Add FAQs to Docs Datasets and Help sections (#14211)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
2024-07-04 20:42:31 +02:00 · 2024-07-04 20:42:31 +02:00 · d5db9c916f
commit d5db9c916f
parent 64862f1b69
73 changed files with 3296 additions and 110 deletions
--- a/docs/en/datasets/detect/african-wildlife.md
+++ b/docs/en/datasets/detect/african-wildlife.md
@ -100,3 +100,48 @@ This example illustrates the variety and complexity of images in the African wil
 ## Citations and Acknowledgments

 The dataset has been released available under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
+
+## FAQ
+
+### What is the African Wildlife Dataset, and how can it be used in computer vision projects?
+
+The African Wildlife Dataset includes images of four common animal species found in South African nature reserves: buffalo, elephant, rhino, and zebra. It is a valuable resource for training computer vision algorithms in object detection and animal identification. The dataset supports various tasks like object tracking, research, and conservation efforts. For more information on its structure and applications, refer to the [Dataset Structure](#dataset-structure) section and [Applications](#applications) of the dataset.
+
+### How do I train a YOLOv8 model using the African Wildlife Dataset?
+
+You can train a YOLOv8 model on the African Wildlife Dataset by using the `african-wildlife.yaml` configuration file. Below is an example of how to train the YOLOv8n model for 100 epochs with an image size of 640:
+
+!!! Example
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="african-wildlife.yaml", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+    
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=african-wildlife.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+For additional training parameters and options, refer to the [Training](../../modes/train.md) documentation.
+
+### Where can I find the YAML configuration file for the African Wildlife Dataset?
+
+The YAML configuration file for the African Wildlife Dataset, named `african-wildlife.yaml`, can be found at [this GitHub link](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/african-wildlife.yaml). This file defines the dataset configuration, including paths, classes, and other details crucial for training machine learning models. See the [Dataset YAML](#dataset-yaml) section for more details.
+
+### Can I see sample images and annotations from the African Wildlife Dataset?
+
+Yes, the African Wildlife Dataset includes a wide variety of images showcasing diverse animal species in their natural habitats. You can view sample images and their corresponding annotations in the [Sample Images and Annotations](#sample-images-and-annotations) section. This section also illustrates the use of mosaicing technique to combine multiple images into one for enriched batch diversity, enhancing the model's generalization ability.
+
+### How can the African Wildlife Dataset be used to support wildlife conservation and research?
+
+The African Wildlife Dataset is ideal for supporting wildlife conservation and research by enabling the training and evaluation of models to identify African wildlife in different habitats. These models can assist in monitoring animal populations, studying their behavior, and recognizing conservation needs. Additionally, the dataset can be utilized for educational purposes, helping students and researchers understand the characteristics and behaviors of different animal species. More details can be found in the [Applications](#applications) section.
--- a/docs/en/datasets/detect/argoverse.md
+++ b/docs/en/datasets/detect/argoverse.md
@ -95,3 +95,59 @@ If you use the Argoverse dataset in your research or development work, please ci
        ```

 We would like to acknowledge Argo AI for creating and maintaining the Argoverse dataset as a valuable resource for the autonomous driving research community. For more information about the Argoverse dataset and its creators, visit the [Argoverse dataset website](https://www.argoverse.org/).
+
+## FAQ
+
+### What is the Argoverse dataset and its key features?
+
+The [Argoverse](https://www.argoverse.org/) dataset, developed by Argo AI, supports autonomous driving research. It includes over 290K labeled 3D object tracks and 5 million object instances across 1,263 distinct scenes. The dataset provides high-resolution camera images, LiDAR point clouds, and annotated HD maps, making it valuable for tasks like 3D tracking, motion forecasting, and stereo depth estimation.
+
+### How can I train an Ultralytics YOLO model using the Argoverse dataset?
+
+To train a YOLOv8 model with the Argoverse dataset, use the provided YAML configuration file and the following code:
+
+!!! Example "Train Example"
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="Argoverse.yaml", epochs=100, imgsz=640)
+        ```
+    
+
+    === "CLI"
+    
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=Argoverse.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+For a detailed explanation of the arguments, refer to the model [Training](../../modes/train.md) page.
+
+### What types of data and annotations are available in the Argoverse dataset?
+
+The Argoverse dataset includes various sensor data types such as high-resolution camera images, LiDAR point clouds, and HD map data. Annotations include 3D bounding boxes, object tracks, and trajectory information. These comprehensive annotations are essential for accurate model training in tasks like 3D object tracking, motion forecasting, and stereo depth estimation.
+
+### How is the Argoverse dataset structured?
+
+The dataset is divided into three main subsets:
+
+1. **Argoverse 3D Tracking**: Contains 113 scenes with over 290K labeled 3D object tracks, focusing on 3D object tracking tasks. It includes LiDAR point clouds, camera images, and sensor calibration information.
+2. **Argoverse Motion Forecasting**: Consists of 324K vehicle trajectories collected from 60 hours of driving data, suitable for motion forecasting tasks.
+3. **Argoverse Stereo Depth Estimation**: Includes over 10K stereo image pairs with corresponding LiDAR point clouds for ground truth depth estimation.
+
+### Where can I download the Argoverse dataset now that it has been removed from Amazon S3?
+
+The Argoverse dataset `*.zip` file, previously available on Amazon S3, can now be manually downloaded from [Google Drive](https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link).
+
+### What is the YAML configuration file used for with the Argoverse dataset?
+
+A YAML file contains the dataset's paths, classes, and other essential information. For the Argoverse dataset, the configuration file, `Argoverse.yaml`, can be found at the following link: [Argoverse.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Argoverse.yaml).
+
+For more information about YAML configurations, see our [datasets](../index.md) guide.
--- a/docs/en/datasets/detect/brain-tumor.md
+++ b/docs/en/datasets/detect/brain-tumor.md
@ -99,3 +99,70 @@ This example highlights the diversity and intricacy of images within the brain t
 ## Citations and Acknowledgments

 The dataset has been released available under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
+
+## FAQ
+
+### What is the structure of the brain tumor dataset available in Ultralytics documentation?
+
+The brain tumor dataset is divided into two subsets: the **training set** consists of 893 images with corresponding annotations, while the **testing set** comprises 223 images with paired annotations. This structured division aids in developing robust and accurate computer vision models for detecting brain tumors. For more information on the dataset structure, visit the [Dataset Structure](#dataset-structure) section.
+
+### How can I train a YOLOv8 model on the brain tumor dataset using Ultralytics?
+
+You can train a YOLOv8 model on the brain tumor dataset for 100 epochs with an image size of 640px using both Python and CLI methods. Below are the examples for both:
+
+!!! Example "Train Example"
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="brain-tumor.yaml", epochs=100, imgsz=640)
+        ```
+    
+
+    === "CLI"
+    
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=brain-tumor.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+For a detailed list of available arguments, refer to the [Training](../../modes/train.md) page.
+
+### What are the benefits of using the brain tumor dataset for AI in healthcare?
+
+Using the brain tumor dataset in AI projects enables early diagnosis and treatment planning for brain tumors. It helps in automating brain tumor identification through computer vision, facilitating accurate and timely medical interventions, and supporting personalized treatment strategies. This application holds significant potential in improving patient outcomes and medical efficiencies.
+
+### How do I perform inference using a fine-tuned YOLOv8 model on the brain tumor dataset?
+
+Inference using a fine-tuned YOLOv8 model can be performed with either Python or CLI approaches. Here are the examples:
+
+!!! Example "Inference Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("path/to/best.pt")  # load a brain-tumor fine-tuned model
+
+        # Inference using the model
+        results = model.predict("https://ultralytics.com/assets/brain-tumor-sample.jpg")
+        ```
+
+    === "CLI"
+    
+        ```bash
+        # Start prediction with a finetuned *.pt model
+        yolo detect predict model='path/to/best.pt' imgsz=640 source="https://ultralytics.com/assets/brain-tumor-sample.jpg"
+        ```
+
+### Where can I find the YAML configuration for the brain tumor dataset?
+
+The YAML configuration file for the brain tumor dataset can be found at [brain-tumor.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/brain-tumor.yaml). This file includes paths, classes, and additional relevant information necessary for training and evaluating models on this dataset.
--- a/docs/en/datasets/detect/coco.md
+++ b/docs/en/datasets/detect/coco.md
@ -113,3 +113,65 @@ If you use the COCO dataset in your research or development work, please cite th
        ```

 We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
+
+## FAQ
+
+### What is the COCO dataset and why is it important for computer vision?
+
+The [COCO dataset](https://cocodataset.org/#home) (Common Objects in Context) is a large-scale dataset used for object detection, segmentation, and captioning. It contains 330K images with detailed annotations for 80 object categories, making it essential for benchmarking and training computer vision models. Researchers use COCO due to its diverse categories and standardized evaluation metrics like mean Average Precision (mAP).
+
+### How can I train a YOLO model using the COCO dataset?
+
+To train a YOLOv8 model using the COCO dataset, you can use the following code snippets:
+
+!!! Example "Train Example"
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="coco.yaml", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+    
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=coco.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+Refer to the [Training page](../../modes/train.md) for more details on available arguments.
+
+### What are the key features of the COCO dataset?
+
+The COCO dataset includes:
+
+- 330K images, with 200K annotated for object detection, segmentation, and captioning.
+- 80 object categories ranging from common items like cars and animals to specific ones like handbags and sports equipment.
+- Standardized evaluation metrics for object detection (mAP) and segmentation (mean Average Recall, mAR).
+- **Mosaicing** technique in training batches to enhance model generalization across various object sizes and contexts.
+
+### Where can I find pretrained YOLOv8 models trained on the COCO dataset?
+
+Pretrained YOLOv8 models on the COCO dataset can be downloaded from the links provided in the documentation. Examples include:
+
+- [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt)
+- [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt)
+- [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt)
+
+These models vary in size, mAP, and inference speed, providing options for different performance and resource requirements.
+
+### How is the COCO dataset structured and how do I use it?
+
+The COCO dataset is split into three subsets:
+
+1. **Train2017**: 118K images for training.
+2. **Val2017**: 5K images for validation during training.
+3. **Test2017**: 20K images for benchmarking trained models. Results need to be submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7384) for performance evaluation.
+
+The dataset's YAML configuration file is available at [coco.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml), which defines paths, classes, and dataset details.
--- a/docs/en/datasets/detect/coco8.md
+++ b/docs/en/datasets/detect/coco8.md
@ -88,3 +88,48 @@ If you use the COCO dataset in your research or development work, please cite th
        ```

 We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
+
+## FAQ
+
+### What is the Ultralytics COCO8 dataset used for?
+
+The Ultralytics COCO8 dataset is a compact yet versatile object detection dataset consisting of the first 8 images from the COCO train 2017 set, with 4 images for training and 4 for validation. It is designed for testing and debugging object detection models and experimentation with new detection approaches. Despite its small size, COCO8 offers enough diversity to act as a sanity check for your training pipelines before deploying larger datasets. For more details, view the [COCO8 dataset](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8.yaml).
+
+### How do I train a YOLOv8 model using the COCO8 dataset?
+
+To train a YOLOv8 model using the COCO8 dataset, you can employ either Python or CLI commands. Here's how you can start:
+
+!!! Example "Train Example"
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+### Why should I use Ultralytics HUB for managing my COCO8 training?
+
+Ultralytics HUB is an all-in-one web tool designed to simplify the training and deployment of YOLO models, including the Ultralytics YOLOv8 models on the COCO8 dataset. It offers cloud training, real-time tracking, and seamless dataset management. HUB allows you to start training with a single click and avoids the complexities of manual setups. Discover more about [Ultralytics HUB](https://hub.ultralytics.com) and its benefits.
+
+### What are the benefits of using mosaic augmentation in training with the COCO8 dataset?
+
+Mosaic augmentation, demonstrated in the COCO8 dataset, combines multiple images into a single image during training. This technique increases the variety of objects and scenes in each training batch, improving the model's ability to generalize across different object sizes, aspect ratios, and contexts. This results in a more robust object detection model. For more details, refer to the [training guide](#usage).
+
+### How can I validate my YOLOv8 model trained on the COCO8 dataset?
+
+Validation of your YOLOv8 model trained on the COCO8 dataset can be performed using the model's validation commands. You can invoke the validation mode via CLI or Python script to evaluate the model's performance using precise metrics. For detailed instructions, visit the [Validation](../../modes/val.md) page.
--- a/docs/en/datasets/detect/globalwheat2020.md
+++ b/docs/en/datasets/detect/globalwheat2020.md
@ -89,3 +89,57 @@ If you use the Global Wheat Head Dataset in your research or development work, p
        ```

 We would like to acknowledge the researchers and institutions that contributed to the creation and maintenance of the Global Wheat Head Dataset as a valuable resource for the plant phenotyping and crop management research community. For more information about the dataset and its creators, visit the [Global Wheat Head Dataset website](https://www.global-wheat.com/).
+
+## FAQ
+
+### What is the Global Wheat Head Dataset used for?
+
+The Global Wheat Head Dataset is primarily used for developing and training deep learning models aimed at wheat head detection. This is crucial for applications in wheat phenotyping and crop management, allowing for more accurate estimations of wheat head density, size, and overall crop yield potential. Accurate detection methods help in assessing crop health and maturity, essential for efficient crop management.
+
+### How do I train a YOLOv8n model on the Global Wheat Head Dataset?
+
+To train a YOLOv8n model on the Global Wheat Head Dataset, you can use the following code snippets. Make sure you have the `GlobalWheat2020.yaml` configuration file specifying dataset paths and classes:
+
+!!! Example "Train Example"
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Load a pre-trained model (recommended for training)
+        model = YOLO("yolov8n.pt")
+
+        # Train the model
+        results = model.train(data="GlobalWheat2020.yaml", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=GlobalWheat2020.yaml model=yolov8n.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 Global Wheat Head Dataset?
+
+Key features of the Global Wheat Head Dataset include:
+
+- Over 3,000 training images from Europe (France, UK, Switzerland) and North America (Canada).
+- Approximately 1,000 test images from Australia, Japan, and China.
+- High variability in wheat head appearances due to different growing environments.
+- Detailed annotations with wheat head bounding boxes to aid object detection models.
+
+These features facilitate the development of robust models capable of generalization across multiple regions.
+
+### Where can I find the configuration YAML file for the Global Wheat Head Dataset?
+
+The configuration YAML file for the Global Wheat Head Dataset, named `GlobalWheat2020.yaml`, is available on GitHub. You can access it at this [link](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/GlobalWheat2020.yaml). This file contains necessary information about dataset paths, classes, and other configuration details needed for model training in Ultralytics YOLO.
+
+### Why is wheat head detection important in crop management?
+
+Wheat head detection is critical in crop management because it enables accurate estimation of wheat head density and size, which are essential for evaluating crop health, maturity, and yield potential. By leveraging deep learning models trained on datasets like the Global Wheat Head Dataset, farmers and researchers can better monitor and manage crops, leading to improved productivity and optimized resource use in agricultural practices. This technological advancement supports sustainable agriculture and food security initiatives.
+
+For more information on applications of AI in agriculture, visit [AI in Agriculture](https://www.ultralytics.com/solutions/ai-in-agriculture).
--- a/docs/en/datasets/detect/index.md
+++ b/docs/en/datasets/detect/index.md
@ -61,6 +61,7 @@ Here's how you can use these formats to train your model:
        # Train the model
        results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
        ```
+
    === "CLI"

        ```bash
@ -111,3 +112,76 @@ You can easily convert labels from the popular COCO dataset format to the YOLO f
 This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format.

 Remember to double-check if the dataset you want to use is compatible with your model and follows the necessary format conventions. Properly formatted datasets are crucial for training successful object detection models.
+
+## FAQ
+
+### What is the Ultralytics YOLO dataset format and how to structure it?
+
+The Ultralytics YOLO format is a structured configuration for defining datasets in your training projects. It involves setting paths to your training, validation, and testing images and corresponding labels. For example:
+
+```yaml
+path: ../datasets/coco8  # dataset root directory
+train: images/train  # training images (relative to 'path')
+val: images/val  # validation images (relative to 'path')
+test:  # optional test images
+names:
+  0: person
+  1: bicycle
+  2: car
+  # ...
+```
+
+Labels are saved in `*.txt` files with one file per image, formatted as `class x_center y_center width height` with normalized coordinates. For a detailed guide, see the [COCO8 dataset example](coco8.md).
+
+### How do I convert a COCO dataset to the YOLO format?
+
+You can convert a COCO dataset to the YOLO format using the Ultralytics conversion tools. Here's a quick method:
+
+```python
+from ultralytics.data.converter import convert_coco
+
+convert_coco(labels_dir="path/to/coco/annotations/")
+```
+
+This code will convert your COCO annotations to YOLO format, enabling seamless integration with Ultralytics YOLO models. For additional details, visit the [Port or Convert Label Formats](#port-or-convert-label-formats) section.
+
+### Which datasets are supported by Ultralytics YOLO for object detection?
+
+Ultralytics YOLO supports a wide range of datasets, including:
+
+- [Argoverse](argoverse.md)
+- [COCO](coco.md)
+- [LVIS](lvis.md)
+- [COCO8](coco8.md)
+- [Global Wheat 2020](globalwheat2020.md)
+- [Objects365](objects365.md)
+- [OpenImagesV7](open-images-v7.md)
+
+Each dataset page provides detailed information on the structure and usage tailored for efficient YOLOv8 training. Explore the full list in the [Supported Datasets](#supported-datasets) section.
+
+### How do I start training a YOLOv8 model using my dataset?
+
+To start training a YOLOv8 model, ensure your dataset is formatted correctly and the paths are defined in a YAML file. Use the following script to begin training:
+
+!!! Example
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        model = YOLO("yolov8n.pt")  # Load a pretrained model
+        results = model.train(data="path/to/your_dataset.yaml", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+    
+        ```bash
+        yolo detect train data=path/to/your_dataset.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+Refer to the [Usage](#usage) section for more details on utilizing different modes, including CLI commands.
+
+### Where can I find practical examples of using Ultralytics YOLO for object detection?
+
+Ultralytics provides numerous examples and practical guides for using YOLOv8 in diverse applications. For a comprehensive overview, visit the [Ultralytics Blog](https://www.ultralytics.com/blog) where you can find case studies, detailed tutorials, and community stories showcasing object detection, segmentation, and more with YOLOv8. For specific examples, check the [Usage](../../modes/predict.md) section in the documentation.
--- a/docs/en/datasets/detect/lvis.md
+++ b/docs/en/datasets/detect/lvis.md
@ -29,7 +29,7 @@ The [LVIS dataset](https://www.lvisdataset.org/) is a large-scale, fine-grained
 - The dataset comprises 1203 object categories, including common objects like cars, bicycles, and animals, as well as more specific categories such as umbrellas, handbags, and sports equipment.
 - Annotations include object bounding boxes, segmentation masks, and captions for each image.
 - LVIS provides standardized evaluation metrics like mean Average Precision (mAP) for object detection, and mean Average Recall (mAR) for segmentation tasks, making it suitable for comparing model performance.
- LVIS uses the exactly the same images as [COCO](./coco.md) dataset, but with different splits and different annotations.
+- LVIS uses exactly the same images as [COCO](./coco.md) dataset, but with different splits and different annotations.

 ## Dataset Structure

@ -107,3 +107,53 @@ If you use the LVIS dataset in your research or development work, please cite th
        ```

 We would like to acknowledge the LVIS Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the LVIS dataset and its creators, visit the [LVIS dataset website](https://www.lvisdataset.org/).
+
+## FAQ
+
+### What is the LVIS dataset, and how is it used in computer vision?
+
+The [LVIS dataset](https://www.lvisdataset.org/) is a large-scale dataset with fine-grained vocabulary-level annotations developed by Facebook AI Research (FAIR). It is primarily used for object detection and instance segmentation, featuring over 1203 object categories and 2 million instance annotations. Researchers and practitioners use it to train and benchmark models like Ultralytics YOLO for advanced computer vision tasks. The dataset's extensive size and diversity make it an essential resource for pushing the boundaries of model performance in detection and segmentation.
+
+### How can I train a YOLOv8n model using the LVIS dataset?
+
+To train a YOLOv8n model on the LVIS dataset for 100 epochs with an image size of 640, follow the example below. This process utilizes Ultralytics' framework, which offers comprehensive training features.
+
+!!! Example "Train Example"
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="lvis.yaml", epochs=100, imgsz=640)
+        ```
+    
+
+    === "CLI"
+    
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=lvis.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+For detailed training configurations, refer to the [Training](../../modes/train.md) documentation.
+
+### How does the LVIS dataset differ from the COCO dataset?
+
+The images in the LVIS dataset are the same as those in the [COCO dataset](./coco.md), but the two differ in terms of splitting and annotations. LVIS provides a larger and more detailed vocabulary with 1203 object categories compared to COCO's 80 categories. Additionally, LVIS focuses on annotation completeness and diversity, aiming to push the limits of object detection and instance segmentation models by offering more nuanced and comprehensive data.
+
+### Why should I use Ultralytics YOLO for training on the LVIS dataset?
+
+Ultralytics YOLO models, including the latest YOLOv8, are optimized for real-time object detection with state-of-the-art accuracy and speed. They support a wide range of annotations, such as the fine-grained ones provided by the LVIS dataset, making them ideal for advanced computer vision applications. Moreover, Ultralytics offers seamless integration with various [training](../../modes/train.md), [validation](../../modes/val.md), and [prediction](../../modes/predict.md) modes, ensuring efficient model development and deployment.
+
+### Can I see some sample annotations from the LVIS dataset?
+
+Yes, the LVIS dataset includes a variety of images with diverse object categories and complex scenes. Here is an example of a sample image along with its annotations:
+
+![LVIS Dataset sample image](https://github.com/ultralytics/ultralytics/assets/26833433/38cc033a-68b0-47f3-a5b8-4ef554362e40)
+
+This mosaiced image demonstrates a training batch composed of multiple dataset images combined into one. Mosaicing increases the variety of objects and scenes within each training batch, enhancing the model's ability to generalize across different contexts. For more details on the LVIS dataset, explore the [LVIS dataset documentation](#key-features).
--- a/docs/en/datasets/detect/objects365.md
+++ b/docs/en/datasets/detect/objects365.md
@ -90,3 +90,51 @@ If you use the Objects365 dataset in your research or development work, please c
        ```

 We would like to acknowledge the team of researchers who created and maintain the Objects365 dataset as a valuable resource for the computer vision research community. For more information about the Objects365 dataset and its creators, visit the [Objects365 dataset website](https://www.objects365.org/).
+
+## FAQ
+
+### What is the Objects365 dataset used for?
+
+The [Objects365 dataset](https://www.objects365.org/) is designed for object detection tasks in machine learning and computer vision. It provides a large-scale, high-quality dataset with 2 million annotated images and 30 million bounding boxes across 365 categories. Leveraging such a diverse dataset helps improve the performance and generalization of object detection models, making it invaluable for research and development in the field.
+
+### How can I train a YOLOv8 model on the Objects365 dataset?
+
+To train a YOLOv8n model using the Objects365 dataset for 100 epochs with an image size of 640, follow these instructions:
+
+!!! Example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="Objects365.yaml", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=Objects365.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+Refer to the [Training](../../modes/train.md) page for a comprehensive list of available arguments.
+
+### Why should I use the Objects365 dataset for my object detection projects?
+
+The Objects365 dataset offers several advantages for object detection tasks:
+1. **Diversity**: It includes 2 million images with objects in diverse scenarios, covering 365 categories.
+2. **High-quality Annotations**: Over 30 million bounding boxes provide comprehensive ground truth data.
+3. **Performance**: Models pre-trained on Objects365 significantly outperform those trained on datasets like ImageNet, leading to better generalization.
+
+### Where can I find the YAML configuration file for the Objects365 dataset?
+
+The YAML configuration file for the Objects365 dataset is available at [Objects365.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Objects365.yaml). This file contains essential information such as dataset paths and class labels, crucial for setting up your training environment.
+
+### How does the dataset structure of Objects365 enhance object detection modeling?
+
+The [Objects365 dataset](https://www.objects365.org/) is organized with 2 million high-resolution images and comprehensive annotations of over 30 million bounding boxes. This structure ensures a robust dataset for training deep learning models in object detection, offering a wide variety of objects and scenarios. Such diversity and volume help in developing models that are more accurate and capable of generalizing well to real-world applications. For more details on the dataset structure, refer to the [Dataset YAML](#dataset-yaml) section.
--- a/docs/en/datasets/detect/open-images-v7.md
+++ b/docs/en/datasets/detect/open-images-v7.md
@ -129,3 +129,71 @@ For those employing Open Images V7 in their work, it's prudent to cite the relev
        ```

 A heartfelt acknowledgment goes out to the Google AI team for creating and maintaining the Open Images V7 dataset. For a deep dive into the dataset and its offerings, navigate to the [official Open Images V7 website](https://storage.googleapis.com/openimages/web/index.html).
+
+## FAQ
+
+### What is the Open Images V7 dataset?
+
+Open Images V7 is an extensive and versatile dataset created by Google, designed to advance research in computer vision. It includes image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives, making it ideal for various computer vision tasks such as object detection, segmentation, and relationship detection.
+
+### How do I train a YOLOv8 model on the Open Images V7 dataset?
+
+To train a YOLOv8 model on the Open Images V7 dataset, you can use both Python and CLI commands. Here's an example of training the YOLOv8n model for 100 epochs with an image size of 640:
+
+!!! Example "Train Example"
+    
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Load a COCO-pretrained YOLOv8n model
+        model = YOLO("yolov8n.pt")
+
+        # Train the model on the Open Images V7 dataset
+        results = model.train(data="open-images-v7.yaml", epochs=100, imgsz=640)
+        ```
+    
+
+    === "CLI"
+    
+        ```bash
+        # Train a COCO-pretrained YOLOv8n model on the Open Images V7 dataset
+        yolo detect train data=open-images-v7.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+For more details on arguments and settings, refer to the [Training](../../modes/train.md) page.
+
+### What are some key features of the Open Images V7 dataset?
+
+The Open Images V7 dataset includes approximately 9 million images with various annotations:
+- **Bounding Boxes**: 16 million bounding boxes across 600 object classes.
+- **Segmentation Masks**: Masks for 2.8 million objects across 350 classes.
+- **Visual Relationships**: 3.3 million annotations indicating relationships, properties, and actions.
+- **Localized Narratives**: 675,000 descriptions combining voice, text, and mouse traces.
+- **Point-Level Labels**: 66.4 million labels across 1.4 million images.
+- **Image-Level Labels**: 61.4 million labels across 20,638 classes.
+
+### What pretrained models are available for the Open Images V7 dataset?
+
+Ultralytics provides several YOLOv8 pretrained models for the Open Images V7 dataset, each with different sizes and performance metrics:
+
+| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+|-------|-----------------------|----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
+| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-oiv7.pt) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 |
+| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-oiv7.pt) | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 |
+| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-oiv7.pt) | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 |
+| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
+| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
+
+### What applications can the Open Images V7 dataset be used for?
+
+The Open Images V7 dataset supports a variety of computer vision tasks including:
+- **Image Classification**
+- **Object Detection**
+- **Instance Segmentation**
+- **Visual Relationship Detection**
+- **Multimodal Image Descriptions**
+
+Its comprehensive annotations and broad scope make it suitable for training and evaluating advanced machine learning models, as highlighted in practical use cases detailed in our [applications](#applications) section.
--- a/docs/en/datasets/detect/roboflow-100.md
+++ b/docs/en/datasets/detect/roboflow-100.md
@ -128,3 +128,91 @@ If you use the Roboflow 100 dataset in your research or development work, please
 Our thanks go to the Roboflow team and all the contributors for their hard work in creating and sustaining the Roboflow 100 dataset.

 If you are interested in exploring more datasets to enhance your object detection and machine learning projects, feel free to visit [our comprehensive dataset collection](../index.md).
+
+## FAQ
+
+### What is the Roboflow 100 dataset, and why is it significant for object detection?
+
+The **Roboflow 100** dataset, developed by [Roboflow](https://roboflow.com/?ref=ultralytics) and sponsored by Intel, is a crucial [object detection](../../tasks/detect.md) benchmark. It features 100 diverse datasets from over 90,000 public datasets, covering domains such as healthcare, aerial imagery, and video games. This diversity ensures that models can adapt to various real-world scenarios, enhancing their robustness and performance.
+
+### How can I use the Roboflow 100 dataset for benchmarking my object detection models?
+
+To use the Roboflow 100 dataset for benchmarking, you can implement the RF100Benchmark class from the Ultralytics library. Here's a brief example:
+
+!!! Example "Benchmarking example"
+
+    === "Python"
+    
+        ```python
+        import os
+        import shutil
+        from pathlib import Path
+
+        from ultralytics.utils.benchmarks import RF100Benchmark
+
+        # Initialize RF100Benchmark and set API key
+        benchmark = RF100Benchmark()
+        benchmark.set_key(api_key="YOUR_ROBOFLOW_API_KEY")
+
+        # Parse dataset and define file paths
+        names, cfg_yamls = benchmark.parse_dataset()
+        val_log_file = Path("ultralytics-benchmarks") / "validation.txt"
+        eval_log_file = Path("ultralytics-benchmarks") / "evaluation.txt"
+
+        # Run benchmarks on each dataset in RF100
+        for ind, path in enumerate(cfg_yamls):
+            path = Path(path)
+            if path.exists():
+                # Fix YAML file and run training
+                benchmark.fix_yaml(str(path))
+                os.system(f"yolo detect train data={path} model=yolov8s.pt epochs=1 batch=16")
+
+                # Run validation and evaluate
+                os.system(f"yolo detect val data={path} model=runs/detect/train/weights/best.pt > {val_log_file} 2>&1")
+                benchmark.evaluate(str(path), str(val_log_file), str(eval_log_file), ind)
+
+                # Remove 'runs' directory
+                runs_dir = Path.cwd() / "runs"
+                shutil.rmtree(runs_dir)
+            else:
+                print("YAML file path does not exist")
+                continue
+
+        print("RF100 Benchmarking completed!")
+        ```
+
+### Which domains are covered by the Roboflow 100 dataset?
+
+The **Roboflow 100** dataset spans seven domains, each providing unique challenges and applications for object detection models:
+
+1. **Aerial**: 7 datasets, 9,683 images, 24 classes
+2. **Video Games**: 7 datasets, 11,579 images, 88 classes
+3. **Microscopic**: 11 datasets, 13,378 images, 28 classes
+4. **Underwater**: 5 datasets, 18,003 images, 39 classes
+5. **Documents**: 8 datasets, 24,813 images, 90 classes
+6. **Electromagnetic**: 12 datasets, 36,381 images, 41 classes
+7. **Real World**: 50 datasets, 110,615 images, 495 classes
+
+This setup allows for extensive and varied testing of models across different real-world applications.
+
+### How do I access and download the Roboflow 100 dataset?
+
+The **Roboflow 100** dataset is accessible on [GitHub](https://github.com/roboflow/roboflow-100-benchmark) and [Roboflow Universe](https://universe.roboflow.com/roboflow-100). You can download the entire dataset from GitHub or select individual datasets on Roboflow Universe using the export button.
+
+### What should I include when citing the Roboflow 100 dataset in my research?
+
+When using the Roboflow 100 dataset in your research, ensure to properly cite it. Here is the recommended citation:
+
+!!! Quote
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{2211.13523,
+            Author = {Floriana Ciaglia and Francesco Saverio Zuppichini and Paul Guerrie and Mark McQuade and Jacob Solawetz},
+            Title = {Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark},
+            Eprint = {arXiv:2211.13523},
+        }
+        ```
+
+For more details, you can refer to our [comprehensive dataset collection](../index.md).
--- a/docs/en/datasets/detect/signature.md
+++ b/docs/en/datasets/detect/signature.md
@ -88,3 +88,83 @@ This example illustrates the variety and complexity of images in the signature D
 ## Citations and Acknowledgments

 The dataset has been released available under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
+
+## FAQ
+
+### What is the Signature Detection Dataset, and how can it be used?
+
+The Signature Detection Dataset is a collection of annotated images aimed at detecting human signatures within various document types. It can be applied in computer vision tasks such as object detection and tracking, primarily for document verification, fraud detection, and archival research. This dataset helps train models to recognize signatures in different contexts, making it valuable for both research and practical applications.
+
+### How do I train a YOLOv8n model on the Signature Detection Dataset?
+
+To train a YOLOv8n model on the Signature Detection Dataset, follow these steps:
+
+1. Download the `signature.yaml` dataset configuration file from [signature.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/signature.yaml).
+2. Use the following Python script or CLI command to start training:
+
+!!! Example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a pretrained model
+        model = YOLO("yolov8n.pt")
+
+        # Train the model
+        results = model.train(data="signature.yaml", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        yolo detect train data=signature.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+For more details, refer to the [Training](../../modes/train.md) page.
+
+### What are the main applications of the Signature Detection Dataset?
+
+The Signature Detection Dataset can be used for:
+
+1. **Document Verification**: Automatically verifying the presence and authenticity of human signatures in documents.
+2. **Fraud Detection**: Identifying forged or fraudulent signatures in legal and financial documents.
+3. **Archival Research**: Assisting historians and archivists in the digital analysis and cataloging of historical documents.
+4. **Education**: Supporting academic research and teaching in the fields of computer vision and machine learning.
+
+### How can I perform inference using a model trained on the Signature Detection Dataset?
+
+To perform inference using a model trained on the Signature Detection Dataset, follow these steps:
+
+1. Load your fine-tuned model.
+2. Use the below Python script or CLI command to perform inference:
+
+!!! Example "Inference Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load the fine-tuned model
+        model = YOLO("path/to/best.pt")
+
+        # Perform inference
+        results = model.predict("https://ultralytics.com/assets/signature-s.mp4", conf=0.75)
+        ```
+
+    === "CLI"
+
+        ```bash
+        yolo detect predict model='path/to/best.pt' imgsz=640 source="https://ultralytics.com/assets/signature-s.mp4" conf=0.75
+        ```
+
+### What is the structure of the Signature Detection Dataset, and where can I find more information?
+
+The Signature Detection Dataset is divided into two subsets:
+
+- **Training Set**: Contains 143 images with annotations.
+- **Validation Set**: Includes 35 images with annotations.
+
+For detailed information, you can refer to the [Dataset Structure](#dataset-structure) section. Additionally, view the complete dataset configuration in the `signature.yaml` file located at [signature.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/signature.yaml).
--- a/docs/en/datasets/detect/sku-110k.md
+++ b/docs/en/datasets/detect/sku-110k.md
@ -91,3 +91,80 @@ If you use the SKU-110k dataset in your research or development work, please cit
        ```

 We would like to acknowledge Eran Goldman et al. for creating and maintaining the SKU-110k dataset as a valuable resource for the computer vision research community. For more information about the SKU-110k dataset and its creators, visit the [SKU-110k dataset GitHub repository](https://github.com/eg4000/SKU110K_CVPR19).
+
+## FAQ
+
+### What is the SKU-110k dataset and why is it important for object detection?
+
+The SKU-110k dataset consists of densely packed retail shelf images designed to aid research in object detection tasks. Developed by Eran Goldman et al., it includes over 110,000 unique SKU categories. Its importance lies in its ability to challenge state-of-the-art object detectors with diverse object appearances and close proximity, making it an invaluable resource for researchers and practitioners in computer vision. Learn more about the dataset's structure and applications in our [SKU-110k Dataset](#sku-110k-dataset) section.
+
+### How do I train a YOLOv8 model using the SKU-110k dataset?
+
+Training a YOLOv8 model on the SKU-110k dataset is straightforward. Here's an example to train a YOLOv8n model for 100 epochs with an image size of 640:
+
+!!! Example "Train Example"
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="SKU-110K.yaml", epochs=100, imgsz=640)
+        ```
+    
+
+    === "CLI"
+    
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=SKU-110K.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+### What are the main subsets of the SKU-110k dataset?
+
+The SKU-110k dataset is organized into three main subsets:
+
+1. **Training set**: Contains images and annotations used for training object detection models.
+2. **Validation set**: Consists of images and annotations used for model validation during training.
+3. **Test set**: Designed for the final evaluation of trained object detection models.
+
+Refer to the [Dataset Structure](#dataset-structure) section for more details.
+
+### How do I configure the SKU-110k dataset for training?
+
+The SKU-110k dataset configuration is defined in a YAML file, which includes details about the dataset's paths, classes, and other relevant information. The `SKU-110K.yaml` file is maintained at [SKU-110K.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/SKU-110K.yaml). For example, you can train a model using this configuration as shown in our [Usage](#usage) section.
+
+### What are the key features of the SKU-110k dataset in the context of deep learning?
+
+The SKU-110k dataset features images of store shelves from around the world, showcasing densely packed objects that pose significant challenges for object detectors:
+
+- Over 110,000 unique SKU categories
+- Diverse object appearances
+- Annotations include bounding boxes and SKU category labels
+
+These features make the SKU-110k dataset particularly valuable for training and evaluating deep learning models in object detection tasks. For more details, see the [Key Features](#key-features) section.
+
+### How do I cite the SKU-110k dataset in my research?
+
+If you use the SKU-110k dataset in your research or development work, please cite the following paper:
+
+!!! Quote ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @inproceedings{goldman2019dense,
+         author    = {Eran Goldman and Roei Herzig and Aviv Eisenschtat and Jacob Goldberger and Tal Hassner},
+         title     = {Precise Detection in Densely Packed Scenes},
+         booktitle = {Proc. Conf. Comput. Vision Pattern Recognition (CVPR)},
+         year      = {2019}
+        }
+        ```
+
+More information about the dataset can be found in the [Citations and Acknowledgments](#citations-and-acknowledgments) section.
--- a/docs/en/datasets/detect/visdrone.md
+++ b/docs/en/datasets/detect/visdrone.md
@ -90,3 +90,74 @@ If you use the VisDrone dataset in your research or development work, please cit
        ```

 We would like to acknowledge the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China, for creating and maintaining the VisDrone dataset as a valuable resource for the drone-based computer vision research community. For more information about the VisDrone dataset and its creators, visit the [VisDrone Dataset GitHub repository](https://github.com/VisDrone/VisDrone-Dataset).
+
+## FAQ
+
+### What is the VisDrone Dataset and what are its key features?
+
+The [VisDrone Dataset](https://github.com/VisDrone/VisDrone-Dataset) is a large-scale benchmark created by the AISKYEYE team at Tianjin University, China. It is designed for various computer vision tasks related to drone-based image and video analysis. Key features include:
+- **Composition**: 288 video clips with 261,908 frames and 10,209 static images.
+- **Annotations**: Over 2.6 million bounding boxes for objects like pedestrians, cars, bicycles, and tricycles.
+- **Diversity**: Collected across 14 cities, in urban and rural settings, under different weather and lighting conditions.
+- **Tasks**: Split into five main tasks—object detection in images and videos, single-object and multi-object tracking, and crowd counting.
+
+### How can I use the VisDrone Dataset to train a YOLOv8 model with Ultralytics?
+
+To train a YOLOv8 model on the VisDrone dataset for 100 epochs with an image size of 640, you can follow these steps:
+
+!!! Example "Train Example"
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Load a pretrained model
+        model = YOLO("yolov8n.pt")
+
+        # Train the model
+        results = model.train(data="VisDrone.yaml", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+    
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=VisDrone.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+For additional configuration options, please refer to the model [Training](../../modes/train.md) page.
+
+### What are the main subsets of the VisDrone dataset and their applications?
+
+The VisDrone dataset is divided into five main subsets, each tailored for a specific computer vision task:
+1. **Task 1**: Object detection in images.
+2. **Task 2**: Object detection in videos.
+3. **Task 3**: Single-object tracking.
+4. **Task 4**: Multi-object tracking.
+5. **Task 5**: Crowd counting.
+
+These subsets are widely used for training and evaluating deep learning models in drone-based applications such as surveillance, traffic monitoring, and public safety.
+
+### Where can I find the configuration file for the VisDrone dataset in Ultralytics?
+
+The configuration file for the VisDrone dataset, `VisDrone.yaml`, can be found in the Ultralytics repository at the following link:
+[VisDrone.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VisDrone.yaml).
+
+### How can I cite the VisDrone dataset if I use it in my research?
+
+If you use the VisDrone dataset in your research or development work, please cite the following paper:
+
+!!! Quote "BibTeX"
+
+    ```bibtex
+    @ARTICLE{9573394,
+      author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
+      journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+      title={Detection and Tracking Meet Drones Challenge},
+      year={2021},
+      volume={},
+      number={},
+      pages={1-1},
+      doi={10.1109/TPAMI.2021.3119563}}
+    ```
--- a/docs/en/datasets/detect/voc.md
+++ b/docs/en/datasets/detect/voc.md
@ -92,3 +92,46 @@ If you use the VOC dataset in your research or development work, please cite the
        ```

 We would like to acknowledge the PASCAL VOC Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the VOC dataset and its creators, visit the [PASCAL VOC dataset website](http://host.robots.ox.ac.uk/pascal/VOC/).
+
+## FAQ
+
+### What is the PASCAL VOC dataset and why is it important for computer vision tasks?
+
+The [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) (Visual Object Classes) dataset is a renowned benchmark for object detection, segmentation, and classification in computer vision. It includes comprehensive annotations like bounding boxes, class labels, and segmentation masks across 20 different object categories. Researchers use it widely to evaluate the performance of models like Faster R-CNN, YOLO, and Mask R-CNN due to its standardized evaluation metrics such as mean Average Precision (mAP).
+
+### How do I train a YOLOv8 model using the VOC dataset?
+
+To train a YOLOv8 model with the VOC dataset, you need the dataset configuration in a YAML file. Here's an example to start training a YOLOv8n model for 100 epochs with an image size of 640:
+
+!!! Example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="VOC.yaml", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=VOC.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+### What are the primary challenges included in the VOC dataset?
+
+The VOC dataset includes two main challenges: VOC2007 and VOC2012. These challenges test object detection, segmentation, and classification across 20 diverse object categories. Each image is meticulously annotated with bounding boxes, class labels, and segmentation masks. The challenges provide standardized metrics like mAP, facilitating the comparison and benchmarking of different computer vision models.
+
+### How does the PASCAL VOC dataset enhance model benchmarking and evaluation?
+
+The PASCAL VOC dataset enhances model benchmarking and evaluation through its detailed annotations and standardized metrics like mean Average Precision (mAP). These metrics are crucial for assessing the performance of object detection and classification models. The dataset's diverse and complex images ensure comprehensive model evaluation across various real-world scenarios.
+
+### How do I use the VOC dataset for semantic segmentation in YOLO models?
+
+To use the VOC dataset for semantic segmentation tasks with YOLO models, you need to configure the dataset properly in a YAML file. The YAML file defines paths and classes needed for training segmentation models. Check the VOC dataset YAML configuration file at [VOC.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VOC.yaml) for detailed setups.
--- a/docs/en/datasets/detect/xview.md
+++ b/docs/en/datasets/detect/xview.md
@ -95,3 +95,68 @@ If you use the xView dataset in your research or development work, please cite t
        ```

 We would like to acknowledge the [Defense Innovation Unit](https://www.diu.mil/) (DIU) and the creators of the xView dataset for their valuable contribution to the computer vision research community. For more information about the xView dataset and its creators, visit the [xView dataset website](http://xviewdataset.org/).
+
+## FAQ
+
+### What is the xView dataset and how does it benefit computer vision research?
+
+The [xView](http://xviewdataset.org/) dataset is one of the largest publicly available collections of high-resolution overhead imagery, containing over 1 million object instances across 60 classes. It is designed to enhance various facets of computer vision research such as reducing the minimum resolution for detection, improving learning efficiency, discovering more object classes, and advancing fine-grained object detection.
+
+### How can I use Ultralytics YOLO to train a model on the xView dataset?
+
+To train a model on the xView dataset using Ultralytics YOLO, follow these steps:
+
+!!! Example "Train Example"
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="xView.yaml", epochs=100, imgsz=640)
+        ```
+    
+
+    === "CLI"
+    
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=xView.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+For detailed arguments and settings, refer to the model [Training](../../modes/train.md) page.
+
+### What are the key features of the xView dataset?
+
+The xView dataset stands out due to its comprehensive set of features:
+- Over 1 million object instances across 60 distinct classes.
+- High-resolution imagery at 0.3 meters.
+- Diverse object types including small, rare, and fine-grained objects, all annotated with bounding boxes.
+- Availability of a pre-trained baseline model and examples in TensorFlow and PyTorch.
+
+### What is the dataset structure of xView, and how is it annotated?
+
+The xView dataset comprises high-resolution satellite images collected from WorldView-3 satellites at a 0.3m ground sample distance. It encompasses over 1 million objects across 60 classes in approximately 1,400 km² of imagery. Each object within the dataset is annotated with bounding boxes, making it ideal for training and evaluating deep learning models for object detection in overhead imagery. For a detailed overview, you can look at the dataset structure section [here](#dataset-structure).
+
+### How do I cite the xView dataset in my research?
+
+If you utilize the xView dataset in your research, please cite the following paper:
+
+!!! Quote "BibTeX"
+
+    ```bibtex
+    @misc{lam2018xview,
+          title={xView: Objects in Context in Overhead Imagery},
+          author={Darius Lam and Richard Kuzma and Kevin McGee and Samuel Dooley and Michael Laielli and Matthew Klaric and Yaroslav Bulatov and Brendan McCord},
+          year={2018},
+          eprint={1802.07856},
+          archivePrefix={arXiv},
+          primaryClass={cs.CV}
+    }
+    ```
+   
+For more information about the xView dataset, visit the official [xView dataset website](http://xviewdataset.org/).