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
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--- a/docs/en/datasets/segment/coco.md
+++ b/docs/en/datasets/segment/coco.md
@ -102,3 +102,63 @@ If you use the COCO-Seg dataset in your research or development work, please cit
        ```

 We extend our thanks to the COCO Consortium for creating and maintaining this invaluable 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-Seg dataset and how does it differ from the original COCO dataset?
+
+The [COCO-Seg](https://cocodataset.org/#home) dataset is an extension of the original COCO (Common Objects in Context) dataset, specifically designed for instance segmentation tasks. While it uses the same images as the COCO dataset, COCO-Seg includes more detailed segmentation annotations, making it a powerful resource for researchers and developers focusing on object instance segmentation.
+
+### How can I train a YOLOv8 model using the COCO-Seg dataset?
+
+To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a detailed list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! Example "Train Example"
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="coco-seg.yaml", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+    
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=coco-seg.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+### What are the key features of the COCO-Seg dataset?
+
+The COCO-Seg dataset includes several key features:
+
+- Retains the original 330K images from the COCO dataset.
+- Annotates the same 80 object categories found in the original COCO.
+- Provides more detailed instance segmentation masks for each object.
+- Uses standardized evaluation metrics such as mean Average Precision (mAP) for object detection and mean Average Recall (mAR) for instance segmentation tasks.
+
+### What pretrained models are available for COCO-Seg, and what are their performance metrics?
+
+The COCO-Seg dataset supports multiple pretrained YOLOv8 segmentation models with varying performance metrics. Here's a summary of the available models and their key metrics:
+
+| Model                                                                                        | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<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-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-seg.pt) | 640                   | 36.7                 | 30.5                  | 96.1                           | 1.21                                | 3.4                | 12.6              |
+| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-seg.pt) | 640                   | 44.6                 | 36.8                  | 155.7                          | 1.47                                | 11.8               | 42.6              |
+| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-seg.pt) | 640                   | 49.9                 | 40.8                  | 317.0                          | 2.18                                | 27.3               | 110.2             |
+| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-seg.pt) | 640                   | 52.3                 | 42.6                  | 572.4                          | 2.79                                | 46.0               | 220.5             |
+| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-seg.pt) | 640                   | 53.4                 | 43.4                  | 712.1                          | 4.02                                | 71.8               | 344.1             |
+
+### How is the COCO-Seg dataset structured and what subsets does it contain?
+
+The COCO-Seg dataset is partitioned into three subsets for specific training and evaluation needs:
+
+1. **Train2017**: Contains 118K images used primarily for training instance segmentation models.
+2. **Val2017**: Comprises 5K images utilized for validation during the training process.
+3. **Test2017**: Encompasses 20K images reserved for testing and benchmarking trained models. Note that ground truth annotations for this subset are not publicly available, and performance results are submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7383) for assessment.