@@ -106,3 +106,70 @@ If you use the hand-keypoints dataset in your research or development work, plea
     The images were collected and used under the respective licenses provided by each platform and are distributed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
 
 We would also like to acknowledge the creator of this dataset, [Rion Dsilva](https://www.linkedin.com/in/rion-dsilva-043464229/), for his great contribution to Vision AI research.
+
+## FAQ
+
+### How do I train a YOLOv8 model on the Hand Keypoints dataset?
+
+To train a YOLOv8 model on the Hand Keypoints dataset, you can use either Python or the command line interface (CLI). Here's an example for training a YOLOv8n-pose model for 100 epochs with an image size of 640:
+
+!!! Example
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n-pose.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="hand-keypoints.yaml", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo pose train data=hand-keypoints.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
+        ```
+
+For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+### What are the key features of the Hand Keypoints dataset?
+
+The Hand Keypoints dataset is designed for advanced pose estimation tasks and includes several key features:
+
+- **Large Dataset**: Contains 26,768 images with hand keypoint annotations.
+- **YOLOv8 Compatibility**: Ready for use with YOLOv8 models.
+- **21 Keypoints**: Detailed hand pose representation, including wrist and finger joints.
+
+For more details, you can explore the [Hand Keypoints Dataset](#introduction) section.
+
+### What applications can benefit from using the Hand Keypoints dataset?
+
+The Hand Keypoints dataset can be applied in various fields, including:
+
+- **Gesture Recognition**: Enhancing human-computer interaction.
+- **AR/VR Controls**: Improving user experience in augmented and virtual reality.
+- **Robotic Manipulation**: Enabling precise control of robotic hands.
+- **Healthcare**: Analyzing hand movements for medical diagnostics.
+- **Animation**: Capturing motion for realistic animations.
+- **Biometric Authentication**: Enhancing security systems.
+
+For more information, refer to the [Applications](#applications) section.
+
+### How is the Hand Keypoints dataset structured?
+
+The Hand Keypoints dataset is divided into two subsets:
+
+1. **Train**: Contains 18,776 images for training pose estimation models.
+2. **Val**: Contains 7,992 images for validation purposes during model training.
+
+This structure ensures a comprehensive training and validation process. For more details, see the [Dataset Structure](#dataset-structure) section.
+
+### How do I use the dataset YAML file for training?
+
+The dataset configuration is defined in a YAML file, which includes paths, classes, and other relevant information. The `hand-keypoints.yaml` file can be found at [hand-keypoints.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/hand-keypoints.yaml).
+
+To use this YAML file for training, specify it in your training script or CLI command as shown in the training example above. For more details, refer to the [Dataset YAML](#dataset-yaml) section.

@@ -58,7 +58,7 @@ Explore the YOLOv8 Docs, a comprehensive resource designed to help you understan
 - **Predict** new images and videos with YOLOv8   [:octicons-image-16: Predict on Images](modes/predict.md){ .md-button }
 - **Train** a new YOLOv8 model on your own custom dataset   [:fontawesome-solid-brain: Train a Model](modes/train.md){ .md-button }
 - **Tasks** YOLOv8 tasks like segment, classify, pose and track   [:material-magnify-expand: Explore Tasks](tasks/index.md){ .md-button }
-- **NEW 🚀 Explore** datasets with advanced semantic and SQL search   [:material-magnify-expand: Explore a Dataset](datasets/explorer/index.md){ .md-button }
+- **[YOLO11](models/yolo11.md) NEW 🚀**: Ultralytics' latest SOTA models   [:material-magnify-expand: Explore a Dataset](models/yolo11.md){ .md-button }
 
 <p align="center">
   <br>
@@ -84,6 +84,7 @@ Explore the YOLOv8 Docs, a comprehensive resource designed to help you understan
 - [YOLOv8](https://github.com/ultralytics/ultralytics) is the latest version of YOLO by Ultralytics. As a cutting-edge, state-of-the-art (SOTA) model, YOLOv8 builds on the success of previous versions, introducing new features and improvements for enhanced performance, flexibility, and efficiency. YOLOv8 supports a full range of vision AI tasks, including [detection](tasks/detect.md), [segmentation](tasks/segment.md), [pose estimation](tasks/pose.md), [tracking](modes/track.md), and [classifica
 - [YOLOv9](models/yolov9.md) introduces innovative methods like Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN).
 - [YOLOv10](models/yolov10.md) is created by researchers from [Tsinghua University](https://www.tsinghua.edu.cn/en/) using the [Ultralytics](https://www.ultralytics.com/) [Python package](https://pypi.org/project/ultralytics/). This version provides real-time [object detection](tasks/detect.md) advancements by introducing an End-to-End head that eliminates Non-Maximum Suppression (NMS) requirements.
+- **[YOLO11](models/yolo11.md) NEW 🚀**: Ultralytics' latest YOLO models delivering state-of-the-art (SOTA) performance across multiple tasks.
 
 ## YOLO Licenses: How is Ultralytics YOLO licensed?
 

@@ -1,19 +1,20 @@
-| Argument        | Type    | Default       | Range         | Description                                                                                                                                                               |
-| --------------- | ------- | ------------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `hsv_h`         | `float` | `0.015`       | `0.0 - 1.0`   | Adjusts the hue of the image by a fraction of the color wheel, introducing color variability. Helps the model generalize across different lighting conditions.            |
-| `hsv_s`         | `float` | `0.7`         | `0.0 - 1.0`   | Alters the saturation of the image by a fraction, affecting the intensity of colors. Useful for simulating different environmental conditions.                            |
-| `hsv_v`         | `float` | `0.4`         | `0.0 - 1.0`   | Modifies the value (brightness) of the image by a fraction, helping the model to perform well under various lighting conditions.                                          |
-| `degrees`       | `float` | `0.0`         | `-180 - +180` | Rotates the image randomly within the specified degree range, improving the model's ability to recognize objects at various orientations.                                 |
-| `translate`     | `float` | `0.1`         | `0.0 - 1.0`   | Translates the image horizontally and vertically by a fraction of the image size, aiding in learning to detect partially visible objects.                                 |
-| `scale`         | `float` | `0.5`         | `>=0.0`       | Scales the image by a gain factor, simulating objects at different distances from the camera.                                                                             |
-| `shear`         | `float` | `0.0`         | `-180 - +180` | Shears the image by a specified degree, mimicking the effect of objects being viewed from different angles.                                                               |
-| `perspective`   | `float` | `0.0`         | `0.0 - 0.001` | Applies a random perspective transformation to the image, enhancing the model's ability to understand objects in 3D space.                                                |
-| `flipud`        | `float` | `0.0`         | `0.0 - 1.0`   | Flips the image upside down with the specified probability, increasing the data variability without affecting the object's characteristics.                               |
-| `fliplr`        | `float` | `0.5`         | `0.0 - 1.0`   | Flips the image left to right with the specified probability, useful for learning symmetrical objects and increasing dataset diversity.                                   |
-| `bgr`           | `float` | `0.0`         | `0.0 - 1.0`   | Flips the image channels from RGB to BGR with the specified probability, useful for increasing robustness to incorrect channel ordering.                                  |
-| `mosaic`        | `float` | `1.0`         | `0.0 - 1.0`   | Combines four training images into one, simulating different scene compositions and object interactions. Highly effective for complex scene understanding.                |
-| `mixup`         | `float` | `0.0`         | `0.0 - 1.0`   | Blends two images and their labels, creating a composite image. Enhances the model's ability to generalize by introducing label noise and visual variability.             |
-| `copy_paste`    | `float` | `0.0`         | `0.0 - 1.0`   | Copies objects from one image and pastes them onto another, useful for increasing object instances and learning object occlusion.                                         |
-| `auto_augment`  | `str`   | `randaugment` | -             | Automatically applies a predefined augmentation policy (`randaugment`, `autoaugment`, `augmix`), optimizing for classification tasks by diversifying the visual features. |
-| `erasing`       | `float` | `0.4`         | `0.0 - 0.9`   | Randomly erases a portion of the image during classification training, encouraging the model to focus on less obvious features for recognition.                           |
-| `crop_fraction` | `float` | `1.0`         | `0.1 - 1.0`   | Crops the classification image to a fraction of its size to emphasize central features and adapt to object scales, reducing background distractions.                      |
+| Argument          | Type    | Default       | Range         | Description                                                                                                                                                               |
+| ----------------- | ------- | ------------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `hsv_h`           | `float` | `0.015`       | `0.0 - 1.0`   | Adjusts the hue of the image by a fraction of the color wheel, introducing color variability. Helps the model generalize across different lighting conditions.            |
+| `hsv_s`           | `float` | `0.7`         | `0.0 - 1.0`   | Alters the saturation of the image by a fraction, affecting the intensity of colors. Useful for simulating different environmental conditions.                            |
+| `hsv_v`           | `float` | `0.4`         | `0.0 - 1.0`   | Modifies the value (brightness) of the image by a fraction, helping the model to perform well under various lighting conditions.                                          |
+| `degrees`         | `float` | `0.0`         | `-180 - +180` | Rotates the image randomly within the specified degree range, improving the model's ability to recognize objects at various orientations.                                 |
+| `translate`       | `float` | `0.1`         | `0.0 - 1.0`   | Translates the image horizontally and vertically by a fraction of the image size, aiding in learning to detect partially visible objects.                                 |
+| `scale`           | `float` | `0.5`         | `>=0.0`       | Scales the image by a gain factor, simulating objects at different distances from the camera.                                                                             |
+| `shear`           | `float` | `0.0`         | `-180 - +180` | Shears the image by a specified degree, mimicking the effect of objects being viewed from different angles.                                                               |
+| `perspective`     | `float` | `0.0`         | `0.0 - 0.001` | Applies a random perspective transformation to the image, enhancing the model's ability to understand objects in 3D space.                                                |
+| `flipud`          | `float` | `0.0`         | `0.0 - 1.0`   | Flips the image upside down with the specified probability, increasing the data variability without affecting the object's characteristics.                               |
+| `fliplr`          | `float` | `0.5`         | `0.0 - 1.0`   | Flips the image left to right with the specified probability, useful for learning symmetrical objects and increasing dataset diversity.                                   |
+| `bgr`             | `float` | `0.0`         | `0.0 - 1.0`   | Flips the image channels from RGB to BGR with the specified probability, useful for increasing robustness to incorrect channel ordering.                                  |
+| `mosaic`          | `float` | `1.0`         | `0.0 - 1.0`   | Combines four training images into one, simulating different scene compositions and object interactions. Highly effective for complex scene understanding.                |
+| `mixup`           | `float` | `0.0`         | `0.0 - 1.0`   | Blends two images and their labels, creating a composite image. Enhances the model's ability to generalize by introducing label noise and visual variability.             |
+| `copy_paste`      | `float` | `0.0`         | `0.0 - 1.0`   | Copies objects from one image and pastes them onto another, useful for increasing object instances and learning object occlusion.                                         |
+| `copy_paste_mode` | `str`   | `flip`        | -             | Copy-Paste augmentation method selection among the options of (`"flip"`, `"mixup"`).                                                                                      |
+| `auto_augment`    | `str`   | `randaugment` | -             | Automatically applies a predefined augmentation policy (`randaugment`, `autoaugment`, `augmix`), optimizing for classification tasks by diversifying the visual features. |
+| `erasing`         | `float` | `0.4`         | `0.0 - 0.9`   | Randomly erases a portion of the image during classification training, encouraging the model to focus on less obvious features for recognition.                           |
+| `crop_fraction`   | `float` | `1.0`         | `0.1 - 1.0`   | Crops the classification image to a fraction of its size to emphasize central features and adapt to object scales, reducing background distractions.                      |

@@ -17,16 +17,17 @@ Here are some of the key models supported:
 3. **[YOLOv5](yolov5.md)**: An improved version of the YOLO architecture by Ultralytics, offering better performance and speed trade-offs compared to previous versions.
 4. **[YOLOv6](yolov6.md)**: Released by [Meituan](https://about.meituan.com/) in 2022, and in use in many of the company's autonomous delivery robots.
 5. **[YOLOv7](yolov7.md)**: Updated YOLO models released in 2022 by the authors of YOLOv4.
-6. **[YOLOv8](yolov8.md) NEW 🚀**: The latest version of the YOLO family, featuring enhanced capabilities such as [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), pose/keypoints estimation, and classification.
+6. **[YOLOv8](yolov8.md)**: The latest version of the YOLO family, featuring enhanced capabilities such as [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), pose/keypoints estimation, and classification.
 7. **[YOLOv9](yolov9.md)**: An experimental model trained on the Ultralytics [YOLOv5](yolov5.md) codebase implementing Programmable Gradient Information (PGI).
 8. **[YOLOv10](yolov10.md)**: By Tsinghua University, featuring NMS-free training and efficiency-accuracy driven architecture, delivering state-of-the-art performance and latency.
-9. **[Segment Anything Model (SAM)](sam.md)**: Meta's original Segment Anything Model (SAM).
-10. **[Segment Anything Model 2 (SAM2)](sam-2.md)**: The next generation of Meta's Segment Anything Model (SAM) for videos and images.
-11. **[Mobile Segment Anything Model (MobileSAM)](mobile-sam.md)**: MobileSAM for mobile applications, by Kyung Hee University.
-12. **[Fast Segment Anything Model (FastSAM)](fast-sam.md)**: FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
-13. **[YOLO-NAS](yolo-nas.md)**: YOLO Neural Architecture Search (NAS) Models.
-14. **[Realtime Detection Transformers (RT-DETR)](rtdetr.md)**: Baidu's PaddlePaddle Realtime Detection [Transformer](https://www.ultralytics.com/glossary/transformer) (RT-DETR) models.
-15. **[YOLO-World](yolo-world.md)**: Real-time Open Vocabulary Object Detection models from Tencent AI Lab.
+9. **[YOLO11](yolo11.md) NEW 🚀**: Ultralytics' latest YOLO models delivering state-of-the-art (SOTA) performance across multiple tasks.
+10. **[Segment Anything Model (SAM)](sam.md)**: Meta's original Segment Anything Model (SAM).
+11. **[Segment Anything Model 2 (SAM2)](sam-2.md)**: The next generation of Meta's Segment Anything Model (SAM) for videos and images.
+12. **[Mobile Segment Anything Model (MobileSAM)](mobile-sam.md)**: MobileSAM for mobile applications, by Kyung Hee University.
+13. **[Fast Segment Anything Model (FastSAM)](fast-sam.md)**: FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
+14. **[YOLO-NAS](yolo-nas.md)**: YOLO Neural Architecture Search (NAS) Models.
+15. **[Realtime Detection Transformers (RT-DETR)](rtdetr.md)**: Baidu's PaddlePaddle Realtime Detection [Transformer](https://www.ultralytics.com/glossary/transformer) (RT-DETR) models.
+16. **[YOLO-World](yolo-world.md)**: Real-time Open Vocabulary Object Detection models from Tencent AI Lab.
 
 <p align="center">
   <br>

@@ -4,7 +4,7 @@ description: Discover YOLOv8, the latest advancement in real-time object detecti
 keywords: YOLOv8, real-time object detection, YOLO series, Ultralytics, computer vision, advanced object detection, AI, machine learning, deep learning
 ---
 
-# YOLOv8
+# Ultralytics YOLOv8
 
 ## Overview
 

@@ -143,6 +143,18 @@ keywords: Ultralytics, YOLO, neural networks, block modules, DFL, Proto, HGStem,
 
 <br><br><hr><br>
 
+## ::: ultralytics.nn.modules.block.C3f
+
+<br><br><hr><br>
+
+## ::: ultralytics.nn.modules.block.C3k2
+
+<br><br><hr><br>
+
+## ::: ultralytics.nn.modules.block.C3k
+
+<br><br><hr><br>
+
 ## ::: ultralytics.nn.modules.block.RepVGGDW
 
 <br><br><hr><br>
@@ -159,10 +171,22 @@ keywords: Ultralytics, YOLO, neural networks, block modules, DFL, Proto, HGStem,
 
 <br><br><hr><br>
 
+## ::: ultralytics.nn.modules.block.PSABlock
+
+<br><br><hr><br>
+
 ## ::: ultralytics.nn.modules.block.PSA
 
 <br><br><hr><br>
 
+## ::: ultralytics.nn.modules.block.C2PSA
+
+<br><br><hr><br>
+
+## ::: ultralytics.nn.modules.block.C2fPSA
+
+<br><br><hr><br>
+
 ## ::: ultralytics.nn.modules.block.SCDown
 
 <br><br>

@@ -1,6 +1,9 @@
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   avatar: https://avatars.githubusercontent.com/u/116908874?v=4
   username: jk4e
+1185102784@qq.com:
+  avatar: null
+  username: null
 130829914+IvorZhu331@users.noreply.github.com:
   avatar: https://avatars.githubusercontent.com/u/130829914?v=4
   username: IvorZhu331