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

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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@ -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.