from typing import Dict, List, Tuple
+from typing import TYPE_CHECKING, Dict, List, Tuple

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

@@ -21,6 +20,9 @@ from supervision.detection.utils import (
 )
 from supervision.utils.file import read_json_file, save_json_file

+if TYPE_CHECKING:
+    from supervision.dataset.core import DetectionDataset
+

 def coco_categories_to_classes(coco_categories: List[dict]) -> List[str]:
    return [
@@ -161,7 +163,7 @@ def load_coco_annotations(
    images_directory_path: str,
    annotations_path: str,
    force_masks: bool = False,
-) -> Tuple[List[str], Dict[str, np.ndarray], Dict[str, Detections]]:
+) -> Tuple[List[str], List[str], Dict[str, Detections]]:
    coco_data = read_json_file(file_path=annotations_path)
    classes = coco_categories_to_classes(coco_categories=coco_data["categories"])
    class_index_mapping = build_coco_class_index_mapping(
@@ -172,7 +174,7 @@ def load_coco_annotations(
        coco_annotations=coco_data["annotations"]
    )

-    images = {}
+    images = []
    annotations = {}

    for coco_image in coco_images:
@@ -184,7 +186,6 @@ def load_coco_annotations(
        image_annotations = coco_annotations_groups.get(coco_image["id"], [])
        image_path = os.path.join(images_directory_path, image_name)

-        image = cv2.imread(image_path)
        annotation = coco_annotations_to_detections(
            image_annotations=image_annotations,
            resolution_wh=(image_width, image_height),
@@ -195,23 +196,20 @@ def load_coco_annotations(
            detections=annotation,
        )

-        images[image_path] = image
+        images.append(image_path)
        annotations[image_path] = annotation

    return classes, images, annotations


 def save_coco_annotations(
+    dataset: "DetectionDataset",
    annotation_path: str,
-    images: Dict[str, np.ndarray],
-    annotations: Dict[str, Detections],
-    classes: List[str],
    min_image_area_percentage: float = 0.0,
    max_image_area_percentage: float = 1.0,
    approximation_percentage: float = 0.75,
 ) -> None:
    Path(annotation_path).parent.mkdir(parents=True, exist_ok=True)
-    info = {}
    licenses = [
        {
            "id": 1,
@@ -222,10 +220,10 @@ def save_coco_annotations(

    coco_annotations = []
    coco_images = []
-    coco_categories = classes_to_coco_categories(classes=classes)
+    coco_categories = classes_to_coco_categories(classes=dataset.classes)

    image_id, annotation_id = 1, 1
-    for image_path, image in images.items():
+    for image_path, image, annotation in dataset:
        image_height, image_width, _ = image.shape
        image_name = f"{Path(image_path).stem}{Path(image_path).suffix}"
        coco_image = {
@@ -238,10 +236,8 @@ def save_coco_annotations(
        }

        coco_images.append(coco_image)
-        detections = annotations[image_path]
-
        coco_annotation, annotation_id = detections_to_coco_annotations(
-            detections=detections,
+            detections=annotation,
            image_id=image_id,
            annotation_id=annotation_id,
            min_image_area_percentage=min_image_area_percentage,
@@ -253,7 +249,7 @@ def save_coco_annotations(
        image_id += 1

    annotation_dict = {
-        "info": info,
+        "info": {},
        "licenses": licenses,
        "categories": coco_categories,
        "images": coco_images,
@@ -5,7 +5,8 @@ from xml.etree.ElementTree import Element, SubElement

 import cv2
 import numpy as np
-from defusedxml.ElementTree import fromstring, parse, tostring
+from defusedxml.ElementTree import parse, tostring
+from defusedxml.minidom import parseString

 from supervision.dataset.utils import approximate_mask_with_polygons
 from supervision.detection.core import Detections
@@ -129,8 +130,7 @@ def detections_to_pascal_voc(
            annotation.append(next_object)

    # Generate XML string
-    xml_string = fromstring(tostring(annotation)).toprettyxml(indent="  ")
-
+    xml_string = parseString(tostring(annotation)).toprettyxml(indent="  ")
    return xml_string


@@ -138,7 +138,7 @@ def load_pascal_voc_annotations(
    images_directory_path: str,
    annotations_directory_path: str,
    force_masks: bool = False,
-) -> Tuple[List[str], Dict[str, np.ndarray], Dict[str, Detections]]:
+) -> Tuple[List[str], List[str], Dict[str, Detections]]:
    """
    Loads PASCAL VOC XML annotations and returns the image name,
        a Detections instance, and a list of class names.
@@ -151,44 +151,39 @@ def load_pascal_voc_annotations(
            annotations, regardless of whether they are present.

    Returns:
-        Tuple[List[str], Dict[str, np.ndarray], Dict[str, Detections]]: A tuple
-            containing a list of class names,
-            a dictionary with image names as keys and
-            images as values, and a dictionary with image names as
-            keys and corresponding Detections instances as values.
+        Tuple[List[str], List[str], Dict[str, Detections]]: A tuple with a list
+            of class names, a list of paths to images, and a dictionary with image
+            paths as keys and corresponding Detections instances as values.
    """

-    image_paths = list_files_with_extensions(
-        directory=images_directory_path, extensions=["jpg", "jpeg", "png"]
-    )
+    image_paths = [
+        str(path)
+        for path in list_files_with_extensions(
+            directory=images_directory_path, extensions=["jpg", "jpeg", "png"]
+        )
+    ]

-    classes = []
-    images = {}
+    classes: List[str] = []
    annotations = {}

    for image_path in image_paths:
-        image_name = Path(image_path).stem
-        image_path = str(image_path)
-        image = cv2.imread(image_path)
-
-        annotation_path = os.path.join(annotations_directory_path, f"{image_name}.xml")
+        image_stem = Path(image_path).stem
+        annotation_path = os.path.join(annotations_directory_path, f"{image_stem}.xml")
        if not os.path.exists(annotation_path):
-            images[image_path] = image
            annotations[image_path] = Detections.empty()
            continue

        tree = parse(annotation_path)
        root = tree.getroot()

+        image = cv2.imread(image_path)
        resolution_wh = (image.shape[1], image.shape[0])
        annotation, classes = detections_from_xml_obj(
            root, classes, resolution_wh, force_masks
        )
-
-        images[image_path] = image
        annotations[image_path] = annotation

-    return classes, images, annotations
+    return classes, image_paths, annotations


 def detections_from_xml_obj(
@@ -1,6 +1,6 @@
 import os
 from pathlib import Path
-from typing import Dict, List, Optional, Tuple
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple

 import cv2
 import numpy as np
@@ -17,6 +17,9 @@ from supervision.utils.file import (
    save_yaml_file,
 )

+if TYPE_CHECKING:
+    from supervision.dataset.core import DetectionDataset
+

 def _parse_box(values: List[str]) -> np.ndarray:
    x_center, y_center, width, height = values
@@ -124,7 +127,7 @@ def load_yolo_annotations(
    data_yaml_path: str,
    force_masks: bool = False,
    is_obb: bool = False,
-) -> Tuple[List[str], Dict[str, np.ndarray], Dict[str, Detections]]:
+) -> Tuple[List[str], List[str], Dict[str, Detections]]:
    """
    Loads YOLO annotations and returns class names, images,
        and their corresponding detections.
@@ -142,30 +145,29 @@ def load_yolo_annotations(
            where pairs of [x, y] are box corners.

    Returns:
-        Tuple[List[str], Dict[str, np.ndarray], Dict[str, Detections]]:
+        Tuple[List[str], List[str], Dict[str, Detections]]:
            A tuple containing a list of class names, a dictionary with
            image names as keys and images as values, and a dictionary
            with image names as keys and corresponding Detections instances as values.
    """
-    image_paths = list_files_with_extensions(
-        directory=images_directory_path, extensions=["jpg", "jpeg", "png"]
-    )
+    image_paths = [
+        str(path)
+        for path in list_files_with_extensions(
+            directory=images_directory_path, extensions=["jpg", "jpeg", "png"]
+        )
+    ]

    classes = _extract_class_names(file_path=data_yaml_path)
-    images = {}
    annotations = {}

    for image_path in image_paths:
        image_stem = Path(image_path).stem
-        image_path = str(image_path)
-        image = cv2.imread(image_path)
-
        annotation_path = os.path.join(annotations_directory_path, f"{image_stem}.txt")
        if not os.path.exists(annotation_path):
-            images[image_path] = image
            annotations[image_path] = Detections.empty()
            continue

+        image = cv2.imread(image_path)
        lines = read_txt_file(file_path=annotation_path, skip_empty=True)
        h, w, _ = image.shape
        resolution_wh = (w, h)
@@ -178,9 +180,8 @@ def load_yolo_annotations(
            with_masks=with_masks,
            is_obb=is_obb,
        )
-        images[image_path] = image
        annotations[image_path] = annotation
-    return classes, images, annotations
+    return classes, image_paths, annotations


 def object_to_yolo(
@@ -214,6 +215,9 @@ def detections_to_yolo_annotations(
 ) -> List[str]:
    annotation = []
    for xyxy, mask, _, class_id, _, _ in detections:
+        if class_id is None:
+            raise ValueError("Class ID is required for YOLO annotations.")
+
        if mask is not None:
            polygons = approximate_mask_with_polygons(
                mask=mask,
@@ -239,24 +243,22 @@ def detections_to_yolo_annotations(


 def save_yolo_annotations(
+    dataset: "DetectionDataset",
    annotations_directory_path: str,
-    images: Dict[str, np.ndarray],
-    annotations: Dict[str, Detections],
    min_image_area_percentage: float = 0.0,
    max_image_area_percentage: float = 1.0,
    approximation_percentage: float = 0.75,
 ) -> None:
    Path(annotations_directory_path).mkdir(parents=True, exist_ok=True)
-    for image_path, image in images.items():
-        detections = annotations[image_path]
+    for image_path, image, annotation in dataset:
        image_name = Path(image_path).name
        yolo_annotations_name = _image_name_to_annotation_name(image_name=image_name)
        yolo_annotations_path = os.path.join(
            annotations_directory_path, yolo_annotations_name
        )
        lines = detections_to_yolo_annotations(
-            detections=detections,
-            image_shape=image.shape,
+            detections=annotation,
+            image_shape=image.shape,  # type: ignore
            min_image_area_percentage=min_image_area_percentage,
            max_image_area_percentage=max_image_area_percentage,
            approximation_percentage=approximation_percentage,
@@ -1,8 +1,9 @@
 import copy
 import os
 import random
+import shutil
 from pathlib import Path
-from typing import Dict, List, Optional, Tuple, TypeVar, Union
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, TypeVar, Union

 import cv2
 import numpy as np
@@ -15,6 +16,9 @@ from supervision.detection.utils import (
    mask_to_polygons,
 )

+if TYPE_CHECKING:
+    from supervision.dataset.core import DetectionDataset
+
 T = TypeVar("T")


@@ -59,6 +63,7 @@ def merge_class_lists(class_lists: List[List[str]]) -> List[str]:
 def build_class_index_mapping(
    source_classes: List[str], target_classes: List[str]
 ) -> Dict[int, int]:
+    """Returns the index map of source classes -> target classes."""
    index_mapping = {}

    for i, class_name in enumerate(source_classes):
@@ -94,14 +99,16 @@ def map_detections_class_id(


 def save_dataset_images(
-    images_directory_path: str, images: Dict[str, np.ndarray]
+    dataset: "DetectionDataset", images_directory_path: str
 ) -> None:
    Path(images_directory_path).mkdir(parents=True, exist_ok=True)
-
-    for image_path, image in images.items():
-        image_name = Path(image_path).name
-        target_image_path = os.path.join(images_directory_path, image_name)
-        cv2.imwrite(target_image_path, image)
+    for image_path in dataset.image_paths:
+        final_path = os.path.join(images_directory_path, Path(image_path).name)
+        if image_path in dataset._images_in_memory:
+            image = dataset._images_in_memory[image_path]
+            cv2.imwrite(final_path, image)
+        else:
+            shutil.copyfile(image_path, final_path)


 def train_test_split(
@@ -1,155 +0,0 @@
-from typing import List, Optional, Union
-
-import cv2
-
-from supervision.annotators.base import ImageType
-from supervision.detection.core import Detections
-from supervision.draw.color import Color, ColorPalette
-from supervision.utils.conversion import ensure_cv2_image_for_annotation
-from supervision.utils.internal import deprecated
-
-
-class BoxAnnotator:
-    """
-    A class for drawing bounding boxes on an image using detections provided.
-
-    Attributes:
-        color (Union[Color, ColorPalette]): The color to draw the bounding box,
-            can be a single color or a color palette
-        thickness (int): The thickness of the bounding box lines, default is 2
-        text_color (Color): The color of the text on the bounding box, default is white
-        text_scale (float): The scale of the text on the bounding box, default is 0.5
-        text_thickness (int): The thickness of the text on the bounding box,
-            default is 1
-        text_padding (int): The padding around the text on the bounding box,
-            default is 5
-
-    """
-
-    def __init__(
-        self,
-        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
-        thickness: int = 2,
-        text_color: Color = Color.BLACK,
-        text_scale: float = 0.5,
-        text_thickness: int = 1,
-        text_padding: int = 10,
-    ):
-        self.color: Union[Color, ColorPalette] = color
-        self.thickness: int = thickness
-        self.text_color: Color = text_color
-        self.text_scale: float = text_scale
-        self.text_thickness: int = text_thickness
-        self.text_padding: int = text_padding
-
-    @deprecated(
-        "`BoxAnnotator` is deprecated and will be removed in "
-        "`supervision-0.22.0`. Use `BoundingBoxAnnotator` and `LabelAnnotator` instead"
-    )
-    @ensure_cv2_image_for_annotation
-    def annotate(
-        self,
-        scene: ImageType,
-        detections: Detections,
-        labels: Optional[List[str]] = None,
-        skip_label: bool = False,
-    ) -> ImageType:
-        """
-        Draws bounding boxes on the frame using the detections provided.
-
-        Args:
-            scene (ImageType): The image on which the bounding boxes will be drawn.
-                `ImageType` is a flexible type, accepting either `numpy.ndarray`
-                or `PIL.Image.Image`.
-            detections (Detections): The detections for which the
-                bounding boxes will be drawn
-            labels (Optional[List[str]]): An optional list of labels
-                corresponding to each detection. If `labels` are not provided,
-                corresponding `class_id` will be used as label.
-            skip_label (bool): Is set to `True`, skips bounding box label annotation.
-        Returns:
-            ImageType: The image with the bounding boxes drawn on it, matching the
-                type of `scene` (`numpy.ndarray` or `PIL.Image.Image`)
-
-        Example:
-            ```python
-            import supervision as sv
-
-            classes = ['person', ...]
-            image = ...
-            detections = sv.Detections(...)
-
-            box_annotator = sv.BoxAnnotator()
-            labels = [
-                f"{classes[class_id]} {confidence:0.2f}"
-                for _, _, confidence, class_id, _ in detections
-            ]
-            annotated_frame = box_annotator.annotate(
-                scene=image.copy(),
-                detections=detections,
-                labels=labels
-            )
-            ```
-        """
-        font = cv2.FONT_HERSHEY_SIMPLEX
-        for i in range(len(detections)):
-            x1, y1, x2, y2 = detections.xyxy[i].astype(int)
-            class_id = (
-                detections.class_id[i] if detections.class_id is not None else None
-            )
-            idx = class_id if class_id is not None else i
-            color = (
-                self.color.by_idx(idx)
-                if isinstance(self.color, ColorPalette)
-                else self.color
-            )
-            cv2.rectangle(
-                img=scene,
-                pt1=(x1, y1),
-                pt2=(x2, y2),
-                color=color.as_bgr(),
-                thickness=self.thickness,
-            )
-            if skip_label:
-                continue
-
-            text = (
-                f"{class_id}"
-                if (labels is None or len(detections) != len(labels))
-                else labels[i]
-            )
-
-            text_width, text_height = cv2.getTextSize(
-                text=text,
-                fontFace=font,
-                fontScale=self.text_scale,
-                thickness=self.text_thickness,
-            )[0]
-
-            text_x = x1 + self.text_padding
-            text_y = y1 - self.text_padding
-
-            text_background_x1 = x1
-            text_background_y1 = y1 - 2 * self.text_padding - text_height
-
-            text_background_x2 = x1 + 2 * self.text_padding + text_width
-            text_background_y2 = y1
-
-            cv2.rectangle(
-                img=scene,
-                pt1=(text_background_x1, text_background_y1),
-                pt2=(text_background_x2, text_background_y2),
-                color=color.as_bgr(),
-                thickness=cv2.FILLED,
-            )
-            cv2.putText(
-                img=scene,
-                text=text,
-                org=(text_x, text_y),
-                fontFace=font,
-                fontScale=self.text_scale,
-                color=self.text_color.as_rgb(),
-                thickness=self.text_thickness,
-                lineType=cv2.LINE_AA,
-            )
-        return scene
@@ -30,7 +30,7 @@ from supervision.detection.utils import (
    xywh_to_xyxy,
 )
 from supervision.geometry.core import Position
-from supervision.utils.internal import deprecated, get_instance_variables
+from supervision.utils.internal import get_instance_variables
 from supervision.validators import validate_detections_fields


@@ -608,45 +608,6 @@ class Detections:
            data=data,
        )

-    @classmethod
-    @deprecated(
-        "`Detections.from_roboflow` is deprecated and will be removed in "
-        "`supervision-0.22.0`. Use `Detections.from_inference` instead."
-    )
-    def from_roboflow(cls, roboflow_result: Union[dict, Any]) -> Detections:
-        """
-        !!! failure "Deprecated"
-
-            `Detections.from_roboflow` is deprecated and will be removed in
-            `supervision-0.22.0`. Use `Detections.from_inference` instead.
-
-        Create a Detections object from the [Roboflow](https://roboflow.com/)
-            API inference result or the [Inference](https://inference.roboflow.com/)
-            package results.
-
-        Args:
-            roboflow_result (dict): The result from the
-                Roboflow API containing predictions.
-
-        Returns:
-            (Detections): A Detections object containing the bounding boxes, class IDs,
-                and confidences of the predictions.
-
-        Example:
-            ```python
-            import cv2
-            import supervision as sv
-            from inference import get_model
-
-            image = cv2.imread(<SOURCE_IMAGE_PATH>)
-            model = get_model(model_id="yolov8s-640")
-
-            result = model.infer(image)[0]
-            detections = sv.Detections.from_roboflow(result)
-            ```
-        """
-        return cls.from_inference(roboflow_result)
-
    @classmethod
    def from_sam(cls, sam_result: List[dict]) -> Detections:
        """
@@ -686,7 +647,7 @@ class Detections:
        if np.asarray(xywh).shape[0] == 0:
            return cls.empty()

-        xyxy = xywh_to_xyxy(boxes_xywh=xywh)
+        xyxy = xywh_to_xyxy(xywh=xywh)
        return cls(xyxy=xyxy, mask=mask)

    @classmethod
@@ -174,7 +174,9 @@ def from_florence_2(
            match is not None
        ), f"Expected string to end in location tags, but got {result}"

+        w, h = resolution_wh
        xyxy = np.array([match.groups()], dtype=np.float32)
+        xyxy *= np.array([w, h, w, h]) / 1000
        result_string = result[: match.start()]
        labels = np.array([result_string])
        return xyxy, labels, None, None
@@ -41,7 +41,7 @@ class DetectionsSmoother:
        tracker = sv.ByteTrack(frame_rate=video_info.fps)
        smoother = sv.DetectionsSmoother()

-        annotator = sv.BoundingBoxAnnotator()
+        box_annotator = sv.BoxAnnotator()

        with sv.VideoSink(<TARGET_FILE_PATH>, video_info=video_info) as sink:
            for frame in frame_generator:
@@ -50,7 +50,7 @@ class DetectionsSmoother:
                detections = tracker.update_with_detections(detections)
                detections = smoother.update_with_detections(detections)

-                annotated_frame = bounding_box_annotator.annotate(frame.copy(), detections)
+                annotated_frame = box_annotator.annotate(frame.copy(), detections)
                sink.write_frame(annotated_frame)
        ```
    """  # noqa: E501 // docs
@@ -228,10 +228,78 @@ def pad_boxes(xyxy: np.ndarray, px: int, py: Optional[int] = None) -> np.ndarray
    return result


-def xywh_to_xyxy(boxes_xywh: np.ndarray) -> np.ndarray:
-    xyxy = boxes_xywh.copy()
-    xyxy[:, 2] = boxes_xywh[:, 0] + boxes_xywh[:, 2]
-    xyxy[:, 3] = boxes_xywh[:, 1] + boxes_xywh[:, 3]
+def xywh_to_xyxy(xywh: np.ndarray) -> np.ndarray:
+    """
+    Converts bounding box coordinates from `(x, y, width, height)`
+    format to `(x_min, y_min, x_max, y_max)` format.
+
+    Args:
+        xywh (np.ndarray): A numpy array of shape `(N, 4)` where each row
+            corresponds to a bounding box in the format `(x, y, width, height)`.
+
+    Returns:
+        np.ndarray: A numpy array of shape `(N, 4)` where each row corresponds
+            to a bounding box in the format `(x_min, y_min, x_max, y_max)`.
+
+    Examples:
+        ```python
+        import numpy as np
+        import supervision as sv
+
+        xywh = np.array([
+            [10, 20, 30, 40],
+            [15, 25, 35, 45]
+        ])
+
+        sv.xywh_to_xyxy(xywh=xywh)
+        # array([
+        #     [10, 20, 40, 60],
+        #     [15, 25, 50, 70]
+        # ])
+        ```
+    """
+    xyxy = xywh.copy()
+    xyxy[:, 2] = xywh[:, 0] + xywh[:, 2]
+    xyxy[:, 3] = xywh[:, 1] + xywh[:, 3]
+    return xyxy
+
+
+def xcycwh_to_xyxy(xcycwh: np.ndarray) -> np.ndarray:
+    """
+    Converts bounding box coordinates from `(center_x, center_y, width, height)`
+    format to `(x_min, y_min, x_max, y_max)` format.
+
+    Args:
+        xcycwh (np.ndarray): A numpy array of shape `(N, 4)` where each row
+            corresponds to a bounding box in the format `(center_x, center_y, width,
+            height)`.
+
+    Returns:
+        np.ndarray: A numpy array of shape `(N, 4)` where each row corresponds
+            to a bounding box in the format `(x_min, y_min, x_max, y_max)`.
+
+    Examples:
+        ```python
+        import numpy as np
+        import supervision as sv
+
+        xcycwh = np.array([
+            [50, 50, 20, 30],
+            [30, 40, 10, 15]
+        ])
+
+        sv.xcycwh_to_xyxy(xcycwh=xcycwh)
+        # array([
+        #     [40, 35, 60, 65],
+        #     [25, 32.5, 35, 47.5]
+        # ])
+        ```
+    """
+    xyxy = xcycwh.copy()
+    xyxy[:, 0] = xcycwh[:, 0] - xcycwh[:, 2] / 2
+    xyxy[:, 1] = xcycwh[:, 1] - xcycwh[:, 3] / 2
+    xyxy[:, 2] = xcycwh[:, 0] + xcycwh[:, 2] / 2
+    xyxy[:, 3] = xcycwh[:, 1] + xcycwh[:, 3] / 2
    return xyxy


@@ -5,7 +5,7 @@ from typing import List, Tuple, Union

 import matplotlib.pyplot as plt

-from supervision.utils.internal import classproperty, deprecated
+from supervision.utils.internal import classproperty

 DEFAULT_COLOR_PALETTE = [
    "A351FB",
@@ -255,46 +255,6 @@ class Color:
    def ROBOFLOW(cls) -> Color:
        return Color.from_hex("#A351FB")

-    @classmethod
-    @deprecated(
-        "`Color.white()` is deprecated and will be removed in "
-        "`supervision-0.22.0`. Use `Color.WHITE` instead."
-    )
-    def white(cls) -> Color:
-        return Color.from_hex(color_hex="#ffffff")
-
-    @classmethod
-    @deprecated(
-        "`Color.black()` is deprecated and will be removed in "
-        "`supervision-0.22.0`. Use `Color.BLACK` instead."
-    )
-    def black(cls) -> Color:
-        return Color.from_hex(color_hex="#000000")
-
-    @classmethod
-    @deprecated(
-        "`Color.red()` is deprecated and will be removed in "
-        "`supervision-0.22.0`. Use `Color.RED` instead."
-    )
-    def red(cls) -> Color:
-        return Color.from_hex(color_hex="#ff0000")
-
-    @classmethod
-    @deprecated(
-        "`Color.green()` is deprecated and will be removed in "
-        "`supervision-0.22.0`. Use `Color.GREEN` instead."
-    )
-    def green(cls) -> Color:
-        return Color.from_hex(color_hex="#00ff00")
-
-    @classmethod
-    @deprecated(
-        "`Color.blue()` is deprecated and will be removed in "
-        "`supervision-0.22.0`. Use `Color.BLUE` instead."
-    )
-    def blue(cls) -> Color:
-        return Color.from_hex(color_hex="#0000ff")
-

@dataclass
 class ColorPalette:
@@ -346,33 +306,6 @@ class ColorPalette:
    def LEGACY(cls) -> ColorPalette:
        return ColorPalette.from_hex(color_hex_list=LEGACY_COLOR_PALETTE)

-    @classmethod
-    @deprecated(
-        "`ColorPalette.default()` is deprecated and will be removed in "
-        "`supervision-0.22.0`. Use `Color.DEFAULT` instead."
-    )
-    def default(cls) -> ColorPalette:
-        """
-        !!! failure "Deprecated"
-
-            `ColorPalette.default()` is deprecated and will be removed in
-            `supervision-0.22.0`. Use `Color.DEFAULT` instead.
-
-        Returns a default color palette.
-
-        Returns:
-            ColorPalette: A ColorPalette instance with default colors.
-
-        Example:
-            ```python
-            import supervision as sv
-
-            sv.ColorPalette.default()
-            # ColorPalette(colors=[Color(r=255, g=64, b=64), Color(r=255, g=161, b=160), ...])
-            ```
-        """  # noqa: E501 // docs
-        return ColorPalette.from_hex(color_hex_list=DEFAULT_COLOR_PALETTE)
-
    @classmethod
    def from_hex(cls, color_hex_list: List[str]) -> ColorPalette:
        """
@@ -295,8 +295,8 @@ def calculate_optimal_text_scale(resolution_wh: Tuple[int, int]) -> float:
    Calculate font scale based on the resolution of an image.

    Parameters:
-         resolution_wh (Tuple[int, int]): A tuple representing the width and height
-             of the image.
+        resolution_wh (Tuple[int, int]): A tuple representing the width and height
+            of the image.

    Returns:
         float: The calculated font scale factor.
@@ -243,9 +243,10 @@ class KeyPoints:
        pose landmark detection inference result.

        Args:
-            mediapipe_results (Union[PoseLandmarkerResult, SolutionOutputs]):
-                The output results from Mediapipe. It supports both: the inference
-                result `PoseLandmarker` and the legacy one from `Pose`.
+            mediapipe_results (Union[PoseLandmarkerResult, FaceLandmarkerResult, SolutionOutputs]):
+                The output results from Mediapipe. It support pose and face landmarks
+                from `PoseLandmaker`, `FaceLandmarker` and the legacy ones
+                from `Pose` and `FaceMesh`.
            resolution_wh (Tuple[int, int]): A tuple of the form `(width, height)`
                representing the resolution of the frame.

@@ -283,14 +284,55 @@ class KeyPoints:
            key_points = sv.KeyPoints.from_mediapipe(
                pose_landmarker_result, (image_width, image_height))
            ```
+
+            ```python
+            import cv2
+            import mediapipe as mp
+            import supervision as sv
+
+            image = cv2.imread(<SOURCE_IMAGE_PATH>)
+            image_height, image_width, _ = image.shape
+            mediapipe_image = mp.Image(
+                image_format=mp.ImageFormat.SRGB,
+                data=cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+            options = mp.tasks.vision.FaceLandmarkerOptions(
+                base_options=mp.tasks.BaseOptions(
+                    model_asset_path="face_landmarker.task"
+                ),
+                output_face_blendshapes=True,
+                output_facial_transformation_matrixes=True,
+                num_faces=2)
+
+            FaceLandmarker = mp.tasks.vision.FaceLandmarker
+            with FaceLandmarker.create_from_options(options) as landmarker:
+                face_landmarker_result = landmarker.detect(mediapipe_image)
+
+            key_points = sv.KeyPoints.from_mediapipe(
+                face_landmarker_result, (image_width, image_height))
+            ```
        """  # noqa: E501 // docs
-        results = mediapipe_results.pose_landmarks
-        if not isinstance(mediapipe_results.pose_landmarks, list):
-            if mediapipe_results.pose_landmarks is None:
+        if hasattr(mediapipe_results, "pose_landmarks"):
+            results = mediapipe_results.pose_landmarks
+            if not isinstance(mediapipe_results.pose_landmarks, list):
+                if mediapipe_results.pose_landmarks is None:
+                    results = []
+                else:
+                    results = [
+                        [
+                            landmark
+                            for landmark in mediapipe_results.pose_landmarks.landmark
+                        ]
+                    ]
+        elif hasattr(mediapipe_results, "face_landmarks"):
+            results = mediapipe_results.face_landmarks
+        elif hasattr(mediapipe_results, "multi_face_landmarks"):
+            if mediapipe_results.multi_face_landmarks is None:
                results = []
            else:
                results = [
-                    [landmark for landmark in mediapipe_results.pose_landmarks.landmark]
+                    face_landmark.landmark
+                    for face_landmark in mediapipe_results.multi_face_landmarks
                ]

        if len(results) == 0:
@@ -414,6 +456,57 @@ class KeyPoints:
            data=data,
        )

+    @classmethod
+    def from_detectron2(cls, detectron2_results) -> KeyPoints:
+        """
+        Create a `sv.KeyPoints` object from the
+        [Detectron2](https://github.com/facebookresearch/detectron2) inference result.
+
+        Args:
+            detectron2_results: The output of a
+                Detectron2 model containing instances with prediction data.
+
+        Returns:
+            A `sv.KeyPoints` object containing the keypoint coordinates, class IDs,
+                and class names, and confidences of each keypoint.
+
+        Example:
+            ```python
+            import cv2
+            import supervision as sv
+            from detectron2.engine import DefaultPredictor
+            from detectron2.config import get_cfg
+
+
+            image = cv2.imread(<SOURCE_IMAGE_PATH>)
+            cfg = get_cfg()
+            cfg.merge_from_file(<CONFIG_PATH>)
+            cfg.MODEL.WEIGHTS = <WEIGHTS_PATH>
+            predictor = DefaultPredictor(cfg)
+
+            result = predictor(image)
+            keypoints = sv.KeyPoints.from_detectron2(result)
+            ```
+        """
+
+        if hasattr(detectron2_results["instances"], "pred_keypoints"):
+            if detectron2_results["instances"].pred_keypoints.cpu().numpy().size == 0:
+                return cls.empty()
+            return cls(
+                xy=detectron2_results["instances"]
+                .pred_keypoints.cpu()
+                .numpy()[:, :, :2],
+                confidence=detectron2_results["instances"]
+                .pred_keypoints.cpu()
+                .numpy()[:, :, 2],
+                class_id=detectron2_results["instances"]
+                .pred_classes.cpu()
+                .numpy()
+                .astype(int),
+            )
+        else:
+            return cls.empty()
+
    def __getitem__(
        self, index: Union[int, slice, List[int], np.ndarray, str]
    ) -> Union[KeyPoints, List, np.ndarray, None]:
@@ -276,7 +276,7 @@ class ByteTrack:
            model = YOLO(<MODEL_PATH>)
            tracker = sv.ByteTrack()

-            bounding_box_annotator = sv.BoundingBoxAnnotator()
+            box_annotator = sv.BoxAnnotator()
            label_annotator = sv.LabelAnnotator()

            def callback(frame: np.ndarray, index: int) -> np.ndarray:
@@ -286,7 +286,7 @@ class ByteTrack:

                labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]

-                annotated_frame = bounding_box_annotator.annotate(
+                annotated_frame = box_annotator.annotate(
                    scene=frame.copy(), detections=detections)
                annotated_frame = label_annotator.annotate(
                    scene=annotated_frame, detections=detections, labels=labels)
@@ -31,6 +31,16 @@ else:
    warnings.simplefilter("always", SupervisionWarnings)


+def warn_deprecated(message: str):
+    """
+    Issue a warning that a function is deprecated.
+
+    Args:
+        message (str): The message to display when the function is called.
+    """
+    warnings.warn(message, category=SupervisionWarnings, stacklevel=2)
+
+
 def deprecated_parameter(
    old_parameter: str,
    new_parameter: str,
@@ -82,15 +92,13 @@ def deprecated_parameter(
                else:
                    function_name = func.__name__

-                warnings.warn(
+                warn_deprecated(
                    message=warning_message.format(
                        function_name=function_name,
                        old_parameter=old_parameter,
                        new_parameter=new_parameter,
                        **message_kwargs,
-                    ),
-                    category=SupervisionWarnings,
-                    stacklevel=2,
+                    )
                )

                kwargs[new_parameter] = map_function(kwargs.pop(old_parameter))
@@ -106,11 +114,7 @@ def deprecated(reason: str):
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
-            warnings.warn(
-                f"{func.__name__} is deprecated: {reason}",
-                category=SupervisionWarnings,
-                stacklevel=2,
-            )
+            warn_deprecated(f"{func.__name__} is deprecated: {reason}")
            return func(*args, **kwargs)

        return wrapper
@@ -68,3 +68,17 @@ def fill(sequence: List[V], desired_size: int, content: V) -> List[V]:
    missing_size = max(0, desired_size - len(sequence))
    sequence.extend([content] * missing_size)
    return sequence
+
+
+def find_duplicates(sequence: List) -> List:
+    """
+    Find all duplicate elements in the input sequence.
+    """
+    seen = set()
+    duplicates = set()
+    for element in sequence:
+        if element in seen:
+            duplicates.add(element)
+        else:
+            seen.add(element)
+    return list(duplicates)
@@ -8,8 +8,6 @@ from typing import Callable, Generator, Optional, Tuple
 import cv2
 import numpy as np

-from supervision.utils.internal import deprecated
-

@dataclass
 class VideoInfo:
@@ -239,24 +237,6 @@ class FPSMonitor:
        """  # noqa: E501 // docs
        self.all_timestamps = deque(maxlen=sample_size)

-    @deprecated(
-        "`FPSMonitor.__call__` is deprecated and will be removed in "
-        "`supervision-0.22.0`. Use `FPSMonitor.fps` instead."
-    )
-    def __call__(self) -> float:
-        """
-        !!! failure "Deprecated"
-
-            `FPSMonitor.__call__` is deprecated and will be removed in
-            `supervision-0.22.0`. Use `FPSMonitor.fps` instead.
-
-        Computes and returns the average FPS based on the stored time stamps.
-
-        Returns:
-            float: The average FPS. Returns 0.0 if no time stamps are stored.
-        """
-        return self.fps
-
    @property
    def fps(self) -> float:
        """
@@ -62,8 +62,7 @@ def validate_keypoint_confidence(confidence: Any, n: int, m: int) -> None:
        if not is_valid:
            raise ValueError(
                f"confidence must be a 1D np.ndarray with shape {expected_shape}, but "
-                "got"
-                f"shape {actual_shape}"
+                f"got shape {actual_shape}"
            )


@@ -237,7 +237,7 @@ from supervision.detection.lmm import from_florence_2
            DoesNotRaise(),
        ),
        (  # Region to Category: detected
-            {"<REGION_TO_CATEGORY>": "some object<loc_3><loc_4><loc_5><loc_6>"},
+            {"<REGION_TO_CATEGORY>": "some object<loc_300><loc_400><loc_500><loc_600>"},
            (10, 10),
            (
                np.array([[3, 4, 5, 6]], dtype=np.float32),
@@ -254,11 +254,11 @@ from supervision.detection.lmm import from_florence_2
            DoesNotRaise(),
        ),
        (  # Region to Description: detected
-            {"<REGION_TO_DESCRIPTION>": "some description<loc_3><loc_4><loc_5><loc_6>"},
+            {"<REGION_TO_DESCRIPTION>": "descr<loc_300><loc_400><loc_500><loc_600>"},
            (10, 10),
            (
                np.array([[3, 4, 5, 6]], dtype=np.float32),
-                np.array(["some description"]),
+                np.array(["descr"]),
                None,
                None,
            ),
@@ -17,6 +17,8 @@ from supervision.detection.utils import (
    move_boxes,
    process_roboflow_result,
    scale_boxes,
+    xcycwh_to_xyxy,
+    xywh_to_xyxy,
 )

 TEST_MASK = np.zeros((1, 1000, 1000), dtype=bool)
@@ -1090,3 +1092,49 @@ def test_contains_multiple_segments(
    with exception:
        result = contains_multiple_segments(mask=mask, connectivity=connectivity)
        assert result == expected_result
+
+
+@pytest.mark.parametrize(
+    "xywh, expected_result",
+    [
+        (np.array([[10, 20, 30, 40]]), np.array([[10, 20, 40, 60]])),  # standard case
+        (np.array([[0, 0, 0, 0]]), np.array([[0, 0, 0, 0]])),  # zero size bounding box
+        (
+            np.array([[50, 50, 100, 100]]),
+            np.array([[50, 50, 150, 150]]),
+        ),  # large bounding box
+        (
+            np.array([[-10, -20, 30, 40]]),
+            np.array([[-10, -20, 20, 20]]),
+        ),  # negative coordinates
+        (np.array([[50, 50, 0, 30]]), np.array([[50, 50, 50, 80]])),  # zero width
+        (np.array([[50, 50, 20, 0]]), np.array([[50, 50, 70, 50]])),  # zero height
+        (np.array([]).reshape(0, 4), np.array([]).reshape(0, 4)),  # empty array
+    ],
+)
+def test_xywh_to_xyxy(xywh: np.ndarray, expected_result: np.ndarray) -> None:
+    result = xywh_to_xyxy(xywh)
+    np.testing.assert_array_equal(result, expected_result)
+
+
+@pytest.mark.parametrize(
+    "xcycwh, expected_result",
+    [
+        (np.array([[50, 50, 20, 30]]), np.array([[40, 35, 60, 65]])),  # standard case
+        (np.array([[0, 0, 0, 0]]), np.array([[0, 0, 0, 0]])),  # zero size bounding box
+        (
+            np.array([[50, 50, 100, 100]]),
+            np.array([[0, 0, 100, 100]]),
+        ),  # large bounding box centered at (50, 50)
+        (
+            np.array([[-10, -10, 20, 30]]),
+            np.array([[-20, -25, 0, 5]]),
+        ),  # negative coordinates
+        (np.array([[50, 50, 0, 30]]), np.array([[50, 35, 50, 65]])),  # zero width
+        (np.array([[50, 50, 20, 0]]), np.array([[40, 50, 60, 50]])),  # zero height
+        (np.array([]).reshape(0, 4), np.array([]).reshape(0, 4)),  # empty array
+    ],
+)
+def test_xcycwh_to_xyxy(xcycwh: np.ndarray, expected_result: np.ndarray) -> None:
+    result = xcycwh_to_xyxy(xcycwh)
+    np.testing.assert_array_equal(result, expected_result)
Author	SHA1	Message	Date
Linas Kondrackis	93c1b947fc	Merge pull request #1351 from roboflow/develop `supervision-0.22.0` release	2024-07-12 19:59:08 +03:00
SkalskiP	66d1f4c67d	bump version from `0.22.0rc1` to `0.22.0`	2024-07-12 18:53:53 +02:00
Linas Kondrackis	70a13fbf1e	Merge pull request #1342 from roboflow/release/supervision-0.22.0-deprecations Deprecations, changelog, notebook cleanup, 'new' markers	2024-07-12 19:49:17 +03:00
SkalskiP	1db8e12e1b	small changes	2024-07-12 18:47:41 +02:00
LinasKo	8853321058	Release process: push to 'main', not 'master'	2024-07-12 19:10:25 +03:00
LinasKo	0a5e830159	Add release process doc	2024-07-12 18:59:06 +03:00
pre-commit-ci[bot]	fb041f9b6c	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-07-12 15:20:00 +00:00
LinasKo	28f83b18f9	Deprecate BoundingBoxAnnotator, rename to BoxAnnotator	2024-07-12 18:18:50 +03:00
LinasKo	feb779bae9	Extended code examples in changelog	2024-07-12 15:48:07 +03:00
LinasKo	521dc57b76	Brief early mention that Dataset.images is deprecated	2024-07-12 15:18:20 +03:00
LinasKo	a0775544df	Fix typo: future deprecation will be removed, not 'were' removed	2024-07-12 15:11:17 +03:00
LinasKo	14a0e299a1	Changelog, reorder deprecations	2024-07-12 15:08:11 +03:00
LinasKo	1e783b623b	Wording: <TTF_FONT_PATH>	2024-07-12 15:05:09 +03:00
LinasKo	c7a8654390	deprections text: five subsequent release	2024-07-12 15:03:24 +03:00
Piotr Skalski	a7f8aee2c7	Merge pull request #1332 from roboflow/feat/cheatsheet-links Add cheatsheet links in supervision docs, readme	2024-07-12 13:25:15 +02:00
Piotr Skalski	5ed03491f9	Merge pull request #1327 from roboflow/fix/florence2-region-xyxy Fix bug where florence2 returns normalized xyxy regions	2024-07-12 12:17:18 +02:00
LinasKo	5d979df2f9	Add CropAnnotator PR link	2024-07-12 01:19:46 +03:00
LinasKo	5006f8c2f7	Add heading to deprecations page	2024-07-12 00:19:46 +03:00
LinasKo	8570ce68e2	'new' marker on Detections/Utils	2024-07-12 00:18:21 +03:00
LinasKo	3d3bbb4095	Add recently removed deprecations	2024-07-12 00:08:12 +03:00
LinasKo	a1e3543897	Redo demo.ipynb, quickstart.ipynb images	2024-07-12 00:04:48 +03:00
LinasKo	4b82679b00	Add to changelog: KeyPoints.from_detectron2, xcycwh_to_xyxy	2024-07-11 23:17:18 +03:00
Linas Kondrackis	5d4abe6e0d	Merge pull request #1310 from onuralpszr/detectron2-keypoint feat: 🚀 Keypoint support for detectron2	2024-07-11 14:58:21 +03:00
Piotr Skalski	0405c74cf3	Merge pull request #1340 from roboflow/feature/box_utils `xywh_to_xyxy` and `xcycwh_to_xyxy` box utils	2024-07-11 11:20:19 +02:00
Onuralp SEZER	13fe6e0942	fix: 🐞 add check if keypoints size is 0 return empty keypoint result Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-07-11 04:41:29 +03:00
Onuralp SEZER	03c92f7652	Merge pull request #1341 from roboflow/dependabot/pip/jupytext-1.16.3 ⬆️ Bump jupytext from 1.16.2 to 1.16.3	2024-07-11 04:14:37 +03:00
LinasKo	084926ded3	Deprecations, changelog, notebook cleanup, 'new' markers	2024-07-11 03:21:41 +03:00
dependabot[bot]	382ed8e609	⬆️ Bump jupytext from 1.16.2 to 1.16.3 Bumps [jupytext](https://github.com/mwouts/jupytext) from 1.16.2 to 1.16.3. - [Release notes](https://github.com/mwouts/jupytext/releases) - [Changelog](https://github.com/mwouts/jupytext/blob/main/CHANGELOG.md) - [Commits](https://github.com/mwouts/jupytext/compare/v1.16.2...v1.16.3) --- updated-dependencies: - dependency-name: jupytext dependency-type: direct:development update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com>	2024-07-11 00:17:30 +00:00
pre-commit-ci[bot]	2ed2a793cd	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-07-10 10:15:01 +00:00
SkalskiP	e0fb73f18a	`xywh_to_xyxy` and `xcycwh_to_xyxy` box utils	2024-07-10 12:13:07 +02:00
Piotr Skalski	de896189b8	Merge pull request #1326 from roboflow/feat/dataset-lazy-loading Lazy Dataset Loading	2024-07-10 11:52:57 +02:00
Onuralp SEZER	fbfd078795	Merge pull request #1336 from cdragos/docs/revise-contributing-guidelines docs: revise and restructure CONTRIBUTING.md	2024-07-09 21:34:43 +03:00
Dragos Catarahia	e31aad242a	docs: revise and restructure CONTRIBUTING.md	2024-07-10 01:22:23 +09:00
LinasKo	93c5c39f8f	as_pascal_voc now loads image paths	2024-07-09 15:30:19 +03:00
LinasKo	810148550b	Bugfix: XML parse in object_to_pascal_voc	2024-07-09 15:23:48 +03:00
LinasKo	172877a3cf	Fix accidental changes, rearrange utils, rename methods	2024-07-09 14:56:30 +03:00
pre-commit-ci[bot]	a91fc1e4f3	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-07-09 11:31:00 +00:00
LinasKo	f4fd595783	Fix image poaths in save_dataset, copy images if lazy	2024-07-09 14:30:07 +03:00
LinasKo	e690b92688	Move back save_images to utils	2024-07-09 14:17:04 +03:00
LinasKo	c8f771ec69	Move coco and yolo loaders back into utils	2024-07-09 14:04:11 +03:00
LinasKo	6b09e1d67c	ClassificationDataset, moved coco save function into dataset	2024-07-09 11:57:42 +03:00
LinasKo	12195705f5	LazyLoaded DetectionDataset	2024-07-09 02:13:56 +03:00
Onuralp SEZER	9f7871b4be	Merge pull request #1334 from roboflow/pre-commit-ci-update-config chore(pre_commit): ⬆ pre_commit autoupdate	2024-07-08 22:26:54 +03:00
pre-commit-ci[bot]	44890dc712	chore(pre_commit): ⬆ pre_commit autoupdate updates: - [github.com/astral-sh/ruff-pre-commit: v0.5.0 → v0.5.1](https://github.com/astral-sh/ruff-pre-commit/compare/v0.5.0...v0.5.1)	2024-07-08 17:39:23 +00:00
LinasKo	57ae7467b9	Add cheatsheet links in supervision docs, readme	2024-07-08 13:57:12 +03:00
Iuri de Silvio	1e5e25c54a	Merge pull request #1331 from roboflow/dependabot/pip/ruff-0.5.1 ⬆️ Bump ruff from 0.5.0 to 0.5.1	2024-07-08 10:17:18 +02:00
dependabot[bot]	ba7c4f4404	⬆️ Bump ruff from 0.5.0 to 0.5.1 Bumps [ruff](https://github.com/astral-sh/ruff) from 0.5.0 to 0.5.1. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.5.0...0.5.1) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:development update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com>	2024-07-08 01:13:42 +00:00
Onuralp SEZER	f3b52c08a7	Merge pull request #1329 from roboflow/dependabot/pip/certifi-2024.7.4 ⬆️ Bump certifi from 2024.6.2 to 2024.7.4	2024-07-06 11:39:25 +03:00
dependabot[bot]	1a36842407	⬆️ Bump certifi from 2024.6.2 to 2024.7.4 Bumps [certifi](https://github.com/certifi/python-certifi) from 2024.6.2 to 2024.7.4. - [Commits](https://github.com/certifi/python-certifi/compare/2024.06.02...2024.07.04) --- updated-dependencies: - dependency-name: certifi dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com>	2024-07-06 02:32:58 +00:00
LinasKo	a8b12914c8	Fix florence 2 tests	2024-07-05 13:21:59 +03:00
LinasKo	d3db15fcd0	Fix bug where florence2 returns normalized xyxy regions	2024-07-05 13:14:27 +03:00
Linas Kondrackis	c40734fb8d	Merge pull request #1316 from David-rn/feat/facemesh_from_mediapipe Facemesh from mediapipe	2024-07-05 12:56:50 +03:00
LinasKo	55a9cef270	Minor edit: add Python label to dosctring example	2024-07-05 12:41:02 +03:00
LinasKo	4b9b70fbb2	LazyDataset Loading - accept List[str] of images * Unfinished * Untested * Committing to show Piotr a bit earlier	2024-07-04 14:21:45 +03:00
Onuralp SEZER	e61a1074ac	Merge pull request #1324 from roboflow/dependabot/pip/tox-4.16.0 ⬆️ Bump tox from 4.15.1 to 4.16.0	2024-07-04 13:44:27 +03:00
dependabot[bot]	e6c7f970f0	⬆️ Bump tox from 4.15.1 to 4.16.0 Bumps [tox](https://github.com/tox-dev/tox) from 4.15.1 to 4.16.0. - [Release notes](https://github.com/tox-dev/tox/releases) - [Changelog](https://github.com/tox-dev/tox/blob/main/docs/changelog.rst) - [Commits](https://github.com/tox-dev/tox/compare/4.15.1...4.16.0) --- updated-dependencies: - dependency-name: tox dependency-type: direct:development update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com>	2024-07-04 01:08:33 +00:00
David Redo	a023259445	Updated from_mediapipe docstring with FaceLandmaker	2024-07-01 19:30:17 +02:00
David Redo	b164ef1111	Added check for FaceMesh legacy object when no faces are detected	2024-06-28 15:30:07 +02:00
David Redo	74d19dd069	Added FaceMesh support in from_mediapipe KeyPoints fuction	2024-06-27 17:21:30 +02:00
Onuralp SEZER	185ae462a4	fix: 🐞 confidence shape size fix for validation Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-06-26 16:09:10 +03:00
Onuralp SEZER	3676f07168	fix: 🐞 doc and validation typo fix Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-06-26 15:51:32 +03:00
Onuralp SEZER	099258baa6	feat: ✨ check pred_keypoints is exist if not return empty Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-06-26 15:44:32 +03:00
Onuralp SEZER	0bb3839d29	feat: ✨ Keypoints for detectron2 added Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-06-26 15:38:31 +03:00