YOLO11 Tasks, Modes, Usage, Macros and Solutions Updates (#16593)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/modes/val.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn how to validate your YOLOv8 model with precise metrics, easy-to-use tools, and custom settings for optimal performance.
-keywords: Ultralytics, YOLOv8, model validation, machine learning, object detection, mAP metrics, Python API, CLI
+description: Learn how to validate your YOLO11 model with precise metrics, easy-to-use tools, and custom settings for optimal performance.
+keywords: Ultralytics, YOLO11, model validation, machine learning, object detection, mAP metrics, Python API, CLI
 ---
 
 # Model Validation with Ultralytics YOLO
@@ -10,7 +10,7 @@ keywords: Ultralytics, YOLOv8, model validation, machine learning, object detect
 
 ## Introduction
 
-Validation is a critical step in the [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) pipeline, allowing you to assess the quality of your trained models. Val mode in Ultralytics YOLOv8 provides a robust suite of tools and metrics for evaluating the performance of your [object detection](https://www.ultralytics.com/glossary/object-detection) models. This guide serves as a complete resource for understanding how to effectively use the Val mode to ensure that your models are both accurate and reliable.
+Validation is a critical step in the [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) pipeline, allowing you to assess the quality of your trained models. Val mode in Ultralytics YOLO11 provides a robust suite of tools and metrics for evaluating the performance of your [object detection](https://www.ultralytics.com/glossary/object-detection) models. This guide serves as a complete resource for understanding how to effectively use the Val mode to ensure that your models are both accurate and reliable.
 
 <p align="center">
   <br>
@@ -25,7 +25,7 @@ Validation is a critical step in the [machine learning](https://www.ultralytics.
 
 ## Why Validate with Ultralytics YOLO?
 
-Here's why using YOLOv8's Val mode is advantageous:
+Here's why using YOLO11's Val mode is advantageous:
 
 - **Precision:** Get accurate metrics like mAP50, mAP75, and mAP50-95 to comprehensively evaluate your model.
 - **Convenience:** Utilize built-in features that remember training settings, simplifying the validation process.
@@ -34,7 +34,7 @@ Here's why using YOLOv8's Val mode is advantageous:
 
 ### Key Features of Val Mode
 
-These are the notable functionalities offered by YOLOv8's Val mode:
+These are the notable functionalities offered by YOLO11's Val mode:
 
 - **Automated Settings:** Models remember their training configurations for straightforward validation.
 - **Multi-Metric Support:** Evaluate your model based on a range of accuracy metrics.
@@ -43,11 +43,11 @@ These are the notable functionalities offered by YOLOv8's Val mode:
 
 !!! tip
 
-    * YOLOv8 models automatically remember their training settings, so you can validate a model at the same image size and on the original dataset easily with just `yolo val model=yolov8n.pt` or `model('yolov8n.pt').val()`
+    * YOLO11 models automatically remember their training settings, so you can validate a model at the same image size and on the original dataset easily with just `yolo val model=yolo11n.pt` or `model('yolo11n.pt').val()`
 
 ## Usage Examples
 
-Validate trained YOLOv8n model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the COCO8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes. See Arguments section below for a full list of export arguments.
+Validate trained YOLO11n model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the COCO8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes. See Arguments section below for a full list of export arguments.
 
 !!! example
 
@@ -57,7 +57,7 @@ Validate trained YOLOv8n model [accuracy](https://www.ultralytics.com/glossary/a
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load an official model
+        model = YOLO("yolo11n.pt")  # load an official model
         model = YOLO("path/to/best.pt")  # load a custom model
 
         # Validate the model
@@ -71,7 +71,7 @@ Validate trained YOLOv8n model [accuracy](https://www.ultralytics.com/glossary/a
     === "CLI"
 
         ```bash
-        yolo detect val model=yolov8n.pt  # val official model
+        yolo detect val model=yolo11n.pt  # val official model
         yolo detect val model=path/to/best.pt  # val custom model
         ```
 
@@ -95,7 +95,7 @@ The below examples showcase YOLO model validation with custom arguments in Pytho
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
 
         # Customize validation settings
         validation_results = model.val(data="coco8.yaml", imgsz=640, batch=16, conf=0.25, iou=0.6, device="0")
@@ -104,20 +104,20 @@ The below examples showcase YOLO model validation with custom arguments in Pytho
     === "CLI"
 
         ```bash
-        yolo val model=yolov8n.pt data=coco8.yaml imgsz=640 batch=16 conf=0.25 iou=0.6 device=0
+        yolo val model=yolo11n.pt data=coco8.yaml imgsz=640 batch=16 conf=0.25 iou=0.6 device=0
         ```
 
 ## FAQ
 
-### How do I validate my YOLOv8 model with Ultralytics?
+### How do I validate my YOLO11 model with Ultralytics?
 
-To validate your YOLOv8 model, you can use the Val mode provided by Ultralytics. For example, using the Python API, you can load a model and run validation with:
+To validate your YOLO11 model, you can use the Val mode provided by Ultralytics. For example, using the Python API, you can load a model and run validation with:
 
 ```python
 from ultralytics import YOLO
 
 # Load a model
-model = YOLO("yolov8n.pt")
+model = YOLO("yolo11n.pt")
 
 # Validate the model
 metrics = model.val()
@@ -127,14 +127,14 @@ print(metrics.box.map)  # map50-95
 Alternatively, you can use the command-line interface (CLI):
 
 ```bash
-yolo val model=yolov8n.pt
+yolo val model=yolo11n.pt
 ```
 
 For further customization, you can adjust various arguments like `imgsz`, `batch`, and `conf` in both Python and CLI modes. Check the [Arguments for YOLO Model Validation](#arguments-for-yolo-model-validation) section for the full list of parameters.
 
-### What metrics can I get from YOLOv8 model validation?
+### What metrics can I get from YOLO11 model validation?
 
-YOLOv8 model validation provides several key metrics to assess model performance. These include:
+YOLO11 model validation provides several key metrics to assess model performance. These include:
 
 - mAP50 (mean Average Precision at IoU threshold 0.5)
 - mAP75 (mean Average Precision at IoU threshold 0.75)
@@ -156,16 +156,16 @@ For a complete performance evaluation, it's crucial to review all these metrics.
 
 Using Ultralytics YOLO for validation provides several advantages:
 
-- **[Precision](https://www.ultralytics.com/glossary/precision):** YOLOv8 offers accurate performance metrics including mAP50, mAP75, and mAP50-95.
+- **[Precision](https://www.ultralytics.com/glossary/precision):** YOLO11 offers accurate performance metrics including mAP50, mAP75, and mAP50-95.
 - **Convenience:** The models remember their training settings, making validation straightforward.
 - **Flexibility:** You can validate against the same or different datasets and image sizes.
 - **Hyperparameter Tuning:** Validation metrics help in fine-tuning models for better performance.
 
 These benefits ensure that your models are evaluated thoroughly and can be optimized for superior results. Learn more about these advantages in the [Why Validate with Ultralytics YOLO](#why-validate-with-ultralytics-yolo) section.
 
-### Can I validate my YOLOv8 model using a custom dataset?
+### Can I validate my YOLO11 model using a custom dataset?
 
-Yes, you can validate your YOLOv8 model using a [custom dataset](https://docs.ultralytics.com/datasets/). Specify the `data` argument with the path to your dataset configuration file. This file should include paths to the [validation data](https://www.ultralytics.com/glossary/validation-data), class names, and other relevant details.
+Yes, you can validate your YOLO11 model using a [custom dataset](https://docs.ultralytics.com/datasets/). Specify the `data` argument with the path to your dataset configuration file. This file should include paths to the [validation data](https://www.ultralytics.com/glossary/validation-data), class names, and other relevant details.
 
 Example in Python:
 
@@ -173,7 +173,7 @@ Example in Python:
 from ultralytics import YOLO
 
 # Load a model
-model = YOLO("yolov8n.pt")
+model = YOLO("yolo11n.pt")
 
 # Validate with a custom dataset
 metrics = model.val(data="path/to/your/custom_dataset.yaml")
@@ -183,12 +183,12 @@ print(metrics.box.map)  # map50-95
 Example using CLI:
 
 ```bash
-yolo val model=yolov8n.pt data=path/to/your/custom_dataset.yaml
+yolo val model=yolo11n.pt data=path/to/your/custom_dataset.yaml
 ```
 
 For more customizable options during validation, see the [Example Validation with Arguments](#example-validation-with-arguments) section.
 
-### How do I save validation results to a JSON file in YOLOv8?
+### How do I save validation results to a JSON file in YOLO11?
 
 To save the validation results to a JSON file, you can set the `save_json` argument to `True` when running validation. This can be done in both the Python API and CLI.
 
@@ -198,7 +198,7 @@ Example in Python:
 from ultralytics import YOLO
 
 # Load a model
-model = YOLO("yolov8n.pt")
+model = YOLO("yolo11n.pt")
 
 # Save validation results to JSON
 metrics = model.val(save_json=True)
@@ -207,7 +207,7 @@ metrics = model.val(save_json=True)
 Example using CLI:
 
 ```bash
-yolo val model=yolov8n.pt save_json=True
+yolo val model=yolo11n.pt save_json=True
 ```
 
 This functionality is particularly useful for further analysis or integration with other tools. Check the [Arguments for YOLO Model Validation](#arguments-for-yolo-model-validation) for more details.