Merge pull request #143 from ultralytics/main

Add YOLOv8-OBB https://youtu.be/Z7Z9pHF8wJc (ultralytics#7780)

docs/en/help/contributing.md

@@ -53,27 +53,62 @@ I have read the CLA Document and I sign the CLA
 
 When adding new functions or classes, please include a [Google-style docstring](https://google.github.io/styleguide/pyguide.html) to provide clear and concise documentation for other developers. This will help ensure that your contributions are easy to understand and maintain.
 
-Example Google-style docstring:
-
-```python
-def example_function(arg1: int, arg2: int) -> bool:
-    """
-    Example function that demonstrates Google-style docstrings.
-
-    Args:
-        arg1 (int): The first argument.
-        arg2 (int): The second argument.
-
-    Returns:
-        (bool): True if successful, False otherwise.
-
-    Examples:
-        >>> result = example_function(1, 2)  # returns False
-    """
-    if arg1 == arg2:
-        return True
-    return False
-```
+!!! Example "Example Docstrings"
+
+    === "Google-style"
+
+         This example shows both Google-style docstrings. Note that both input and output `types` must always be enclosed by parentheses, i.e. `(bool)`.
+         ```python
+         def example_function(arg1, arg2=4):
+             """
+             Example function that demonstrates Google-style docstrings.
+         
+             Args:
+                 arg1 (int): The first argument.
+                 arg2 (int): The second argument. Default value is 4.
+         
+             Returns:
+                 (bool): True if successful, False otherwise.
+         
+             Examples:
+                 >>> result = example_function(1, 2)  # returns False
+             """
+             if arg1 == arg2:
+                 return True
+             return False
+         ```
+
+    === "Google-style with type hints"
+
+         This example shows both Google-style docstrings and argument and return type hints, though both are not required, one can be used without the other.
+         ```python
+         def example_function(arg1: int, arg2: int = 4) -> bool:
+             """
+             Example function that demonstrates Google-style docstrings.
+         
+             Args:
+                 arg1: The first argument.
+                 arg2: The second argument. Default value is 4.
+         
+             Returns:
+                 True if successful, False otherwise.
+         
+             Examples:
+                 >>> result = example_function(1, 2)  # returns False
+             """
+             if arg1 == arg2:
+                 return True
+             return False
+         ```
+
+    === "Single-line"
+
+         Smaller or simpler functions can utilize a single-line docstring. Note the docstring must use 3 double-quotes, and be a complete sentence starting with a capital letter and ending with a period.
+         ```python
+         def example_small_function(arg1: int, arg2: int = 4) -> bool:
+             """Example function that demonstrates a single-line docstring."""
+             return arg1 == arg2
+         ```
 
 ### GitHub Actions CI Tests
 

docs/en/hub/inference-api.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Access object detection capabilities of YOLOv8 via our RESTful API. Learn how to use the YOLO Inference API with Python or CLI for swift object detection.
-keywords: Ultralytics, YOLOv8, Inference API, object detection, RESTful API, Python, CLI, Quickstart
+description: Access object detection capabilities of YOLOv8 via our RESTful API. Learn how to use the YOLO Inference API with Python or cURL for swift object detection.
+keywords: Ultralytics, YOLOv8, Inference API, object detection, RESTful API, Python, cURL, Quickstart
 ---
 
 # YOLO Inference API
@@ -44,9 +44,9 @@ print(response.json())
 
 In this example, replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `path/to/image.jpg` with the path to the image you want to analyze.
 
-## Example Usage with CLI
+## Example Usage with cURL
 
-You can use the YOLO Inference API with the command-line interface (CLI) by utilizing the `curl` command. Replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `image.jpg` with the path to the image you want to analyze:
+You can use the YOLO Inference API with client URL (cURL) by utilizing the `curl` command. Replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `image.jpg` with the path to the image you want to analyze:
 
 ```bash
 curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
@@ -103,11 +103,11 @@ The JSON list contains information about the detected objects, their coordinates
 
 ### Detect Model Format
 
-YOLO detection models, such as `yolov8n.pt`, can return JSON responses from local inference, CLI inference, and Python inference. All of these methods produce the same JSON response format.
+YOLO detection models, such as `yolov8n.pt`, can return JSON responses from local inference, cURL inference, and Python inference. All of these methods produce the same JSON response format.
 
 !!! Example "Detect Model JSON Response"
 
-    === "Local"
+    === "`ultralytics`"
 
         ```python
         from ultralytics import YOLO
@@ -122,7 +122,7 @@ YOLO detection models, such as `yolov8n.pt`, can return JSON responses from loca
         print(results[0].tojson())
         ```
 
-    === "CLI"
+    === "cURL"
 
         ```bash
         curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
@@ -201,11 +201,11 @@ YOLO detection models, such as `yolov8n.pt`, can return JSON responses from loca
 
 ### Segment Model Format
 
-YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses from local inference, CLI inference, and Python inference. All of these methods produce the same JSON response format.
+YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses from local inference, cURL inference, and Python inference. All of these methods produce the same JSON response format.
 
 !!! Example "Segment Model JSON Response"
 
-    === "Local"
+    === "`ultralytics`"
 
         ```python
         from ultralytics import YOLO
@@ -220,7 +220,7 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr
         print(results[0].tojson())
         ```
 
-    === "CLI"
+    === "cURL"
 
         ```bash
         curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
@@ -342,11 +342,11 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr
 
 ### Pose Model Format
 
-YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from local inference, CLI inference, and Python inference. All of these methods produce the same JSON response format.
+YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from local inference, cURL inference, and Python inference. All of these methods produce the same JSON response format.
 
 !!! Example "Pose Model JSON Response"
 
-    === "Local"
+    === "`ultralytics`"
 
         ```python
         from ultralytics import YOLO
@@ -361,7 +361,7 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca
         print(results[0].tojson())
         ```
 
-    === "CLI"
+    === "cURL"
 
         ```bash
         curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \

docs/en/tasks/obb.md

@@ -18,6 +18,19 @@ The output of an oriented object detector is a set of rotated bounding boxes tha
 
     YOLOv8 OBB models use the `-obb` suffix, i.e. `yolov8n-obb.pt` and are pretrained on [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml).
 
+<p align="center">
+  <br>
+  <iframe width="720" height="405" src="https://www.youtube.com/embed/Z7Z9pHF8wJc"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> Object Detection using Ultralytics YOLOv8 Oriented Bounding Boxes (YOLOv8-OBB)
+</p>
+
+## Visual Samples
+
 |                                                    Ships Detection using OBB                                                    |                                                    Vehicle Detection using OBB                                                    |
 |:-------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------:|
 | ![Ships Detection using OBB](https://github.com/RizwanMunawar/ultralytics/assets/62513924/5051d324-416f-4b58-ab62-f1bf9d7134b0) | ![Vehicle Detection using OBB](https://github.com/RizwanMunawar/ultralytics/assets/62513924/9a366262-910a-403b-a5e2-9c68b75700d3) |

docs/mkdocs.yml

@@ -533,7 +533,7 @@ plugins:
       add_desc: False
       add_image: True
       add_share_buttons: True
-      default_image: https://github.com/ultralytics/ultralytics/assets/26833433/6d09221c-c52a-4234-9a5d-b862e93c6529
+      default_image: https://github.com/ultralytics/assets/blob/main/yolov8/banner-yolov8.png
   - mkdocs-jupyter
   - redirects:
       redirect_maps:

pyproject.toml

@@ -93,7 +93,7 @@ dev = [
     "mkdocstrings[python]",
     "mkdocs-jupyter", # for notebooks
     "mkdocs-redirects", # for 301 redirects
-    "mkdocs-ultralytics-plugin>=0.0.38", # for meta descriptions and images, dates and authors
+    "mkdocs-ultralytics-plugin>=0.0.40", # for meta descriptions and images, dates and authors
 ]
 export = [
     "onnx>=1.12.0", # ONNX export