supervision-0.13.0 release

`supervision-0.13.0` release

Author: SkalskiP

CONTRIBUTING.md

@@ -24,14 +24,15 @@ Before you contribute a new feature, consider submitting an Issue to discuss the
 
 ## How to Contribute Changes
 
-First, fork this repository to your own GitHub account. Create a new branch that describes your changes (i.e. `line-counter-docs`). Push your changes to the branch on your fork and then submit a pull request to this repository.
+First, fork this repository to your own GitHub account. Create a new branch that describes your changes (i.e. `line-counter-docs`). Push your changes to the branch on your fork and then submit a pull request to `develop` branch of this repository.
 
 When creating new functions, please ensure you have the following:
 
 1. Docstrings for the function and all parameters.
 2. Unit tests for the function.
 3. Examples in the documentation for the function.
 4. Created an entry in our docs to autogenerate the documentation for the function.
+5. Please share google colab with minimal code to test new feature or reproduce PR whenever it is possible. Please ensure that google colab can be accessed without any issue. 
 
 All pull requests will be reviewed by the maintainers of the project. We will provide feedback and ask for changes if necessary.
 
@@ -48,4 +49,4 @@ So far, **there is no types checking with mypy**. See [issue](https://github.com
 
 ## 🧪 tests 
 
-[`pytests`](https://docs.pytest.org/en/7.1.x/) is used to run our tests.
+[`pytests`](https://docs.pytest.org/en/7.1.x/) is used to run our tests.

README.md

@@ -31,7 +31,7 @@ Pip install the supervision package in a
 pip install supervision[desktop]
 ```
 
-Read more about desktop, headless and local installation in our [guide](https://roboflow.github.io/supervision/).
+Read more about desktop, headless, and local installation in our [guide](https://roboflow.github.io/supervision/).
 
 ## 🔥 quickstart
 
@@ -52,7 +52,7 @@ Read more about desktop, headless and local installation in our [guide](https://
 <details close>
 <summary>👉 more detections utils</summary>
 
-- Easily switch inference pipeline between supported object detection / instance segmentation models
+- Easily switch inference pipeline between supported object detection/instance segmentation models
 
     ```python
     >>> import supervision as sv
@@ -107,7 +107,7 @@ Read more about desktop, headless and local installation in our [guide](https://
 <details close>
 <summary>👉 more dataset utils</summary>
 
-- Load object detection / instance segmentation datasets in one of supported formats
+- Load object detection/instance segmentation datasets in one of the supported formats
 
     ```python
     >>> dataset = sv.DetectionDataset.from_yolo(
@@ -138,7 +138,7 @@ Read more about desktop, headless and local installation in our [guide](https://
            [ 20.154999, 347.825   , 416.125   , 915.895   ]], dtype=float32)
     ```
 
-- Split dataset for training, testing and validation
+- Split dataset for training, testing, and validation
 
     ```python
     >>> train_dataset, test_dataset = dataset.split(split_ratio=0.7)
@@ -148,7 +148,7 @@ Read more about desktop, headless and local installation in our [guide](https://
     (700, 150, 150)
     ```
 
-- Merge multiple datasets together
+- Merge multiple datasets
 
     ```python
     >>> ds_1 = sv.DetectionDataset(...)
@@ -170,7 +170,7 @@ Read more about desktop, headless and local installation in our [guide](https://
     ['cat', 'dog', 'person']
     ```
 
-- Save object detection / instance segmentation datasets in one of supported formats
+- Save object detection/instance segmentation datasets in one of the supported formats
 
     ```python
     >>> dataset.as_yolo(
@@ -203,15 +203,15 @@ Read more about desktop, headless and local installation in our [guide](https://
     ... )
     ```
 
-- Load classification datasets in one of supported formats
+- Load classification datasets in one of the supported formats
 
     ```python
     >>> cs = sv.ClassificationDataset.from_folder_structure(
     ...     root_directory_path='...'
     ... )
     ```
 
-- Save classification datasets in one of supported formats
+- Save classification datasets in one of the supported formats
 
     ```python
     >>> cs.as_folder_structure(
@@ -245,6 +245,30 @@ array([
 ])
 ```
 
+<details close>
+<summary>👉 more metrics</summary>
+
+- Mean average precision (mAP) for object detection tasks.
+
+    ```python
+    >>> import supervision as sv
+    
+    >>> dataset = sv.DetectionDataset.from_yolo(...)
+    
+    >>> def callback(image: np.ndarray) -> sv.Detections:
+    ...     ...
+    
+    >>> mean_average_precision = sv.MeanAveragePrecision.benchmark(
+    ...     dataset = dataset,
+    ...     callback = callback
+    ... )
+    
+    >>> mean_average_precision.map50_95
+    0.433 
+    ```
+  
+</details>
+
 ## 🎬 tutorials
 
 <p align="left">

docs/changelog.md

@@ -1,3 +1,39 @@
+### 0.13.0 <small>August 8, 2023</small>
+
+- Added [#236](https://github.com/roboflow/supervision/pull/236): support for mean average precision (mAP) for object detection models with [`sv.MeanAveragePrecision`](https://roboflow.github.io/supervision/metrics/detection/#meanaverageprecision). 
+
+```python
+>>> import supervision as sv
+>>> from ultralytics import YOLO
+
+>>> dataset = sv.DetectionDataset.from_yolo(...)
+
+>>> model = YOLO(...)
+>>> def callback(image: np.ndarray) -> sv.Detections:
+...     result = model(image)[0]
+...     return sv.Detections.from_yolov8(result)
+
+>>> mean_average_precision = sv.MeanAveragePrecision.benchmark(
+...     dataset = dataset,
+...     callback = callback
+... )
+
+>>> mean_average_precision.map50_95
+0.433
+```
+
+- Added [#256](https://github.com/roboflow/supervision/pull/256): support for ByteTrack for object tracking with [`sv.ByteTrack`](https://roboflow.github.io/supervision/tracker/core/#bytetrack).
+
+- Added [#222](https://github.com/roboflow/supervision/pull/222): [`sv.Detections.from_ultralytics`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use `supervision` with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.
+
+!!! warning
+
+    [`sv.Detections.from_yolov8`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_yolov8) is now deprecated and will be removed with `supervision-0.15.0` release.
+
+- Added [#191](https://github.com/roboflow/supervision/pull/191): [`sv.Detections.from_paddledet`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_paddledet) to enable seamless integration with [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) framework.
+
+- Added [#245](https://github.com/roboflow/supervision/pull/245): support for loading PASCAL VOC segmentation datasets with [`sv.DetectionDataset.`](https://roboflow.github.io/supervision/dataset/core/#supervision.dataset.core.DetectionDataset.from_pascal_voc).
+
 ### 0.12.0 <small>July 24, 2023</small>
 
 !!! warning

docs/metrics/detection.md

@@ -6,3 +6,7 @@
 ## ConfusionMatrix
 
 :::supervision.metrics.detection.ConfusionMatrix
+
+## MeanAveragePrecision
+
+:::supervision.metrics.detection.MeanAveragePrecision

docs/tracker/core.md

@@ -0,0 +1,4 @@
+## ByteTrack
+
+:::supervision.tracker.byte_tracker.core.ByteTrack
+

mkdocs.yml

@@ -37,6 +37,8 @@ nav:
         - Utils: detection/utils.md
         - Tools:
             - Polygon Zone: detection/tools/polygon_zone.md
+    - Trackers:
+        - Core: tracker/core.md
     - Dataset:
         - Core: dataset/core.md
     - Metrics: