bump version from 0.23.0 to 0.24.0rc1

Merge pull request #1500 from roboflow/fix/101-coco-algorithm
101 AP algo: do not interpollate precision
2024-09-04 17:34:21 +02:00 · 2024-09-04 17:27:59 +02:00 · 2024-09-04 16:45:06 +02:00 · 2024-09-03 19:08:07 +02:00 · 2024-09-03 00:52:13 +03:00 · 2024-09-02 17:48:28 +00:00
13 changed files with 94 additions and 143 deletions
@@ -32,7 +32,7 @@ repos:
          additional_dependencies: ["bandit[toml]"]

  -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.6.2
+      rev: v0.6.3
      hooks:
      -   id: ruff
          args: [--fix, --exit-non-zero-on-fix]
@@ -295,7 +295,7 @@ We love your input! Please see our [contributing guide](https://github.com/robof
          />
      </a>
      <img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
-      <a href="https://disuss.roboflow.com">
+      <a href="https://discuss.roboflow.com">
          <img
            src="https://media.roboflow.com/notebooks/template/icons/purple/forum.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633584"
            width="3%"
@@ -368,7 +368,7 @@
                "<br>\n",
                "\n",
                "\n",
-                "In the code above we've created a`video_info` variable to pass information about the video to our `VideoSink`. The `VideoSink` is a cool little context manager that allows us to `write_frames()` to a video ouput file. We're also optionally using `tqdm` to display a progress bar with a % complete. We only scratched the surface of all of the customizable Annotators and additional features that Supervision and Inference have to offer. Stay tuned for more cookbooks on how to take advantge of them in your computer vision applications. Happy building! \ud83d\ude80"
+                "In the code above we've created a`video_info` variable to pass information about the video to our `VideoSink`. The `VideoSink` is a cool little context manager that allows us to `write_frames()` to a video output file. We're also optionally using `tqdm` to display a progress bar with a % complete. We only scratched the surface of all of the customizable Annotators and additional features that Supervision and Inference have to offer. Stay tuned for more cookbooks on how to take advantage of them in your computer vision applications. Happy building! \ud83d\ude80"
            ]
        }
    ],
@@ -402,7 +402,7 @@
        {
            "cell_type": "markdown",
            "source": [
-                "## Improve Vizualizations"
+                "## Improve Visualizations"
            ],
            "metadata": {
                "id": "HsPNfuM_3ENR"
@@ -103,7 +103,7 @@
            "source": [
                "## Download a Video Asset\n",
                "\n",
-                "Now that we have our enviornment setup, lets download a video that we can detect objects in. Supervision comes with a great utility to help us hit the ground running. We can use the below snippet to he video is save a video asset in our local directory. It can also be accessed with the variable `path_to_video` for additional application logic."
+                "Now that we have our environment setup, lets download a video that we can detect objects in. Supervision comes with a great utility to help us hit the ground running. We can use the below snippet to he video is save a video asset in our local directory. It can also be accessed with the variable `path_to_video` for additional application logic."
            ]
        },
        {
@@ -951,7 +951,7 @@
            },
            "source": [
                "#### Import your data\n",
-                "To load your data back in, upload the saved file to the Colab enviorment and run the code cell."
+                "To load your data back in, upload the saved file to the Colab environment and run the code cell."
            ]
        },
        {
@@ -1577,13 +1577,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>

 [[package]]
 name = "jupyterlab"
-version = "4.2.4"
+version = "4.2.5"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]

 [package.dependencies]
@@ -2130,13 +2130,13 @@ min-versions = ["babel (==2.9.0)", "click (==7.0)", "colorama (==0.4)", "ghp-imp

 [[package]]
 name = "mkdocs-autorefs"
-version = "1.0.1"
+version = "1.2.0"
 description = "Automatically link across pages in MkDocs."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]

 [package.dependencies]
@@ -2215,13 +2215,13 @@ pygments = ">2.12.0"

 [[package]]
 name = "mkdocs-material"
-version = "9.5.33"
+version = "9.5.34"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -2257,25 +2257,25 @@ files = [

 [[package]]
 name = "mkdocstrings"
-version = "0.25.2"
+version = "0.26.0"
 description = "Automatic documentation from sources, for MkDocs."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
 click = ">=7.0"
 importlib-metadata = {version = ">=4.6", markers = "python_version < \"3.10\""}
 Jinja2 = ">=2.11.1"
-Markdown = ">=3.3"
+Markdown = ">=3.6"
 MarkupSafe = ">=1.1"
 mkdocs = ">=1.4"
-mkdocs-autorefs = ">=0.3.1"
+mkdocs-autorefs = ">=1.2"
 mkdocstrings-python = {version = ">=0.5.2", optional = true, markers = "extra == \"python\""}
-platformdirs = ">=2.2.0"
+platformdirs = ">=2.2"
 pymdown-extensions = ">=6.3"
 typing-extensions = {version = ">=4.1", markers = "python_version < \"3.10\""}

@@ -2785,24 +2785,6 @@ files = [
 [package.dependencies]
 types-pytz = ">=2022.1.1"

-[[package]]
-name = "pandas-stubs"
-version = "2.0.3.230814"
-description = "Type annotations for pandas"
-optional = true
-python-versions = ">=3.8"
-files = [
-    {file = "pandas_stubs-2.0.3.230814-py3-none-any.whl", hash = "sha256:4b3dfc027d49779176b7daa031a3405f7b839bcb6e312f4b9f29fea5feec5b4f"},
-    {file = "pandas_stubs-2.0.3.230814.tar.gz", hash = "sha256:1d5cc09e36e3d9f9a1ed9dceae4e03eeb26d1b898dd769996925f784365c8769"},
-]
-
-[package.dependencies]
-numpy = [
-    {version = "<=1.24.3", markers = "python_full_version <= \"3.8.0\""},
-    {version = ">=1.25.0", markers = "python_version >= \"3.9\""},
-]
-types-pytz = ">=2022.1.1"
-
 [[package]]
 name = "pandocfilters"
 version = "1.5.1"
@@ -3838,29 +3820,29 @@ files = [

 [[package]]
 name = "ruff"
-version = "0.6.2"
+version = "0.6.3"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
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 [[package]]
@@ -4232,6 +4214,17 @@ files = [
    {file = "types_python_dateutil-2.9.0.20240316-py3-none-any.whl", hash = "sha256:6b8cb66d960771ce5ff974e9dd45e38facb81718cc1e208b10b1baccbfdbee3b"},
 ]

+[[package]]
+name = "types-pytz"
+version = "2024.1.0.20240417"
+description = "Typing stubs for pytz"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "types-pytz-2024.1.0.20240417.tar.gz", hash = "sha256:6810c8a1f68f21fdf0f4f374a432487c77645a0ac0b31de4bf4690cf21ad3981"},
+    {file = "types_pytz-2024.1.0.20240417-py3-none-any.whl", hash = "sha256:8335d443310e2db7b74e007414e74c4f53b67452c0cb0d228ca359ccfba59659"},
+]
+
 [[package]]
 name = "types-pyyaml"
 version = "6.0.12.20240808"
@@ -4323,6 +4316,7 @@ optional = false
 python-versions = ">=3.8"
 files = [
    {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
+    {file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
 ]

 [package.extras]
@@ -4513,4 +4507,4 @@ metrics = ["pandas", "pandas-stubs"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "84cd2523482606530c1b5dbe931d053dfd356e0c6e80c741ddc10ea7d808affd"
+content-hash = "ab1a7151a2f59ad9a820550fca7361d9817ff8260e8b6d527b198b09d98612e4"
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.23.0"
+version = "0.24.0rc1"
 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>"]
@@ -85,7 +85,7 @@ docutils = [

 [tool.poetry.group.docs.dependencies]
 mkdocs-material = { extras = ["imaging"], version = "^9.5.5" }
-mkdocstrings = { extras = ["python"], version = "^0.25.2" }
+mkdocstrings = { extras = ["python"], version = ">=0.25.2,<0.27.0" }
 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
@@ -86,19 +86,10 @@ class MeanAveragePrecision(Metric):
    ) -> MeanAveragePrecisionResult:
        """
        Calculate Mean Average Precision based on predicted and ground-truth
-            detections at different threshold.
+            detections at different thresholds.

-        Args:
-            predictions (List[np.ndarray]): Each element of the list describes
-                a single image and has `shape = (M, 6)` where `M` is
-                the number of detected objects. Each row is expected to be
-                in `(x_min, y_min, x_max, y_max, class, conf)` format.
-            targets (List[np.ndarray]): Each element of the list describes a single
-                image and has `shape = (N, 5)` where `N` is the
-                number of ground-truth objects. Each row is expected to be in
-                `(x_min, y_min, x_max, y_max, class)` format.
        Returns:
-            (MeanAveragePrecision): New instance of MeanAveragePrecision.
+            (MeanAveragePrecisionResult): New instance of MeanAveragePrecision.

        Example:
            ```python
@@ -219,7 +210,7 @@ class MeanAveragePrecision(Metric):
        )

    @staticmethod
-    def compute_average_precision(recall: np.ndarray, precision: np.ndarray) -> float:
+    def _compute_average_precision(recall: np.ndarray, precision: np.ndarray) -> float:
        """
        Compute the average precision using 101-point interpolation (COCO), given
            the recall and precision curves.
@@ -231,16 +222,15 @@ class MeanAveragePrecision(Metric):
        Returns:
            (float): Average precision.
        """
-        extended_recall = np.concatenate(([0.0], recall, [1.0]))
-        extended_precision = np.concatenate(([1.0], precision, [0.0]))
-        max_accumulated_precision = np.flip(
-            np.maximum.accumulate(np.flip(extended_precision))
-        )
-        interpolated_recall_levels = np.linspace(0, 1, 101)
-        interpolated_precision = np.interp(
-            interpolated_recall_levels, extended_recall, max_accumulated_precision
-        )
-        average_precision = np.trapz(interpolated_precision, interpolated_recall_levels)
+        if len(recall) == 0 and len(precision) == 0:
+            return 0.0
+
+        recall_levels = np.linspace(0, 1, 101)
+        precision_levels = np.zeros_like(recall_levels)
+        for r, p in zip(recall[::-1], precision[::-1]):
+            precision_levels[recall_levels <= r] = p
+
+        average_precision = (1 / 100 * precision_levels).sum()
        return average_precision

    @staticmethod
@@ -316,14 +306,14 @@ class MeanAveragePrecision(Metric):

            false_positives = (1 - matches[is_class]).cumsum(0)
            true_positives = matches[is_class].cumsum(0)
-            true_negatives = total_true - true_positives
+            false_negatives = total_true - true_positives

-            recall = true_positives / (true_positives + true_negatives + eps)
+            recall = true_positives / (true_positives + false_negatives + eps)
            precision = true_positives / (true_positives + false_positives)

            for iou_level_idx in range(matches.shape[1]):
                average_precisions[class_idx, iou_level_idx] = (
-                    MeanAveragePrecision.compute_average_precision(
+                    MeanAveragePrecision._compute_average_precision(
                        recall[:, iou_level_idx], precision[:, iou_level_idx]
                    )
                )
@@ -1,7 +1,6 @@
 from contextlib import ExitStack as DoesNotRaise
 from typing import List, Optional

-import numpy as np
 import pytest

 from supervision import DetectionDataset
@@ -13,29 +12,29 @@ from test.test_utils import mock_detections
    [
        (
            [],
-            DetectionDataset(classes=[], images={}, annotations={}),
+            DetectionDataset(classes=[], images=[], annotations={}),
            DoesNotRaise(),
        ),  # empty dataset list
        (
-            [DetectionDataset(classes=[], images={}, annotations={})],
-            DetectionDataset(classes=[], images={}, annotations={}),
+            [DetectionDataset(classes=[], images=[], annotations={})],
+            DetectionDataset(classes=[], images=[], annotations={}),
            DoesNotRaise(),
        ),  # single empty dataset
        (
            [
-                DetectionDataset(classes=["dog", "person"], images={}, annotations={}),
-                DetectionDataset(classes=["dog", "person"], images={}, annotations={}),
+                DetectionDataset(classes=["dog", "person"], images=[], annotations={}),
+                DetectionDataset(classes=["dog", "person"], images=[], annotations={}),
            ],
-            DetectionDataset(classes=["dog", "person"], images={}, annotations={}),
+            DetectionDataset(classes=["dog", "person"], images=[], annotations={}),
            DoesNotRaise(),
        ),  # two datasets; no images and annotations, the same classes
        (
            [
-                DetectionDataset(classes=["dog", "person"], images={}, annotations={}),
-                DetectionDataset(classes=["cat"], images={}, annotations={}),
+                DetectionDataset(classes=["dog", "person"], images=[], annotations={}),
+                DetectionDataset(classes=["cat"], images=[], annotations={}),
            ],
            DetectionDataset(
-                classes=["cat", "dog", "person"], images={}, annotations={}
+                classes=["cat", "dog", "person"], images=[], annotations={}
            ),
            DoesNotRaise(),
        ),  # two datasets; no images and annotations, different classes
@@ -43,10 +42,7 @@ from test.test_utils import mock_detections
            [
                DetectionDataset(
                    classes=["dog", "person"],
-                    images={
-                        "image-1.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                        "image-2.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                    },
+                    images=["image-1.png", "image-2.png"],
                    annotations={
                        "image-1.png": mock_detections(
                            xyxy=[[0, 0, 10, 10]], class_id=[0]
@@ -56,14 +52,11 @@ from test.test_utils import mock_detections
                        ),
                    },
                ),
-                DetectionDataset(classes=[], images={}, annotations={}),
+                DetectionDataset(classes=[], images=[], annotations={}),
            ],
            DetectionDataset(
                classes=["dog", "person"],
-                images={
-                    "image-1.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                    "image-2.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                },
+                images=["image-1.png", "image-2.png"],
                annotations={
                    "image-1.png": mock_detections(xyxy=[[0, 0, 10, 10]], class_id=[0]),
                    "image-2.png": mock_detections(xyxy=[[0, 0, 10, 10]], class_id=[1]),
@@ -75,10 +68,7 @@ from test.test_utils import mock_detections
            [
                DetectionDataset(
                    classes=["dog", "person"],
-                    images={
-                        "image-1.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                        "image-2.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                    },
+                    images=["image-1.png", "image-2.png"],
                    annotations={
                        "image-1.png": mock_detections(
                            xyxy=[[0, 0, 10, 10]], class_id=[0]
@@ -88,14 +78,11 @@ from test.test_utils import mock_detections
                        ),
                    },
                ),
-                DetectionDataset(classes=["cat"], images={}, annotations={}),
+                DetectionDataset(classes=["cat"], images=[], annotations={}),
            ],
            DetectionDataset(
                classes=["cat", "dog", "person"],
-                images={
-                    "image-1.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                    "image-2.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                },
+                images=["image-1.png", "image-2.png"],
                annotations={
                    "image-1.png": mock_detections(xyxy=[[0, 0, 10, 10]], class_id=[1]),
                    "image-2.png": mock_detections(xyxy=[[0, 0, 10, 10]], class_id=[2]),
@@ -107,10 +94,7 @@ from test.test_utils import mock_detections
            [
                DetectionDataset(
                    classes=["dog", "person"],
-                    images={
-                        "image-1.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                        "image-2.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                    },
+                    images=["image-1.png", "image-2.png"],
                    annotations={
                        "image-1.png": mock_detections(
                            xyxy=[[0, 0, 10, 10]], class_id=[0]
@@ -122,9 +106,7 @@ from test.test_utils import mock_detections
                ),
                DetectionDataset(
                    classes=["cat"],
-                    images={
-                        "image-3.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                    },
+                    images=["image-3.png"],
                    annotations={
                        "image-3.png": mock_detections(
                            xyxy=[[0, 0, 10, 10]], class_id=[0]
@@ -134,11 +116,7 @@ from test.test_utils import mock_detections
            ],
            DetectionDataset(
                classes=["cat", "dog", "person"],
-                images={
-                    "image-1.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                    "image-2.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                    "image-3.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                },
+                images=["image-1.png", "image-2.png", "image-3.png"],
                annotations={
                    "image-1.png": mock_detections(xyxy=[[0, 0, 10, 10]], class_id=[1]),
                    "image-2.png": mock_detections(xyxy=[[0, 0, 10, 10]], class_id=[2]),
@@ -151,10 +129,7 @@ from test.test_utils import mock_detections
            [
                DetectionDataset(
                    classes=["dog", "person"],
-                    images={
-                        "image-1.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                        "image-2.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                    },
+                    images=["image-1.png", "image-2.png"],
                    annotations={
                        "image-1.png": mock_detections(
                            xyxy=[[0, 0, 10, 10]], class_id=[0]
@@ -166,10 +141,7 @@ from test.test_utils import mock_detections
                ),
                DetectionDataset(
                    classes=["dog", "person"],
-                    images={
-                        "image-2.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                        "image-3.png": np.zeros((100, 100, 3), dtype=np.uint8),
-                    },
+                    images=["image-2.png", "image-3.png"],
                    annotations={
                        "image-2.png": mock_detections(
                            xyxy=[[0, 0, 10, 10]], class_id=[0]
@@ -278,7 +278,7 @@ def test_map_detections_class_id(
            np.array([[[]]]).astype(bool),
            None,
            pytest.raises(AssertionError),
-        ),  # raises AssertionError because mask dimentionality is not 2D
+        ),  # raises AssertionError because mask dimensionality is not 2D
        (
            np.array([[]]).astype(bool),
            None,
@@ -26,7 +26,6 @@ DETECTIONS = mock_detections(
 )

 POLYGON = np.array([[100, 100], [200, 100], [200, 200], [100, 200]])
-FRAME_RESOLUTION = (300, 300)


@pytest.mark.parametrize(
@@ -36,7 +35,6 @@ FRAME_RESOLUTION = (300, 300)
            DETECTIONS,
            sv.PolygonZone(
                POLYGON,
-                FRAME_RESOLUTION,
                triggering_anchors=(
                    sv.Position.TOP_LEFT,
                    sv.Position.TOP_RIGHT,
@@ -53,7 +51,6 @@ FRAME_RESOLUTION = (300, 300)
            DETECTIONS,
            sv.PolygonZone(
                POLYGON,
-                FRAME_RESOLUTION,
            ),
            np.array(
                [False, False, True, True, True, True, False, False, False], dtype=bool
@@ -64,7 +61,6 @@ FRAME_RESOLUTION = (300, 300)
            DETECTIONS,
            sv.PolygonZone(
                POLYGON,
-                FRAME_RESOLUTION,
                triggering_anchors=[sv.Position.BOTTOM_CENTER],
            ),
            np.array(
@@ -76,7 +72,6 @@ FRAME_RESOLUTION = (300, 300)
            sv.Detections.empty(),
            sv.PolygonZone(
                POLYGON,
-                FRAME_RESOLUTION,
            ),
            np.array([], dtype=bool),
            DoesNotRaise(),
@@ -107,4 +102,4 @@ def test_polygon_zone_trigger(
 )
 def test_polygon_zone_initialization(polygon, triggering_anchors, exception):
    with exception:
-        sv.PolygonZone(polygon, FRAME_RESOLUTION, triggering_anchors=triggering_anchors)
+        sv.PolygonZone(polygon, triggering_anchors=triggering_anchors)
@@ -975,7 +975,7 @@ def test_get_data_item(
            ),
            False,
            DoesNotRaise(),
-        ),  # foreground object in 2 seperate elements
+        ),  # foreground object in 2 separate elements
        (
            np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]).astype(
                bool
@@ -1032,7 +1032,7 @@ def test_contains_holes(
            4,
            True,
            DoesNotRaise(),
-        ),  # foreground object in 2 seperate elements
+        ),  # foreground object in 2 separate elements
        (
            np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]).astype(
                bool
Author	SHA1	Message	Date
SkalskiP	e52989280a	bump version from `0.23.0` to `0.24.0rc1`	2024-09-04 17:34:21 +02:00
Piotr Skalski	621f8e4d29	Merge pull request #1500 from roboflow/fix/101-coco-algorithm 101 AP algo: do not interpollate precision	2024-09-04 17:27:59 +02:00
LinasKo	7a1c3149fd	Fix typo, false positives -> false negatives	2024-09-04 16:45:06 +02:00
LinasKo	5467db4447	101 AP algo: do not interpollate precision	2024-09-03 19:08:07 +02:00
Onuralp SEZER	96bdcb3dea	Merge pull request #1499 from roboflow/pre-commit-ci-update-config chore(pre_commit): ⬆ pre_commit autoupdate	2024-09-03 00:52:13 +03:00
pre-commit-ci[bot]	54278ea1f0	chore(pre_commit): ⬆ pre_commit autoupdate updates: - [github.com/astral-sh/ruff-pre-commit: v0.6.2 → v0.6.3](https://github.com/astral-sh/ruff-pre-commit/compare/v0.6.2...v0.6.3)	2024-09-02 17:48:28 +00:00
Onuralp SEZER	4e9aec6d2a	Merge pull request #1497 from roboflow/dependabot/pip/mkdocs-material-9.5.34 ⬆️ Bump mkdocs-material from 9.5.33 to 9.5.34	2024-09-02 16:17:47 +03:00
Onuralp SEZER	db79350caa	Merge pull request #1498 from roboflow/dependabot/pip/mkdocstrings-0.26.0 ⬆️ Bump mkdocstrings from 0.25.2 to 0.26.0	2024-09-02 16:17:31 +03:00
dependabot[bot]	a6e0177cb9	⬆️ Bump mkdocstrings from 0.25.2 to 0.26.0 Bumps [mkdocstrings](https://github.com/mkdocstrings/mkdocstrings) from 0.25.2 to 0.26.0. - [Release notes](https://github.com/mkdocstrings/mkdocstrings/releases) - [Changelog](https://github.com/mkdocstrings/mkdocstrings/blob/main/CHANGELOG.md) - [Commits](https://github.com/mkdocstrings/mkdocstrings/compare/0.25.2...0.26.0) --- updated-dependencies: - dependency-name: mkdocstrings dependency-type: direct:development update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com>	2024-09-02 00:12:29 +00:00
dependabot[bot]	d3968822a7	⬆️ Bump mkdocs-material from 9.5.33 to 9.5.34 Bumps [mkdocs-material](https://github.com/squidfunk/mkdocs-material) from 9.5.33 to 9.5.34. - [Release notes](https://github.com/squidfunk/mkdocs-material/releases) - [Changelog](https://github.com/squidfunk/mkdocs-material/blob/master/CHANGELOG) - [Commits](https://github.com/squidfunk/mkdocs-material/compare/9.5.33...9.5.34) --- updated-dependencies: - dependency-name: mkdocs-material dependency-type: direct:development update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com>	2024-09-02 00:09:45 +00:00
Onuralp SEZER	b06d979525	Merge pull request #1494 from Kadermiyanyedi/fix-test-warnings fix(refactor): 🐞 Update deprecated usages of supervision tests to the latest version	2024-08-31 01:47:06 +03:00
Onuralp SEZER	1ed782560d	Merge pull request #1495 from Kadermiyanyedi/fix-typos fix: documentation and notebook spell fixes	2024-08-31 01:42:35 +03:00
Kader Miyanyedi	3c41c9721d	fix: fix typos	2024-08-31 01:31:16 +03:00
Onuralp SEZER	e805212ae6	Merge pull request #1493 from roboflow/dependabot/pip/ruff-0.6.3 ⬆️ Bump ruff from 0.6.2 to 0.6.3	2024-08-31 01:25:52 +03:00
Kader Miyanyedi	80567df1ea	fix: fix test warnings	2024-08-31 01:21:27 +03:00
dependabot[bot]	fab89d2374	⬆️ Bump ruff from 0.6.2 to 0.6.3 Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.2 to 0.6.3. - [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/0.6.2...0.6.3) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:development update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com>	2024-08-30 00:35:59 +00:00
Onuralp SEZER	a937fa5100	Merge pull request #1491 from roboflow/dependabot/pip/jupyterlab-4.2.5 ⬆️ Bump jupyterlab from 4.2.4 to 4.2.5	2024-08-29 22:14:17 +03:00
dependabot[bot]	a298ceb18c	⬆️ Bump jupyterlab from 4.2.4 to 4.2.5 Bumps [jupyterlab](https://github.com/jupyterlab/jupyterlab) from 4.2.4 to 4.2.5. - [Release notes](https://github.com/jupyterlab/jupyterlab/releases) - [Changelog](https://github.com/jupyterlab/jupyterlab/blob/@jupyterlab/lsp@4.2.5/CHANGELOG.md) - [Commits](https://github.com/jupyterlab/jupyterlab/compare/@jupyterlab/lsp@4.2.4...@jupyterlab/lsp@4.2.5) --- updated-dependencies: - dependency-name: jupyterlab dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com>	2024-08-29 18:12:52 +00:00
LinasKo	c7c02bb4de	Merge pull request #1490 from CharlesCNorton/patch-1 fix: correct typo in URL	2024-08-29 15:23:55 +03:00
CharlesCNorton	a7debec87d	fix: correct typo in URL fix: Correct typo in link URL from "disuss.roboflow.com" to "discuss.roboflow.com"	2024-08-28 17:36:04 -04:00
LinasKo	b4b941fe1b	hotfix: mAP docs	2024-08-28 20:59:54 +03:00
LinasKo	3ddc4bd007	Merge pull request #1489 from roboflow/main Merge `main` back into `develop` after `supervision-0.23.0` release.	2024-08-28 20:43:02 +03:00