diff --git a/README.md b/README.md
index cb29008492..0242ca3fb8 100644
--- a/README.md
+++ b/README.md
@@ -150,8 +150,8 @@ See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage e
 | [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640                   | 53.4                 | 42.9                  | 344.2 ± 3.2                    | 7.8 ± 0.2                           | 27.6               | 142.2             |
 | [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640                   | 54.7                 | 43.8                  | 664.5 ± 3.2                    | 15.8 ± 0.7                          | 62.1               | 319.0             |
 
-- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val segment data=coco-seg.yaml device=0`
-- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu`
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val segment data=coco.yaml device=0`
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco.yaml batch=1 device=0|cpu`
 
 </details>
 
diff --git a/README.zh-CN.md b/README.zh-CN.md
index aec15a2e1d..47cbaaaa99 100644
--- a/README.zh-CN.md
+++ b/README.zh-CN.md
@@ -150,8 +150,8 @@ YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://d
 | [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640                 | 53.4                 | 42.9                  | 344.2 ± 3.2                   | 7.8 ± 0.2                          | 27.6             | 142.2             |
 | [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640                 | 54.7                 | 43.8                  | 664.5 ± 3.2                   | 15.8 ± 0.7                         | 62.1             | 319.0             |
 
-- **mAP<sup>val</sup>** 值针对单模型单尺度在 [COCO val2017](https://cocodataset.org/) 数据集上进行。 <br>复制命令 `yolo val segment data=coco-seg.yaml device=0`
-- **速度**在使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例的 COCO 验证图像上平均。 <br>复制命令 `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu`
+- **mAP<sup>val</sup>** 值针对单模型单尺度在 [COCO val2017](https://cocodataset.org/) 数据集上进行。 <br>复制命令 `yolo val segment data=coco.yaml device=0`
+- **速度**在使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例的 COCO 验证图像上平均。 <br>复制命令 `yolo val segment data=coco.yaml batch=1 device=0|cpu`
 
 </details>
 
diff --git a/docs/en/datasets/segment/coco.md b/docs/en/datasets/segment/coco.md
index 5ff52f46a2..2dd8a0f53a 100644
--- a/docs/en/datasets/segment/coco.md
+++ b/docs/en/datasets/segment/coco.md
@@ -56,14 +56,14 @@ To train a YOLO11n-seg model on the COCO-Seg dataset for 100 [epochs](https://ww
         model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data="coco-seg.yaml", epochs=100, imgsz=640)
+        results = model.train(data="coco.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo segment train data=coco-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=coco.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
         ```
 
 ## Sample Images and Annotations
@@ -118,14 +118,14 @@ To train a YOLO11n-seg model on the COCO-Seg dataset for 100 epochs with an imag
         model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data="coco-seg.yaml", epochs=100, imgsz=640)
+        results = model.train(data="coco.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo segment train data=coco-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=coco.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
         ```
 
 ### What are the key features of the COCO-Seg dataset?
diff --git a/docs/en/tasks/segment.md b/docs/en/tasks/segment.md
index c422c6fd62..33c19d9d3c 100644
--- a/docs/en/tasks/segment.md
+++ b/docs/en/tasks/segment.md
@@ -36,8 +36,8 @@ YOLO11 pretrained Segment models are shown here. Detect, Segment and Pose models
 
 {% include "macros/yolo-seg-perf.md" %}
 
-- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val segment data=coco-seg.yaml device=0`
-- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu`
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val segment data=coco.yaml device=0`
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco.yaml batch=1 device=0|cpu`
 
 ## Train