Supervision

- Added [282](https://github.com/roboflow/supervision/pull/282): support for SAHI inference technique with [`sv.InferenceSlicer`](/0.14.0/detection/tools/inference_slicer).

python
>>> import cv2
>>> import supervision as sv
>>> from ultralytics import YOLO

>>> image = cv2.imread(SOURCE_IMAGE_PATH)
>>> model = YOLO(...)

>>> def callback(image_slice: np.ndarray) -> sv.Detections:
... result = model(image_slice)[0]
... return sv.Detections.from_ultralytics(result)

>>> slicer = sv.InferenceSlicer(callback = callback)

>>> detections = slicer(image)


- Added [297](https://github.com/roboflow/supervision/pull/297): [`Detections.from_deepsparse`](/0.14.0/detection/core/#supervision.detection.core.Detections.from_deepsparse) to enable seamless integration with [DeepSparse](https://github.com/neuralmagic/deepsparse) framework.

- Added [281](https://github.com/roboflow/supervision/pull/281): [`sv.Classifications.from_ultralytics`](/0.14.0/classification/core/#supervision.classification.core.Classifications.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use supervision with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.

!!! failure "Deprecated"

[sv.Detections.from_yolov8](/0.14.0/detection/core/supervision.detection.core.Detections.from_yolov8) and [sv.Classifications.from_yolov8](/0.14.0/classification/core/supervision.classification.core.Classifications.from_yolov8) are now deprecated and will be removed with `supervision-0.16.0` release.

- Added [341](https://github.com/roboflow/supervision/pull/341): First supervision usage example script showing how to detect and track objects on video using YOLOv8 + Supervision.

- Changed [296](https://github.com/roboflow/supervision/pull/296): [`sv.ClassificationDataset`](/0.14.0/dataset/core/#supervision.dataset.core.ClassificationDataset) and [`sv.DetectionDataset`](/0.14.0/dataset/core/supervision.dataset.core.DetectionDataset) now use image path (not image name) as dataset keys.

- Fixed [300](https://github.com/roboflow/supervision/pull/300): [`Detections.from_roboflow`](/0.14.0/detection/core/#supervision.detection.core.Detections.from_roboflow) to filter out polygons with less than 3 points.

- Added [236](https://github.com/roboflow/supervision/pull/236): support for mean average precision (mAP) for object detection models with [`sv.MeanAveragePrecision`](/0.13.0/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

!!! failure "Python 3.7. Support Terminated"

With the `supervision-0.12.0` release, we are terminating official support for Python 3.7.

- Added [177](https://github.com/roboflow/supervision/pull/177): initial support for object detection model benchmarking with [`sv.ConfusionMatrix`](/0.12.0/metrics/detection/#confusionmatrix).

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)

>>> confusion_matrix = sv.ConfusionMatrix.benchmark(
... dataset = dataset,
... callback = callback
... )

>>> confusion_matrix.matrix
array([
[0., 0., 0., 0.],
[0., 1., 0., 1.],
[0., 1., 1., 0.],
[1., 1., 0., 0.]
])


- Added [173](https://github.com/roboflow/supervision/pull/173): [`Detections.from_mmdetection`](/0.12.0/detection/core/#supervision.detection.core.Detections.from_mmdetection) to enable seamless integration with [MMDetection](https://github.com/open-mmlab/mmdetection) framework.

- Added [130](https://github.com/roboflow/supervision/issues/130): ability to [install](https://supervision.roboflow.com/) package in `headless` or `desktop` mode.

- Changed [180](https://github.com/roboflow/supervision/pull/180): packing method from `setup.py` to `pyproject.toml`.

- Fixed [188](https://github.com/roboflow/supervision/issues/188): [`sv.DetectionDataset.from_cooc`](/0.12.0/dataset/core/#supervision.dataset.core.DetectionDataset.from_coco) can't be loaded when there are images without annotations.

- Fixed [226](https://github.com/roboflow/supervision/issues/226): [`sv.DetectionDataset.from_yolo`](/0.12.0/dataset/core/#supervision.dataset.core.DetectionDataset.from_yolo) can't load background instances.

- Fix [165](https://github.com/roboflow/supervision/pull/165): [`as_folder_structure`](/0.11.1/dataset/core/#supervision.dataset.core.ClassificationDataset.as_folder_structure) fails to save [`sv.ClassificationDataset`](/0.11.1/dataset/core/classificationdataset) when it is result of inference.

- Added [150](https://github.com/roboflow/supervision/pull/150): ability to load and save [`sv.DetectionDataset`](/0.11.0/dataset/core/#detectiondataset) in COCO format using [`as_coco`](/0.11.0/dataset/core/supervision.dataset.core.DetectionDataset.as_coco) and [`from_coco`](/0.11.0/dataset/core/supervision.dataset.core.DetectionDataset.from_coco) methods.

python
>>> import supervision as sv

>>> ds = sv.DetectionDataset.from_coco(
... images_directory_path='...',
... annotations_path='...'
... )

>>> ds.as_coco(
... images_directory_path='...',
... annotations_path='...'
... )


- Added [158](https://github.com/roboflow/supervision/pull/158): ability to merge multiple [`sv.DetectionDataset`](/0.11.0/dataset/core/#detectiondataset) together using [`merge`](/0.11.0/dataset/core/supervision.dataset.core.DetectionDataset.merge) method.

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']


- Added [162](https://github.com/roboflow/supervision/pull/162): additional `start` and `end` arguments to [`sv.get_video_frames_generator`](/0.11.0/utils/video/#get_video_frames_generator) allowing to generate frames only for a selected part of the video.

- Fix [157](https://github.com/roboflow/supervision/pull/157): incorrect loading of YOLO dataset class names from `data.yaml`.

- Added [125](https://github.com/roboflow/supervision/pull/125): ability to load and save [`sv.ClassificationDataset`](/0.10.0/dataset/core/#classificationdataset) in a folder structure format.

python
>>> import supervision as sv

>>> cs = sv.ClassificationDataset.from_folder_structure(
... root_directory_path='...'
... )

>>> cs.as_folder_structure(
... root_directory_path='...'
... )


- Added [125](https://github.com/roboflow/supervision/pull/125): support for [`sv.ClassificationDataset.split`](/0.10.0/dataset/core/#supervision.dataset.core.ClassificationDataset.split) allowing to divide `sv.ClassificationDataset` into two parts.

- Added [110](https://github.com/roboflow/supervision/pull/110): ability to extract masks from Roboflow API results using [`sv.Detections.from_roboflow`](/0.10.0/detection/core/#supervision.detection.core.Detections.from_roboflow).

- Added [commit hash](https://github.com/roboflow/supervision/commit/d000292eb2f2342544e0947b65528082e60fb8d6): Supervision Quickstart [notebook](https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb) where you can learn more about Detection, Dataset and Video APIs.

- Changed [135](https://github.com/roboflow/supervision/pull/135): `sv.get_video_frames_generator` documentation to better describe actual behavior.