diff --git a/.github/ISSUE_TEMPLATE/bug-report.yml b/.github/ISSUE_TEMPLATE/bug-report.yml
index ff1c83f15a..203250b973 100644
--- a/.github/ISSUE_TEMPLATE/bug-report.yml
+++ b/.github/ISSUE_TEMPLATE/bug-report.yml
@@ -12,7 +12,7 @@ body:
     attributes:
       label: Search before asking
       description: >
-        Please search the [issues](https://github.com/ultralytics/ultralytics/issues) to see if a similar bug report already exists.
+        Please search the Ultralytics [Docs](https://docs.ultralytics.com) and [issues](https://github.com/ultralytics/ultralytics/issues) to see if a similar bug report already exists.
       options:
         - label: >
             I have searched the YOLOv8 [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar bug report.
diff --git a/.github/ISSUE_TEMPLATE/feature-request.yml b/.github/ISSUE_TEMPLATE/feature-request.yml
index 39158a0cff..76fc7b16fe 100644
--- a/.github/ISSUE_TEMPLATE/feature-request.yml
+++ b/.github/ISSUE_TEMPLATE/feature-request.yml
@@ -12,7 +12,7 @@ body:
     attributes:
       label: Search before asking
       description: >
-        Please search the [issues](https://github.com/ultralytics/ultralytics/issues) to see if a similar feature request already exists.
+        Please search the Ultralytics [Docs](https://docs.ultralytics.com) and [issues](https://github.com/ultralytics/ultralytics/issues) to see if a similar feature request already exists.
       options:
         - label: >
             I have searched the YOLOv8 [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar feature requests.
diff --git a/.github/ISSUE_TEMPLATE/question.yml b/.github/ISSUE_TEMPLATE/question.yml
index 258e26baaa..090bc5d281 100644
--- a/.github/ISSUE_TEMPLATE/question.yml
+++ b/.github/ISSUE_TEMPLATE/question.yml
@@ -12,7 +12,7 @@ body:
     attributes:
       label: Search before asking
       description: >
-        Please search the [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/ultralytics/ultralytics/discussions) to see if a similar question already exists.
+        Please search the Ultralytics [Docs](https://docs.ultralytics.com), [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/ultralytics/ultralytics/discussions) to see if a similar question already exists.
       options:
         - label: >
             I have searched the YOLOv8 [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/ultralytics/ultralytics/discussions) and found no similar questions.
diff --git a/.github/workflows/codeql.yaml b/.github/workflows/codeql.yaml
index 8d52a17811..23d3d4e29b 100644
--- a/.github/workflows/codeql.yaml
+++ b/.github/workflows/codeql.yaml
@@ -19,24 +19,24 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        language: [ 'python' ]
+        language: ['python']
         # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python', 'ruby' ]
 
     steps:
-    - name: Checkout repository
-      uses: actions/checkout@v4
+      - name: Checkout repository
+        uses: actions/checkout@v4
 
-    # Initializes the CodeQL tools for scanning.
-    - name: Initialize CodeQL
-      uses: github/codeql-action/init@v2
-      with:
-        languages: ${{ matrix.language }}
-        # If you wish to specify custom queries, you can do so here or in a config file.
-        # By default, queries listed here will override any specified in a config file.
-        # Prefix the list here with "+" to use these queries and those in the config file.
-        # queries: security-extended,security-and-quality
+      # Initializes the CodeQL tools for scanning.
+      - name: Initialize CodeQL
+        uses: github/codeql-action/init@v2
+        with:
+          languages: ${{ matrix.language }}
+          # If you wish to specify custom queries, you can do so here or in a config file.
+          # By default, queries listed here will override any specified in a config file.
+          # Prefix the list here with "+" to use these queries and those in the config file.
+          # queries: security-extended,security-and-quality
 
-    - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@v2
-      with:
-        category: "/language:${{matrix.language}}"
+      - name: Perform CodeQL Analysis
+        uses: github/codeql-action/analyze@v2
+        with:
+          category: "/language:${{matrix.language}}"
diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml
index 5f0289a5f9..224fe57b9a 100644
--- a/.github/workflows/greetings.yml
+++ b/.github/workflows/greetings.yml
@@ -16,7 +16,7 @@ jobs:
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           pr-message: |
-            👋 Hello @${{ github.actor }}, thank you for submitting a YOLOv8 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:
+            👋 Hello @${{ github.actor }}, thank you for submitting an Ultralytics YOLOv8 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:
 
             - ✅ Verify your PR is **up-to-date** with `ultralytics/ultralytics` `main` branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by running `git pull` and `git merge main` locally.
             - ✅ Verify all YOLOv8 Continuous Integration (CI) **checks are passing**.
@@ -26,7 +26,7 @@ jobs:
             See our [Contributing Guide](https://docs.ultralytics.com/help/contributing) for details and let us know if you have any questions!
 
           issue-message: |
-            👋 Hello @${{ github.actor }}, thank you for your interest in YOLOv8 🚀! We recommend a visit to the [YOLOv8 Docs](https://docs.ultralytics.com) for new users where you can find many [Python](https://docs.ultralytics.com/usage/python/) and [CLI](https://docs.ultralytics.com/usage/cli/) usage examples and where many of the most common questions may already be answered.
+            👋 Hello @${{ github.actor }}, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the [Docs](https://docs.ultralytics.com) for new users where you can find many [Python](https://docs.ultralytics.com/usage/python/) and [CLI](https://docs.ultralytics.com/usage/cli/) usage examples and where many of the most common questions may already be answered.
 
             If this is a 🐛 Bug Report, please provide a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/) to help us debug it.
 
diff --git a/README.zh-CN.md b/README.zh-CN.md
index 58d3612ffd..706000078a 100644
--- a/README.zh-CN.md
+++ b/README.zh-CN.md
@@ -46,7 +46,7 @@
 
 ## <div align="center">文档</div>
 
-请参阅下面的快速安装和使用示例，以及 [YOLOv8 文档](https://docs.ultralytics.com) 上有关培训、验证、预测和部署的完整文档。
+请参阅下面的快速安装和使用示例，以及 [YOLOv8 文档](https://docs.ultralytics.com) 上有关训练、验证、预测和部署的完整文档。
 
 <details open>
 <summary>安装</summary>
diff --git a/docs/README.md b/docs/README.md
index b252ee736e..bcf7e0f0c1 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -79,6 +79,8 @@ For multi-language MkDocs sites use the following additional steps:
     open http://localhost:8000  # on macOS
     ```
 
+Note the above steps are combined into the Ultralytics [build_docs.py](https://github.com/ultralytics/ultralytics/blob/main/docs/build_docs.py) script.
+
 ### Deploying Your Documentation Site
 
 To deploy your MkDocs documentation site, you will need to choose a hosting provider and a deployment method. Some popular options include GitHub Pages, GitLab Pages, and Amazon S3.
diff --git a/docs/de/tasks/classify.md b/docs/de/tasks/classify.md
index 76b02bf8b3..eb49323970 100644
--- a/docs/de/tasks/classify.md
+++ b/docs/de/tasks/classify.md
@@ -70,7 +70,7 @@ Trainieren Sie das YOLOv8n-cls-Modell auf dem MNIST160-Datensatz für 100 Epoche
 
 ### Datenformat
 
-Das Datenformat für YOLO-Klassifizierungsdatensätze finden Sie im Detail im [Datenleitfaden](../../datasets/classify/index.md).
+Das Datenformat für YOLO-Klassifizierungsdatensätze finden Sie im Detail im [Datenleitfaden](../../../datasets/classify/index.md).
 
 ## Validieren
 
diff --git a/docs/de/tasks/detect.md b/docs/de/tasks/detect.md
index f79b7dfee7..47fae34c68 100644
--- a/docs/de/tasks/detect.md
+++ b/docs/de/tasks/detect.md
@@ -80,7 +80,7 @@ YOLOv8n auf dem COCO128-Datensatz für 100 Epochen bei Bildgröße 640 trainiere
 
 ### Datenformat
 
-Das Datenformat für YOLO-Erkennungsdatensätze finden Sie detailliert im [Dataset Guide](../../datasets/detect/index.md). Um Ihren vorhandenen Datensatz von anderen Formaten (wie COCO etc.) in das YOLO-Format zu konvertieren, verwenden Sie bitte das [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO)-Tool von Ultralytics.
+Das Datenformat für YOLO-Erkennungsdatensätze finden Sie detailliert im [Dataset Guide](../../../datasets/detect/index.md). Um Ihren vorhandenen Datensatz von anderen Formaten (wie COCO etc.) in das YOLO-Format zu konvertieren, verwenden Sie bitte das [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO)-Tool von Ultralytics.
 
 ## Validierung
 
diff --git a/docs/de/tasks/pose.md b/docs/de/tasks/pose.md
index ef051382b3..16e9cdefe9 100644
--- a/docs/de/tasks/pose.md
+++ b/docs/de/tasks/pose.md
@@ -81,7 +81,7 @@ Trainieren Sie ein YOLOv8-Pose-Modell auf dem COCO128-Pose-Datensatz.
 
 ### Datensatzformat
 
-Das YOLO-Pose-Datensatzformat finden Sie detailliert im [Datensatz-Leitfaden](../../datasets/pose/index.md). Um Ihren bestehenden Datensatz aus anderen Formaten (wie COCO usw.) in das YOLO-Format zu konvertieren, verwenden Sie bitte das [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO)-Tool von Ultralytics.
+Das YOLO-Pose-Datensatzformat finden Sie detailliert im [Datensatz-Leitfaden](../../../datasets/pose/index.md). Um Ihren bestehenden Datensatz aus anderen Formaten (wie COCO usw.) in das YOLO-Format zu konvertieren, verwenden Sie bitte das [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO)-Tool von Ultralytics.
 
 ## Validieren
 
diff --git a/docs/de/tasks/segment.md b/docs/de/tasks/segment.md
index 31f1552a06..52f7fb2d50 100644
--- a/docs/de/tasks/segment.md
+++ b/docs/de/tasks/segment.md
@@ -80,7 +80,7 @@ Trainieren Sie YOLOv8n-seg auf dem COCO128-seg-Datensatz für 100 Epochen mit ei
 
 ### Datenformat
 
-Das YOLO Segmentierungsdatenformat finden Sie detailliert im [Dataset Guide](../../datasets/segment/index.md). Um Ihre vorhandenen Daten aus anderen Formaten (wie COCO usw.) in das YOLO-Format umzuwandeln, verwenden Sie bitte das [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO)-Tool von Ultralytics.
+Das YOLO Segmentierungsdatenformat finden Sie detailliert im [Dataset Guide](../../../datasets/segment/index.md). Um Ihre vorhandenen Daten aus anderen Formaten (wie COCO usw.) in das YOLO-Format umzuwandeln, verwenden Sie bitte das [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO)-Tool von Ultralytics.
 
 ## Val
 
diff --git a/docs/en/guides/hyperparameter-tuning.md b/docs/en/guides/hyperparameter-tuning.md
index 9f16adc6d6..66b90aa186 100644
--- a/docs/en/guides/hyperparameter-tuning.md
+++ b/docs/en/guides/hyperparameter-tuning.md
@@ -188,7 +188,7 @@ This file contains scatter plots generated from `tune_results.csv`, helping you
 
 This directory contains the saved PyTorch models for the last and the best iterations during the hyperparameter tuning process.
 
-- **`last.pt`**: The last.pt weights for the iteration that achieved the best fitness score.
+- **`last.pt`**: The last.pt are the weights from the last epoch of training.
 - **`best.pt`**: The best.pt weights for the iteration that achieved the best fitness score.
 
 Using these results, you can make more informed decisions for your future model trainings and analyses. Feel free to consult these artifacts to understand how well your model performed and how you might improve it further.
diff --git a/docs/es/tasks/classify.md b/docs/es/tasks/classify.md
index 7aa892fdb2..fc1d85bf2d 100644
--- a/docs/es/tasks/classify.md
+++ b/docs/es/tasks/classify.md
@@ -70,7 +70,7 @@ Entrena el modelo YOLOv8n-cls en el conjunto de datos MNIST160 durante 100 époc
 
 ### Formato del conjunto de datos
 
-El formato del conjunto de datos de clasificación YOLO puede encontrarse en detalle en la [Guía de Conjuntos de Datos](../../datasets/classify/index.md).
+El formato del conjunto de datos de clasificación YOLO puede encontrarse en detalle en la [Guía de Conjuntos de Datos](../../../datasets/classify/index.md).
 
 ## Validación
 
diff --git a/docs/es/tasks/detect.md b/docs/es/tasks/detect.md
index 9fe7a31bfa..0ddaf15642 100644
--- a/docs/es/tasks/detect.md
+++ b/docs/es/tasks/detect.md
@@ -80,7 +80,7 @@ Entrena a YOLOv8n en el conjunto de datos COCO128 durante 100 épocas a tamaño
 
 ### Formato del conjunto de datos
 
-El formato del conjunto de datos de detección de YOLO se puede encontrar en detalle en la [Guía de Conjuntos de Datos](../../datasets/detect/index.md). Para convertir tu conjunto de datos existente desde otros formatos (como COCO, etc.) al formato YOLO, por favor usa la herramienta [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) de Ultralytics.
+El formato del conjunto de datos de detección de YOLO se puede encontrar en detalle en la [Guía de Conjuntos de Datos](../../../datasets/detect/index.md). Para convertir tu conjunto de datos existente desde otros formatos (como COCO, etc.) al formato YOLO, por favor usa la herramienta [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) de Ultralytics.
 
 ## Validación
 
diff --git a/docs/es/tasks/pose.md b/docs/es/tasks/pose.md
index 6ef59e6def..80d756d245 100644
--- a/docs/es/tasks/pose.md
+++ b/docs/es/tasks/pose.md
@@ -81,7 +81,7 @@ Entrena un modelo YOLOv8-pose en el conjunto de datos COCO128-pose.
 
 ### Formato del conjunto de datos
 
-El formato del conjunto de datos de pose de YOLO se puede encontrar en detalle en la [Guía de Conjuntos de Datos](../../datasets/pose/index.md). Para convertir tu conjunto de datos existente de otros formatos (como COCO, etc.) al formato de YOLO, usa la herramienta [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) de Ultralytics.
+El formato del conjunto de datos de pose de YOLO se puede encontrar en detalle en la [Guía de Conjuntos de Datos](../../../datasets/pose/index.md). Para convertir tu conjunto de datos existente de otros formatos (como COCO, etc.) al formato de YOLO, usa la herramienta [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) de Ultralytics.
 
 ## Validar
 
diff --git a/docs/es/tasks/segment.md b/docs/es/tasks/segment.md
index ae5db1b83a..5bcf6fa953 100644
--- a/docs/es/tasks/segment.md
+++ b/docs/es/tasks/segment.md
@@ -80,7 +80,7 @@ Entrena el modelo YOLOv8n-seg en el conjunto de datos COCO128-seg durante 100 é
 
 ### Formato del conjunto de datos
 
-El formato del conjunto de datos de segmentación YOLO puede encontrarse detallado en la [Guía de Conjuntos de Datos](../../datasets/segment/index.md). Para convertir tu conjunto de datos existente de otros formatos (como COCO, etc.) al formato YOLO, utiliza la herramienta [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) de Ultralytics.
+El formato del conjunto de datos de segmentación YOLO puede encontrarse detallado en la [Guía de Conjuntos de Datos](../../../datasets/segment/index.md). Para convertir tu conjunto de datos existente de otros formatos (como COCO, etc.) al formato YOLO, utiliza la herramienta [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) de Ultralytics.
 
 ## Validación
 
diff --git a/docs/fr/tasks/classify.md b/docs/fr/tasks/classify.md
index 3647f2f1a6..dd42a3fc12 100644
--- a/docs/fr/tasks/classify.md
+++ b/docs/fr/tasks/classify.md
@@ -70,7 +70,7 @@ Entraînez le modèle YOLOv8n-cls sur le dataset MNIST160 pendant 100 époques a
 
 ### Format du dataset
 
-Le format du dataset de classification YOLO peut être trouvé en détails dans le [Guide des Datasets](../../datasets/classify/index.md).
+Le format du dataset de classification YOLO peut être trouvé en détails dans le [Guide des Datasets](../../../datasets/classify/index.md).
 
 ## Validation
 
@@ -164,7 +164,7 @@ Les formats d'exportation disponibles pour YOLOv8-cls sont présentés dans le t
 | [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n-cls_saved_model/`    | ✅           | `imgsz`, `keras`                                    |
 | [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb`              | `yolov8n-cls.pb`              | ❌           | `imgsz`                                             |
 | [TF Lite](https://www.tensorflow.org/lite)                         | `tflite`          | `yolov8n-cls.tflite`          | ✅           | `imgsz`, `half`, `int8`                             |
-| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)          | `edgetpu`         | `yolov8n-cls_edgetpu.tflite`  | ✅           | `imgsz`                                             |
+| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n-cls_edgetpu.tflite`  | ✅           | `imgsz`                                             |
 | [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n-cls_web_model/`      | ✅           | `imgsz`                                             |
 | [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n-cls_paddle_model/`   | ✅           | `imgsz`                                             |
 | [ncnn](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n-cls_ncnn_model/`     | ✅           | `imgsz`, `half`                                     |
diff --git a/docs/fr/tasks/detect.md b/docs/fr/tasks/detect.md
index f8126ee1d7..ab775a77e8 100644
--- a/docs/fr/tasks/detect.md
+++ b/docs/fr/tasks/detect.md
@@ -80,7 +80,7 @@ Entraînez le modèle YOLOv8n sur le jeu de données COCO128 pendant 100 époque
 
 ### Format des données
 
-Le format des jeux de données de détection YOLO est détaillé dans le [Guide des Jeux de Données](../../datasets/detect/index.md). Pour convertir votre jeu de données existant depuis d'autres formats (comme COCO, etc.) vers le format YOLO, veuillez utiliser l'outil [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) par Ultralytics.
+Le format des jeux de données de détection YOLO est détaillé dans le [Guide des Jeux de Données](../../../datasets/detect/index.md). Pour convertir votre jeu de données existant depuis d'autres formats (comme COCO, etc.) vers le format YOLO, veuillez utiliser l'outil [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) par Ultralytics.
 
 ## Validation
 
diff --git a/docs/fr/tasks/pose.md b/docs/fr/tasks/pose.md
index 99988c5316..7da717e658 100644
--- a/docs/fr/tasks/pose.md
+++ b/docs/fr/tasks/pose.md
@@ -72,7 +72,7 @@ Entraînez un modèle YOLOv8-pose sur le jeu de données COCO128-pose.
 
 ### Format du jeu de données
 
-Le format du jeu de données YOLO pose peut être trouvé en détail dans le [Guide des jeux de données](../../datasets/pose/index.md). Pour convertir votre jeu de données existant à partir d'autres formats (comme COCO, etc.) vers le format YOLO, veuillez utiliser l'outil [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) d'Ultralytics.
+Le format du jeu de données YOLO pose peut être trouvé en détail dans le [Guide des jeux de données](../../../datasets/pose/index.md). Pour convertir votre jeu de données existant à partir d'autres formats (comme COCO, etc.) vers le format YOLO, veuillez utiliser l'outil [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) d'Ultralytics.
 
 ## Val
 
diff --git a/docs/fr/tasks/segment.md b/docs/fr/tasks/segment.md
index d8386948f2..eafc109be7 100644
--- a/docs/fr/tasks/segment.md
+++ b/docs/fr/tasks/segment.md
@@ -80,7 +80,7 @@ Entraînez YOLOv8n-seg sur le jeu de données COCO128-seg pendant 100 époques 
 
 ### Format des données
 
-Le format des données de segmentation YOLO peut être trouvé en détail dans le [Guide du Jeu de Données](../../datasets/segment/index.md). Pour convertir votre jeu de données existant à partir d'autres formats (comme COCO, etc.) au format YOLO, veuillez utiliser l'outil [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) par Ultralytics.
+Le format des données de segmentation YOLO peut être trouvé en détail dans le [Guide du Jeu de Données](../../../datasets/segment/index.md). Pour convertir votre jeu de données existant à partir d'autres formats (comme COCO, etc.) au format YOLO, veuillez utiliser l'outil [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) par Ultralytics.
 
 ## Validation
 
diff --git a/docs/ja/tasks/classify.md b/docs/ja/tasks/classify.md
index 1f82a4e40f..73dff51c7e 100644
--- a/docs/ja/tasks/classify.md
+++ b/docs/ja/tasks/classify.md
@@ -70,7 +70,7 @@ keywords: Ultralytics, YOLOv8, 画像分類, 事前トレーニングされた
 
 ### データセットフォーマット
 
-YOLO分類データセットのフォーマットの詳細は [データセットガイド](../../datasets/classify/index.md) にあります。
+YOLO分類データセットのフォーマットの詳細は [データセットガイド](../../../datasets/classify/index.md) にあります。
 
 ## 検証
 
diff --git a/docs/ja/tasks/detect.md b/docs/ja/tasks/detect.md
index 73b93b993b..59537e961e 100644
--- a/docs/ja/tasks/detect.md
+++ b/docs/ja/tasks/detect.md
@@ -80,7 +80,7 @@ YOLOv8nを画像サイズ640でCOCO128データセットに対して100エポッ
 
 ### データセットの形式
 
-YOLO検出データセットの形式の詳細は、[データセットガイド](../../datasets/detect/index.md)に記載されています。他の形式（COCO等）からYOLO形式に既存のデータセットを変換するには、Ultralyticsの[JSON2YOLO](https://github.com/ultralytics/JSON2YOLO)ツールをご利用ください。
+YOLO検出データセットの形式の詳細は、[データセットガイド](../../../datasets/detect/index.md)に記載されています。他の形式（COCO等）からYOLO形式に既存のデータセットを変換するには、Ultralyticsの[JSON2YOLO](https://github.com/ultralytics/JSON2YOLO)ツールをご利用ください。
 
 ## 検証
 
diff --git a/docs/ja/tasks/pose.md b/docs/ja/tasks/pose.md
index 7ff7454212..f1f2475270 100644
--- a/docs/ja/tasks/pose.md
+++ b/docs/ja/tasks/pose.md
@@ -81,7 +81,7 @@ COCO128-poseデータセットでYOLOv8-poseモデルをトレーニングしま
 
 ### データセットフォーマット
 
-YOLOポーズデータセットフォーマットの詳細は、[データセットガイド](../../datasets/pose/index.md)に記載されています。既存のデータセットを他のフォーマット（COCOなど）からYOLOフォーマットに変換するには、Ultralyticsの[JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) ツールをご使用ください。
+YOLOポーズデータセットフォーマットの詳細は、[データセットガイド](../../../datasets/pose/index.md)に記載されています。既存のデータセットを他のフォーマット（COCOなど）からYOLOフォーマットに変換するには、Ultralyticsの[JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) ツールをご使用ください。
 
 ## Val
 
diff --git a/docs/ja/tasks/segment.md b/docs/ja/tasks/segment.md
index 406ec9d40f..7e1b4e9259 100644
--- a/docs/ja/tasks/segment.md
+++ b/docs/ja/tasks/segment.md
@@ -80,7 +80,7 @@ COCO128-segデータセットで、画像サイズ640でYOLOv8n-segを100エポ
 
 ### データセットフォーマット
 
-YOLOセグメンテーションデータセットのフォーマットの詳細は、[データセットガイド](../../datasets/segment/index.md)で見つけることができます。既存のデータセットを他のフォーマット(例えばCOCOなど)からYOLOフォーマットに変換するには、Ultralyticsの[JSON2YOLO](https://github.com/ultralytics/JSON2YOLO)ツールを使用してください。
+YOLOセグメンテーションデータセットのフォーマットの詳細は、[データセットガイド](../../../datasets/segment/index.md)で見つけることができます。既存のデータセットを他のフォーマット(例えばCOCOなど)からYOLOフォーマットに変換するには、Ultralyticsの[JSON2YOLO](https://github.com/ultralytics/JSON2YOLO)ツールを使用してください。
 
 ## 評価
 
diff --git a/docs/ko/tasks/classify.md b/docs/ko/tasks/classify.md
index 9de926a545..a6e334c5c2 100644
--- a/docs/ko/tasks/classify.md
+++ b/docs/ko/tasks/classify.md
@@ -70,7 +70,7 @@ YOLOv8n-cls 모델을 MNIST160 데이터셋에서 100 에포크 동안 학습시
 
 ### 데이터셋 형식
 
-YOLO 분류 데이터셋 형식은 [데이터셋 가이드](../../datasets/classify/index.md)에서 자세히 확인할 수 있습니다.
+YOLO 분류 데이터셋 형식은 [데이터셋 가이드](../../../datasets/classify/index.md)에서 자세히 확인할 수 있습니다.
 
 ## 검증
 
diff --git a/docs/ko/tasks/detect.md b/docs/ko/tasks/detect.md
index 2946afa40c..6d1bba3459 100644
--- a/docs/ko/tasks/detect.md
+++ b/docs/ko/tasks/detect.md
@@ -80,7 +80,7 @@ COCO128 데이터셋에서 이미지 크기 640으로 YOLOv8n 모델을 100 에
 
 ### 데이터셋 형식
 
-YOLO 감지 데이터셋 형식은 [데이터셋 가이드](../../datasets/detect/index.md)에서 자세히 볼 수 있습니다. 다른 형식(예: COCO 등)의 기존 데이터셋을 YOLO 형식으로 변환하려면 Ultralytics의 [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) 도구를 사용하십시오.
+YOLO 감지 데이터셋 형식은 [데이터셋 가이드](../../../datasets/detect/index.md)에서 자세히 볼 수 있습니다. 다른 형식(예: COCO 등)의 기존 데이터셋을 YOLO 형식으로 변환하려면 Ultralytics의 [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) 도구를 사용하십시오.
 
 ## 검증
 
diff --git a/docs/ko/tasks/pose.md b/docs/ko/tasks/pose.md
index 9500bebaab..3d882805b4 100644
--- a/docs/ko/tasks/pose.md
+++ b/docs/ko/tasks/pose.md
@@ -81,7 +81,7 @@ COCO128-pose 데이터셋에서 YOLOv8-pose 모델 학습하기.
 
 ### 데이터셋 형식
 
-YOLO 포즈 데이터셋 형식에 대한 자세한 내용은 [데이터셋 가이드](../../datasets/pose/index.md)에서 찾아볼 수 있습니다. 기존 데이터셋을 다른 형식(예: COCO 등)에서 YOLO 형식으로 변환하려면 Ultralytics의 [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) 도구를 사용하세요.
+YOLO 포즈 데이터셋 형식에 대한 자세한 내용은 [데이터셋 가이드](../../../datasets/pose/index.md)에서 찾아볼 수 있습니다. 기존 데이터셋을 다른 형식(예: COCO 등)에서 YOLO 형식으로 변환하려면 Ultralytics의 [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) 도구를 사용하세요.
 
 ## 검증
 
diff --git a/docs/ko/tasks/segment.md b/docs/ko/tasks/segment.md
index 6b92d45002..af4b462709 100644
--- a/docs/ko/tasks/segment.md
+++ b/docs/ko/tasks/segment.md
@@ -80,7 +80,7 @@ COCO128-seg 데이터셋에서 이미지 크기 640으로 YOLOv8n-seg을 100 에
 
 ### 데이터셋 형식
 
-YOLO 세그멘테이션 데이터셋 형식은 [데이터셋 가이드](../../datasets/segment/index.md)에서 자세히 확인할 수 있습니다. 기존 데이터셋 (COCO 등)을 YOLO 형식으로 변환하려면 Ultralytics의 [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) 도구를 이용하세요.
+YOLO 세그멘테이션 데이터셋 형식은 [데이터셋 가이드](../../../datasets/segment/index.md)에서 자세히 확인할 수 있습니다. 기존 데이터셋 (COCO 등)을 YOLO 형식으로 변환하려면 Ultralytics의 [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) 도구를 이용하세요.
 
 ## 검증
 
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index 0d15be9501..61c2205f6e 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -118,6 +118,8 @@ extra:
 
 extra_css:
   - stylesheets/style.css
+extra_javascript:
+  - javascript/extra.js
 
 markdown_extensions:
   - admonition
diff --git a/docs/mkdocs_de.yml b/docs/mkdocs_de.yml
index b9627c355b..a2c72e10db 100644
--- a/docs/mkdocs_de.yml
+++ b/docs/mkdocs_de.yml
@@ -117,9 +117,9 @@ extra:
       link: https://ultralytics.com/discord
 
 extra_css:
-  - 'stylesheets/style.css'
+  - stylesheets/style.css
 extra_javascript:
-  - 'javascript/extra.js'
+  - javascript/extra.js
 
 markdown_extensions:
   - admonition
@@ -165,25 +165,25 @@ nav:
           - Segmentierung: tasks/segment.md
           - Klassifizierung: tasks/classify.md
           - Pose: tasks/pose.md
-      - Schnellstart: quickstart.md
-      - Modi:
-          - modes/index.md
-          - Training: modes/train.md
-          - Validierung: modes/val.md
-          - Vorhersage: modes/predict.md
-          - Exportieren: modes/export.md
-          - Verfolgen: modes/track.md
-          - Benchmarking: modes/benchmark.md
-      - Aufgaben:
-          - tasks/index.md
-          - Erkennung: tasks/detect.md
-          - Segmentierung: tasks/segment.md
-          - Klassifizierung: tasks/classify.md
-          - Pose: tasks/pose.md
-      - Modelle:
-          - models/index.md
-      - Datensätze:
-          - datasets/index.md
+  - Schnellstart: quickstart.md
+  - Modi:
+      - modes/index.md
+      - Training: modes/train.md
+      - Validierung: modes/val.md
+      - Vorhersage: modes/predict.md
+      - Exportieren: modes/export.md
+      - Verfolgen: modes/track.md
+      - Benchmarking: modes/benchmark.md
+  - Aufgaben:
+      - tasks/index.md
+      - Erkennung: tasks/detect.md
+      - Segmentierung: tasks/segment.md
+      - Klassifizierung: tasks/classify.md
+      - Pose: tasks/pose.md
+  - Modelle:
+      - models/index.md
+  - Datensätze:
+      - datasets/index.md
 
 # Plugins including 301 redirects navigation ---------------------------------------------------------------------------
 plugins:
diff --git a/docs/mkdocs_es.yml b/docs/mkdocs_es.yml
index 88b54af87b..f9b9c663d8 100644
--- a/docs/mkdocs_es.yml
+++ b/docs/mkdocs_es.yml
@@ -117,9 +117,9 @@ extra:
       link: https://ultralytics.com/discord
 
 extra_css:
-  - 'stylesheets/style.css'
+  - stylesheets/style.css
 extra_javascript:
-  - 'javascript/extra.js'
+  - javascript/extra.js
 
 markdown_extensions:
   - admonition
diff --git a/docs/mkdocs_fr.yml b/docs/mkdocs_fr.yml
index 48cff42daf..c7da2613bf 100644
--- a/docs/mkdocs_fr.yml
+++ b/docs/mkdocs_fr.yml
@@ -117,9 +117,9 @@ extra:
       link: https://ultralytics.com/discord
 
 extra_css:
-  - 'stylesheets/style.css'
+  - stylesheets/style.css
 extra_javascript:
-  - 'javascript/extra.js'
+  - javascript/extra.js
 
 markdown_extensions:
   - admonition
diff --git a/docs/mkdocs_ja.yml b/docs/mkdocs_ja.yml
index 631c5ce63a..5e15543784 100644
--- a/docs/mkdocs_ja.yml
+++ b/docs/mkdocs_ja.yml
@@ -117,9 +117,9 @@ extra:
       link: https://ultralytics.com/discord
 
 extra_css:
-  - 'stylesheets/style.css'
+  - stylesheets/style.css
 extra_javascript:
-  - 'javascript/extra.js'
+  - javascript/extra.js
 
 markdown_extensions:
   - admonition
diff --git a/docs/mkdocs_ko.yml b/docs/mkdocs_ko.yml
index 6e856d20db..b3df73e26a 100644
--- a/docs/mkdocs_ko.yml
+++ b/docs/mkdocs_ko.yml
@@ -117,9 +117,9 @@ extra:
       link: https://ultralytics.com/discord
 
 extra_css:
-  - 'stylesheets/style.css'
+  - stylesheets/style.css
 extra_javascript:
-  - 'javascript/extra.js'
+  - javascript/extra.js
 
 markdown_extensions:
   - admonition
diff --git a/docs/mkdocs_pt.yml b/docs/mkdocs_pt.yml
index 1d77ccb336..677c3dc62c 100644
--- a/docs/mkdocs_pt.yml
+++ b/docs/mkdocs_pt.yml
@@ -117,9 +117,9 @@ extra:
       link: https://ultralytics.com/discord
 
 extra_css:
-  - 'stylesheets/style.css'
+  - stylesheets/style.css
 extra_javascript:
-  - 'javascript/extra.js'
+  - javascript/extra.js
 
 markdown_extensions:
   - admonition
diff --git a/docs/mkdocs_ru.yml b/docs/mkdocs_ru.yml
index 6248b81708..152041eba4 100644
--- a/docs/mkdocs_ru.yml
+++ b/docs/mkdocs_ru.yml
@@ -117,9 +117,9 @@ extra:
       link: https://ultralytics.com/discord
 
 extra_css:
-  - 'stylesheets/style.css'
+  - stylesheets/style.css
 extra_javascript:
-  - 'javascript/extra.js'
+  - javascript/extra.js
 
 markdown_extensions:
   - admonition
diff --git a/docs/mkdocs_zh.yml b/docs/mkdocs_zh.yml
index 0e7da4376b..701ad35587 100644
--- a/docs/mkdocs_zh.yml
+++ b/docs/mkdocs_zh.yml
@@ -117,9 +117,9 @@ extra:
       link: https://ultralytics.com/discord
 
 extra_css:
-  - 'stylesheets/style.css'
+  - stylesheets/style.css
 extra_javascript:
-  - 'javascript/extra.js'
+  - javascript/extra.js
 
 markdown_extensions:
   - admonition
diff --git a/docs/pt/tasks/classify.md b/docs/pt/tasks/classify.md
index 44cb2da049..d953568acd 100644
--- a/docs/pt/tasks/classify.md
+++ b/docs/pt/tasks/classify.md
@@ -70,7 +70,7 @@ Treine o modelo YOLOv8n-cls no dataset MNIST160 por 100 épocas com tamanho de i
 
 ### Formato do dataset
 
-O formato do dataset de classificação YOLO pode ser encontrado em detalhes no [Guia de Datasets](../../datasets/classify/index.md).
+O formato do dataset de classificação YOLO pode ser encontrado em detalhes no [Guia de Datasets](../../../datasets/classify/index.md).
 
 ## Val
 
diff --git a/docs/pt/tasks/detect.md b/docs/pt/tasks/detect.md
index 28070ae499..e942598564 100644
--- a/docs/pt/tasks/detect.md
+++ b/docs/pt/tasks/detect.md
@@ -81,7 +81,7 @@ Treine o YOLOv8n no dataset COCO128 por 100 épocas com tamanho de imagem 640. P
 
 ### Formato do Dataset
 
-O formato do dataset de detecção do YOLO pode ser encontrado em detalhes no [Guia de Datasets](../../datasets/detect/index.md). Para converter seu dataset existente de outros formatos (como COCO, etc.) para o formato YOLO, por favor utilize a ferramenta [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) da Ultralytics.
+O formato do dataset de detecção do YOLO pode ser encontrado em detalhes no [Guia de Datasets](../../../datasets/detect/index.md). Para converter seu dataset existente de outros formatos (como COCO, etc.) para o formato YOLO, por favor utilize a ferramenta [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) da Ultralytics.
 
 ## Validar
 
diff --git a/docs/pt/tasks/pose.md b/docs/pt/tasks/pose.md
index 18c20379c0..dab8ea6451 100644
--- a/docs/pt/tasks/pose.md
+++ b/docs/pt/tasks/pose.md
@@ -83,7 +83,7 @@ Treine um modelo YOLOv8-pose no conjunto de dados COCO128-pose.
 
 ### Formato do conjunto de dados
 
-O formato do conjunto de dados de pose YOLO pode ser encontrado em detalhes no [Guia de Conjuntos de Dados](../../datasets/pose/index.md). Para converter seu conjunto de dados existente de outros formatos (como COCO etc.) para o formato YOLO, por favor, use a ferramenta [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) da Ultralytics.
+O formato do conjunto de dados de pose YOLO pode ser encontrado em detalhes no [Guia de Conjuntos de Dados](../../../datasets/pose/index.md). Para converter seu conjunto de dados existente de outros formatos (como COCO etc.) para o formato YOLO, por favor, use a ferramenta [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) da Ultralytics.
 
 ## Validar
 
diff --git a/docs/pt/tasks/segment.md b/docs/pt/tasks/segment.md
index 8c14b10b4e..6b32e7ffb5 100644
--- a/docs/pt/tasks/segment.md
+++ b/docs/pt/tasks/segment.md
@@ -80,7 +80,7 @@ Treine o modelo YOLOv8n-seg no conjunto de dados COCO128-seg por 100 épocas com
 
 ### Formato do conjunto de dados
 
-O formato do conjunto de dados de segmentação YOLO pode ser encontrado em detalhes no [Guia de Conjuntos de Dados](../../datasets/segment/index.md). Para converter seu conjunto de dados existente de outros formatos (como COCO etc.) para o formato YOLO, utilize a ferramenta [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) da Ultralytics.
+O formato do conjunto de dados de segmentação YOLO pode ser encontrado em detalhes no [Guia de Conjuntos de Dados](../../../datasets/segment/index.md). Para converter seu conjunto de dados existente de outros formatos (como COCO etc.) para o formato YOLO, utilize a ferramenta [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) da Ultralytics.
 
 ## Val
 
diff --git a/docs/ru/tasks/classify.md b/docs/ru/tasks/classify.md
index 76936c6a55..683f0790a0 100644
--- a/docs/ru/tasks/classify.md
+++ b/docs/ru/tasks/classify.md
@@ -70,7 +70,7 @@ keywords: Ultralytics, YOLOv8, классификация изображений
 
 ### Формат набора данных
 
-Формат набора данных для классификации YOLO можно подробно изучить в [Руководстве по наборам данных](../../datasets/classify/index.md).
+Формат набора данных для классификации YOLO можно подробно изучить в [Руководстве по наборам данных](../../../datasets/classify/index.md).
 
 ## Валидация
 
diff --git a/docs/ru/tasks/detect.md b/docs/ru/tasks/detect.md
index 1811e7bf6c..972085f4a8 100644
--- a/docs/ru/tasks/detect.md
+++ b/docs/ru/tasks/detect.md
@@ -80,7 +80,7 @@ keywords: YOLOv8, Ultralytics, обнаружение объектов, пред
 
 ### Формат датасета
 
-Формат датасета для обнаружения YOLO можно найти более подробно в [Руководстве по датасетам](../../datasets/detect/index.md). Чтобы конвертировать ваш существующий датасет из других форматов (например, COCO и т.д.) в формат YOLO, пожалуйста, используйте инструмент [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) от Ultralytics.
+Формат датасета для обнаружения YOLO можно найти более подробно в [Руководстве по датасетам](../../../datasets/detect/index.md). Чтобы конвертировать ваш существующий датасет из других форматов (например, COCO и т.д.) в формат YOLO, пожалуйста, используйте инструмент [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) от Ultralytics.
 
 ## Валидация
 
diff --git a/docs/ru/tasks/pose.md b/docs/ru/tasks/pose.md
index 27d708f8ea..7e645d02ca 100644
--- a/docs/ru/tasks/pose.md
+++ b/docs/ru/tasks/pose.md
@@ -71,7 +71,7 @@ description: Узнайте, как использовать Ultralytics YOLOv8
 
 ### Формат набора данных
 
-Формат набора данных YOLO pose можно найти в подробностях в [Руководстве по наборам данных](../../datasets/pose/index.md). Для преобразования существующего набора данных из других форматов (например, COCO и т.д.) в формат YOLO, пожалуйста, используйте инструмент [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) от Ultralytics.
+Формат набора данных YOLO pose можно найти в подробностях в [Руководстве по наборам данных](../../../datasets/pose/index.md). Для преобразования существующего набора данных из других форматов (например, COCO и т.д.) в формат YOLO, пожалуйста, используйте инструмент [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) от Ultralytics.
 
 ## Проверка
 
diff --git a/docs/ru/tasks/segment.md b/docs/ru/tasks/segment.md
index 673d56cf0b..76abb4e2ab 100644
--- a/docs/ru/tasks/segment.md
+++ b/docs/ru/tasks/segment.md
@@ -81,7 +81,7 @@ keywords: yolov8, сегментация объектов, Ultralytics, набо
 
 ### Формат набора данных
 
-Формат набора данных для сегментации YOLO можно найти детально в [Руководстве по наборам данных](../../datasets/segment/index.md). Чтобы конвертировать свой существующий набор данных из других форматов (например, COCO и т.д.) в формат YOLO, пожалуйста, используйте инструмент [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) от Ultralytics.
+Формат набора данных для сегментации YOLO можно найти детально в [Руководстве по наборам данных](../../../datasets/segment/index.md). Чтобы конвертировать свой существующий набор данных из других форматов (например, COCO и т.д.) в формат YOLO, пожалуйста, используйте инструмент [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) от Ultralytics.
 
 ## Валидация
 
diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py
index 726e11044c..223577dd7e 100644
--- a/ultralytics/__init__.py
+++ b/ultralytics/__init__.py
@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.208'
+__version__ = '8.0.209'
 
 from ultralytics.models import RTDETR, SAM, YOLO
 from ultralytics.models.fastsam import FastSAM
diff --git a/ultralytics/engine/validator.py b/ultralytics/engine/validator.py
index e50cebd5d3..11d7eadb6c 100644
--- a/ultralytics/engine/validator.py
+++ b/ultralytics/engine/validator.py
@@ -77,7 +77,7 @@ class BaseValidator:
         self.args = get_cfg(overrides=args)
         self.dataloader = dataloader
         self.pbar = pbar
-        self.model = None
+        self.stride = None
         self.data = None
         self.device = None
         self.batch_i = None
@@ -146,6 +146,7 @@ class BaseValidator:
                 self.args.workers = 0  # faster CPU val as time dominated by inference, not dataloading
             if not pt:
                 self.args.rect = False
+            self.stride = model.stride  # used in get_dataloader() for padding
             self.dataloader = self.dataloader or self.get_dataloader(self.data.get(self.args.split), self.args.batch)
 
             model.eval()
diff --git a/ultralytics/models/yolo/detect/val.py b/ultralytics/models/yolo/detect/val.py
index 6fca481a32..4d43933080 100644
--- a/ultralytics/models/yolo/detect/val.py
+++ b/ultralytics/models/yolo/detect/val.py
@@ -12,7 +12,6 @@ from ultralytics.utils import LOGGER, ops
 from ultralytics.utils.checks import check_requirements
 from ultralytics.utils.metrics import ConfusionMatrix, DetMetrics, box_iou
 from ultralytics.utils.plotting import output_to_target, plot_images
-from ultralytics.utils.torch_utils import de_parallel
 
 
 class DetectionValidator(BaseValidator):
@@ -191,8 +190,7 @@ class DetectionValidator(BaseValidator):
             mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
             batch (int, optional): Size of batches, this is for `rect`. Defaults to None.
         """
-        gs = max(int(de_parallel(self.model).stride if self.model else 0), 32)
-        return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, stride=gs)
+        return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, stride=self.stride)
 
     def get_dataloader(self, dataset_path, batch_size):
         """Construct and return dataloader."""