From c82935096919119c7c0e8680d95d6ba44c1e3dbe Mon Sep 17 00:00:00 2001
From: mohamedsamirx <94049545+mohamedsamirx@users.noreply.github.com>
Date: Fri, 31 May 2024 07:57:49 +0300
Subject: [PATCH] Update README.md (#152)

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index b41a80d2..98eb236a 100644
--- a/README.md
+++ b/README.md
@@ -21,6 +21,7 @@ Over the past years, YOLOs have emerged as the predominant paradigm in the field
 </details>
 
 **UPDATES** 🔥
+- 2024/05/31: Thanks to [mohamedsamirx](https://github.com/mohamedsamirx) for the integration with [BoTSORT, DeepOCSORT, OCSORT, HybridSORT, ByteTrack, StrongSORT using BoxMOT library](https://colab.research.google.com/drive/1-QV2TNfqaMsh14w5VxieEyanugVBG14V?usp=sharing)!
 - 2024/05/31: Thanks to [kaylorchen](https://github.com/kaylorchen) for the integration with [rk3588](https://github.com/kaylorchen/rk3588-yolo-demo)!
 - 2024/05/31: Please use the [exported format](https://github.com/THU-MIG/yolov10?tab=readme-ov-file#export) for benchmark. In the non-exported format, e.g., pytorch, the speed of YOLOv10 is biased because the unnecessary `cv2` and `cv3` operations in the `v10Detect` are executed during inference.
 - 2024/05/30: We provide [some clarifications and suggestions](https://github.com/THU-MIG/yolov10/issues/136) for detecting smaller objects or objects in the distance with YOLOv10. Thanks to [SkalskiP](https://github.com/SkalskiP)!