Update README.md

README.md

@@ -58,7 +58,7 @@
 * **State-of-the-Art Performance**
 
   * Demonstrates significant mAP gains of YOLOv13-S over YOLOv12-S and earlier versions on the MS COCO benchmark.
-  * Maintains a lightweight model size, ideal for mobile and embedded deployment. Specifically, The FLOPs of Nano and Small are the lowest among the YOLO series.
+  * Maintains a lightweight model size, ideal for mobile and embedded deployment. Specifically, The FLOPs of Nano and Small models are the lowest among the YOLO series.
 
 > YOLOv13 seamlessly combines hypergraph computation with end-to-end information collaboration to deliver a more accurate, robust, and efficient real-time detection solution.