correction YOLO26 Depth single-scale validation metrics

README.md

@@ -202,7 +202,7 @@ See the [Depth Estimation Docs](https://docs.ultralytics.com/tasks/depth) for us
 | [YOLO26x-depth](https://platform.ultralytics.com/ultralytics/yolo26/yolo26x-depth) | 768                         | 0.933                | 0.080                 | 0.344              | 1240.9 ± 73.3                        | 13.6 ± 0.2                                | 57.0                     | 301.7                   |
 
 - **delta1<sup>NYU</sup>** is the percentage of pixels where the predicted depth is within a factor of 1.25 of the ground truth, on the NYU Depth V2 Eigen test split (654 images) with multi-scale + horizontal-flip TTA and log-least-squares alignment.
-- Single-scale accuracy without TTA is reproducible with `yolo depth val model=yolo26n-depth.pt data=nyu-depth.yaml imgsz=768 device=0` (substitute `model=` for each size), which uses median (scale-only) alignment and scores lower: delta1 0.785 (n), 0.786 (s), 0.827 (m), 0.839 (l), 0.843 (x).
+- Single-scale accuracy without TTA is reproducible with `yolo depth val model=yolo26n-depth.pt data=nyu-depth.yaml imgsz=768 device=0` (substitute `model=` for each size), which uses median (scale-only) alignment and scores lower: delta1 0.783 (n), 0.793 (s), 0.840 (m), 0.853 (l), 0.860 (x).
 - **abs_rel** is the mean absolute relative error between predicted and ground-truth depth values.
 - **rmse** is the root mean squared error in meters.
 - **Speed** is inference-only latency (pre/post-processing excluded) at `imgsz=768`, `batch=1`, reported as mean ± std over timed runs after warmup. **CPU ONNX** is ONNX Runtime fp32 on a 32-core Intel Xeon (Skylake); **T4 TensorRT10** is TensorRT fp16 on a Tesla T4.