Update README.md

README.md

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   ### VehicleNet-Y26 — Model Access Agreement
 
-  **VehicleNet-RFDETR-n8** is a multi-vehicle detection model released under the
+  **VehicleNet-RFDETR-n** is a multi-vehicle detection model released under the
   Apache License, Version 2.0. Access to this model is granted exclusively to
   individuals who meet the legal age requirements of their jurisdiction and possess
   the authority to accept and comply with the terms set forth herein.
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   3. You will use this model strictly in accordance with the Apache License 2.0 and all
      applicable local, national, and international laws and regulations.
 
-  **Disclaimer of Warranties:** VehicleNet-RFDETR-n8 is provided "as-is," without
+  **Disclaimer of Warranties:** VehicleNet-RFDETR-n is provided "as-is," without
   warranties of any kind, whether express or implied, including but not limited to
   warranties of accuracy, reliability, fitness for a particular purpose, or suitability
   for deployment in safety-critical or regulated environments. The authors and affiliated
@@ -69,7 +69,7 @@ tags:
 - roboflow
 ---
 
-# VehicleNet-RFDETR-n8
+# VehicleNet-RFDETR-n
 
 <a href="https://www.apache.org/licenses/LICENSE-2.0">
   <img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="Apache 2.0 License">
@@ -83,7 +83,7 @@ tags:
 
 ## Overview
 
-**VehicleNet-RFDETR-n8** is a multi-class vehicle detection model designed for fine-grained vehicle type recognition in real-world traffic scenes. It is fine-tuned on the **UVH-26-MV Dataset**, curated and released by the **Indian Institute of Science (IISc), Bangalore**, which captures the highly complex, dense, and heterogeneous nature of Indian road traffic.
+**VehicleNet-RFDETR-n** is a multi-class vehicle detection model designed for fine-grained vehicle type recognition in real-world traffic scenes. It is fine-tuned on the **UVH-26-MV Dataset**, curated and released by the **Indian Institute of Science (IISc), Bangalore**, which captures the highly complex, dense, and heterogeneous nature of Indian road traffic.
 
 The model recognizes **14 vehicle categories**, including hatchbacks, sedans, SUVs, MUVs, two-wheelers, three-wheelers, buses, trucks, and a range of commercial vehicle types. This **nano variant** is optimized for low-latency inference, balancing speed and accuracy for deployment on resource-constrained hardware.