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docs/en/models/index.md

@@ -27,7 +27,7 @@ Here are some of the key models supported:
 13. **[YOLO-NAS](yolo-nas.md)**: YOLO Neural Architecture Search (NAS) Models.
 14. **[Realtime Detection Transformers (RT-DETR)](rtdetr.md)**: Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models.
 15. **[YOLO-World](yolo-world.md)**: Real-time Open Vocabulary Object Detection models from Tencent AI Lab.
-15. **[LeYOLO](leyolo.md)**: A highly efficient model, featuring innovations such as efficient backbone scaling, Fast Pyramidal Architecture Network (FPAN), and Decoupled Network-in-Network (DNiN) detection head.
+16. **[LeYOLO](leyolo.md)**: A highly efficient model, featuring innovations such as efficient backbone scaling, Fast Pyramidal Architecture Network (FPAN), and Decoupled Network-in-Network (DNiN) detection head.
 
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   <br>

docs/en/models/leyolo.md

@@ -28,10 +28,10 @@ LeYOLO marks a significant step forward in the realm of efficient object detecti
 
 | Model        | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | params<br><sup>(M) | FLOPs (G) | Latency (ms) |
 | ------------ | --------------------- | -------------------- | ------------------ | --------- | ------------ |
-| LeYOLONano   | 640                   | 34.3                 |  1.1               | 2.6       | 2.9          |
-| LeYOLOSmall  | 640                   | 38.2                 |  1.9               | 4.5       | 3.8          |
-| LeYOLOMedium | 640                   | 39.3                 |  2.4               | 5.8       | 4.9          |
-| LeYOLOLarge  | 640                   | 39.2                 |  2.4               | 5.8       | 4.9          |
+| LeYOLONano   | 640                   | 34.3                 | 1.1                | 2.6       | 2.9          |
+| LeYOLOSmall  | 640                   | 38.2                 | 1.9                | 4.5       | 3.8          |
+| LeYOLOMedium | 640                   | 39.3                 | 2.4                | 5.8       | 4.9          |
+| LeYOLOLarge  | 640                   | 39.2                 | 2.4                | 5.8       | 4.9          |
 
 Latency measured on RTX 3060 GPU.
 
@@ -61,14 +61,14 @@ We would like to acknowledge the LeYOLO authors for their significant contributi
 
         ```bibtex
         @misc{hollard2024leyolonewscalableefficient,
-            title={LeYOLO, New Scalable and Efficient CNN Architecture for Object Detection}, 
+            title={LeYOLO, New Scalable and Efficient CNN Architecture for Object Detection},
             author={Lilian Hollard and Lucas Mohimont and Nathalie Gaveau and Luiz-Angelo Steffenel},
             year={2024},
             eprint={2406.14239},
             archivePrefix={arXiv},
             primaryClass={cs.CV},
-            url={https://arxiv.org/abs/2406.14239}, 
+            url={https://arxiv.org/abs/2406.14239},
         }
         ```
 
-The original LeYOLO paper can be found on [arXiv](https://arxiv.org/abs/2406.14239). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/LilianHollard/LeYOLO). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
+The original LeYOLO paper can be found on [arXiv](https://arxiv.org/abs/2406.14239). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/LilianHollard/LeYOLO). We appreciate their efforts in advancing the field and making their work accessible to the broader community.