OBB Docs updates (ultralytics#7512)

Signed-off-by: Glenn Jocher <[email protected]> Co-authored-by: Muhammad Rizwan Munawar <[email protected]> Co-authored-by: Laughing <[email protected]>
i24E · Jan 11, 2024 · 4dc8c40 · 4dc8c40
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diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -59,17 +59,17 @@ repos:
         args:
           - --ignore-words-list=crate,nd,ned,strack,dota,ane,segway,fo,gool,winn,commend,bloc,nam,afterall
 
-  - repo: https://github.com/PyCQA/docformatter
-    rev: v1.7.5
-    hooks:
-      - id: docformatter
-
   - repo: https://github.com/hadialqattan/pycln
     rev: v2.4.0
     hooks:
       - id: pycln
         args: [--all]
 
+#  - repo: https://github.com/PyCQA/docformatter
+#    rev: v1.7.5
+#    hooks:
+#      - id: docformatter
+
 #  - repo: https://github.com/asottile/yesqa
 #    rev: v1.4.0
 #    hooks:

diff --git a/README.md b/README.md
@@ -236,7 +236,7 @@ Our key integrations with leading AI platforms extend the functionality of Ultra
   <a href="https://roboflow.com/?ref=ultralytics">
     <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" alt="Roboflow logo"></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
-  <a href="https://cutt.ly/yolov5-readme-clearml">
+  <a href="https://clear.ml/">
     <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" alt="ClearML logo"></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
   <a href="https://bit.ly/yolov8-readme-comet">
@@ -246,9 +246,9 @@ Our key integrations with leading AI platforms extend the functionality of Ultra
     <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="NeuralMagic logo"></a>
 </div>
 
-|                                                           Roboflow                                                           |                                                            ClearML ⭐ NEW                                                            |                                                                        Comet ⭐ NEW                                                                        |                                           Neural Magic ⭐ NEW                                           |
-| :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
-| Label and export your custom datasets directly to YOLOv8 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv8 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov8-readme-comet) lets you save YOLOv8 models, resume training, and interactively visualize and debug predictions | Run YOLOv8 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
+|                                                           Roboflow                                                           |                                                  ClearML ⭐ NEW                                                  |                                                                        Comet ⭐ NEW                                                                        |                                           Neural Magic ⭐ NEW                                           |
+| :--------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
+| Label and export your custom datasets directly to YOLOv8 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv8 using [ClearML](https://clear.ml/) (open-source!) | Free forever, [Comet](https://bit.ly/yolov8-readme-comet) lets you save YOLOv8 models, resume training, and interactively visualize and debug predictions | Run YOLOv8 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
 
 ## <div align="center">Ultralytics HUB</div>
 

diff --git a/README.zh-CN.md b/README.zh-CN.md
@@ -238,7 +238,7 @@ Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟
   <a href="https://roboflow.com/?ref=ultralytics">
     <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" alt="Roboflow logo"></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
-  <a href="https://cutt.ly/yolov5-readme-clearml">
+  <a href="https://clear.ml/">
     <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" alt="ClearML logo"></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
   <a href="https://bit.ly/yolov8-readme-comet">
@@ -248,9 +248,9 @@ Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟
     <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="NeuralMagic logo"></a>
 </div>
 
-|                                      Roboflow                                      |                                 ClearML ⭐ NEW                                  |                                     Comet ⭐ NEW                                      |                                  Neural Magic ⭐ NEW                                   |
-| :--------------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | :----------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------: |
-| 使用 [Roboflow](https://roboflow.com/?ref=ultralytics) 将您的自定义数据集直接标记并导出至 YOLOv8 进行训练 | 使用 [ClearML](https://cutt.ly/yolov5-readme-clearml)（开源！）自动跟踪、可视化，甚至远程训练 YOLOv8 | 免费且永久，[Comet](https://bit.ly/yolov8-readme-comet) 让您保存 YOLOv8 模型、恢复训练，并以交互式方式查看和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) 使 YOLOv8 推理速度提高多达 6 倍 |
+|                                      Roboflow                                      |                       ClearML ⭐ NEW                        |                                     Comet ⭐ NEW                                      |                                  Neural Magic ⭐ NEW                                   |
+| :--------------------------------------------------------------------------------: | :--------------------------------------------------------: | :----------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------: |
+| 使用 [Roboflow](https://roboflow.com/?ref=ultralytics) 将您的自定义数据集直接标记并导出至 YOLOv8 进行训练 | 使用 [ClearML](https://clear.ml/)（开源！）自动跟踪、可视化，甚至远程训练 YOLOv8 | 免费且永久，[Comet](https://bit.ly/yolov8-readme-comet) 让您保存 YOLOv8 模型、恢复训练，并以交互式方式查看和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) 使 YOLOv8 推理速度提高多达 6 倍 |
 
 ## <div align="center">Ultralytics HUB</div>
 

diff --git a/docs/en/datasets/explorer/api.md b/docs/en/datasets/explorer/api.md
@@ -8,7 +8,7 @@ keywords: Ultralytics Explorer API, Dataset Exploration, SQL Queries, Vector Sim
 
 ## Introduction
 
-<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/docs/en/datasets/explorer/explorer.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>  
+<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/docs/en/datasets/explorer/explorer.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
 The Explorer API is a Python API for exploring your datasets. It supports filtering and searching your dataset using SQL queries, vector similarity search and semantic search.
 
 ## Installation
@@ -42,7 +42,7 @@ dataframe = explorer.get_similar(idx=0)
     Embeddings table for a given dataset and model pair is only created once and reused. These use [LanceDB](https://lancedb.github.io/lancedb/) under the hood, which scales on-disk, so you can create and reuse embeddings for large datasets like COCO without running out of memory.
 
 In case you want to force update the embeddings table, you can pass `force=True` to `create_embeddings_table` method.
-You can direclty access the LanceDB table object to perform advanced analysis. Learn more about it in [Working with table section](#4-advanced---working-with-embeddings-table)
+You can directly access the LanceDB table object to perform advanced analysis. Learn more about it in [Working with table section](#4-advanced---working-with-embeddings-table)
 
 ## 1. Similarity Search
 

diff --git a/docs/en/datasets/explorer/index.md b/docs/en/datasets/explorer/index.md
@@ -10,7 +10,7 @@ keywords: Ultralytics Explorer, CV Dataset Tools, Semantic Search, SQL Dataset Q
     <img width="1709" alt="Ultralytics Explorer Screenshot 1" src="https://github.com/AyushExel/assets/assets/15766192/85675606-fb7f-4b0c-ad1b-d9d07c919414">
 </p>
 
-<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/docs/en/datasets/explorer/explorer.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>  
+<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/docs/en/datasets/explorer/explorer.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
 Ultralytics Explorer is a tool for exploring CV datasets using semantic search, SQL queries, vector similarity search and even using natural language. It is also a Python API for accessing the same functionality.
 
 ### Installation of optional dependencies

diff --git a/docs/en/modes/predict.md b/docs/en/modes/predict.md
@@ -441,6 +441,7 @@ All Ultralytics `predict()` calls will return a list of `Results` objects:
 | `masks`      | `Masks, optional`     | A Masks object containing the detection masks.                                           |
 | `probs`      | `Probs, optional`     | A Probs object containing probabilities of each class for classification task.           |
 | `keypoints`  | `Keypoints, optional` | A Keypoints object containing detected keypoints for each object.                        |
+| `obb`        | `OBB, optional`       | A OBB object containing the oriented detection bounding boxes.                           |
 | `speed`      | `dict`                | A dictionary of preprocess, inference, and postprocess speeds in milliseconds per image. |
 | `names`      | `dict`                | A dictionary of class names.                                                             |
 | `path`       | `str`                 | The path to the image file.                                                              |
@@ -606,6 +607,44 @@ Here's a table summarizing the methods and properties for the `Probs` class:
 
 For more details see the `Probs` class [documentation](../reference/engine/results.md#ultralytics.engine.results.Probs).
 
+### OBB
+
+`OBB` object can be used to index, manipulate, and convert oriented bounding boxes to different formats.
+
+!!! Example "OBB"
+
+    ```python
+    from ultralytics import YOLO
+
+    # Load a pretrained YOLOv8n model
+    model = YOLO('yolov8n-obb.pt')
+
+    # Run inference on an image
+    results = model('bus.jpg')  # results list
+
+    # View results
+    for r in results:
+        print(r.obb)  # print the OBB object containing the oriented detection bounding boxes
+    ```
+
+Here is a table for the `OBB` class methods and properties, including their name, type, and description:
+
+| Name        | Type                      | Description                                                           |
+|-------------|---------------------------|-----------------------------------------------------------------------|
+| `cpu()`     | Method                    | Move the object to CPU memory.                                        |
+| `numpy()`   | Method                    | Convert the object to a numpy array.                                  |
+| `cuda()`    | Method                    | Move the object to CUDA memory.                                       |
+| `to()`      | Method                    | Move the object to the specified device.                              |
+| `conf`      | Property (`torch.Tensor`) | Return the confidence values of the boxes.                            |
+| `cls`       | Property (`torch.Tensor`) | Return the class values of the boxes.                                 |
+| `id`        | Property (`torch.Tensor`) | Return the track IDs of the boxes (if available).                     |
+| `xyxy`      | Property (`torch.Tensor`) | Return the horizontal boxes in xyxy format.                           |
+| `xywhr`     | Property (`torch.Tensor`) | Return the rotated boxes in xywhr format.                             |
+| `xyxyxyxy`  | Property (`torch.Tensor`) | Return the rotated boxes in xyxyxyxy format.                          |
+| `xyxyxyxyn` | Property (`torch.Tensor`) | Return the rotated boxes in xyxyxyxy format normalized by image size. |
+
+For more details see the `OBB` class [documentation](../reference/engine/results.md#ultralytics.engine.results.OBB).
+
 ## Plotting Results
 
 You can use the `plot()` method of a `Result` objects to visualize predictions. It plots all prediction types (boxes, masks, keypoints, probabilities, etc.) contained in the `Results` object onto a numpy array that can then be shown or saved.

diff --git a/docs/en/reference/data/split_dota.md b/docs/en/reference/data/split_dota.md
@@ -1,3 +1,8 @@
+---
+description: Detailed guide on using YOLO with DOTA dataset for object detection, including dataset preparation, image splitting, and label handling.
+keywords: Ultralytics, YOLO, DOTA dataset, object detection, image processing, python, dataset preparation, image splitting, label handling, YOLO with DOTA, computer vision, AI, machine learning
+---
+
 # Reference for `ultralytics/data/split_dota.py`
 
 !!! Note

diff --git a/docs/en/reference/models/yolo/obb/predict.md b/docs/en/reference/models/yolo/obb/predict.md
@@ -1,3 +1,8 @@
+---
+description: Discover OBBPredictor for YOLO, specializing in Oriented Bounding Box predictions. Essential for advanced object detection with Ultralytics YOLO.
+keywords: Ultralytics, OBBPredictor, YOLO, Oriented Bounding Box, object detection, advanced object detection, YOLO model, deep learning, AI, machine learning, computer vision, OBB detection
+---
+
 # Reference for `ultralytics/models/yolo/obb/predict.py`
 
 !!! Note

diff --git a/docs/en/reference/models/yolo/obb/train.md b/docs/en/reference/models/yolo/obb/train.md
@@ -1,3 +1,8 @@
+---
+description: Master the Ultralytics YOLO OBB Trainer: A specialized tool for training YOLO models using Oriented Bounding Boxes. Features detailed usage, model initialization, and training processes.
+keywords: Ultralytics, YOLO OBB Trainer, Oriented Bounding Box, OBB model training, YOLO model training, computer vision, deep learning, machine learning, YOLO object detection, model initialization, YOLO training process
+---
+
 # Reference for `ultralytics/models/yolo/obb/train.py`
 
 !!! Note

diff --git a/docs/en/reference/models/yolo/obb/val.md b/docs/en/reference/models/yolo/obb/val.md
@@ -1,3 +1,8 @@
+---
+description: Learn about Ultralytics' advanced OBBValidator, an extension of YOLO object detection for oriented bounding box validation.
+keywords: Ultralytics, YOLO, OBBValidator, object detection, oriented bounding box, OBB, machine learning, AI, deep learning, Python, YOLO model, image processing, computer vision, YOLO object detection
+---
+
 # Reference for `ultralytics/models/yolo/obb/val.py`
 
 !!! Note

diff --git a/docs/en/tasks/obb.md b/docs/en/tasks/obb.md
@@ -18,6 +18,11 @@ 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).
 
+
+|                                                    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) |
+
 ## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8)
 
 YOLOv8 pretrained OBB models are shown here, which are pretrained on the [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml) dataset.

diff --git a/docs/en/yolov5/tutorials/clearml_logging_integration.md b/docs/en/yolov5/tutorials/clearml_logging_integration.md
@@ -10,7 +10,7 @@ keywords: ClearML, YOLOv5, Ultralytics, AI toolbox, training data, remote traini
 
 ## About ClearML
 
-[ClearML](https://cutt.ly/yolov5-tutorial-clearml) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️.
+[ClearML](https://clear.ml/) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️.
 
 🔨 Track every YOLOv5 training run in the <b>experiment manager</b>
 
@@ -36,7 +36,7 @@ And so much more. It's up to you how many of these tools you want to use, you ca
 
 To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 options to get one:
 
-Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-tutorial-clearml) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go!
+Either sign up for free to the [ClearML Hosted Service](https://clear.ml/) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go!
 
 - Install the `clearml` python package: