update

README.md

@@ -4,8 +4,8 @@
 Official PyTorch implementation of **YOLOv10**.
 
 <p align="center">
-  <img src="figures/latency.png" width=50%>
-  <img src="figures/param.png" width=50%> <br>
+  <img src="figures/latency.png" width=40%>
+  <img src="figures/params.png" width=40%> <br>
   Comparisons with others in terms of latency-accuracy (left) and size-accuracy (right) trade-offs.
 </p>
 
@@ -26,13 +26,13 @@ Over the past years, YOLOs have emerged as the predominant paradigm in the field
 ## Performance
 COCO
 | Model | Test Size | #params | FLOPs | AP$^{val}$ | Latency |
-|:---------------|:----:|:---:|:--:|:--:|:--:|:--:|:--:|
-| YOLOv10-N |   640  |     2.3M    |   6.7G   |     38.5%     | 1.84ms |
-| YOLOv10-S |   640  |     7.2M    |   21.6G  |     46.3%     | 2.49ms |
-| YOLOv10-M |   640  |     15.4M   |   59.1G  |     51.1%     | 4.74ms |
-| YOLOv10-B |   640  |     19.1M   |  92.0G |     52.5%     | 5.74ms |
-| YOLOv10-L |   640  |     24.4M   |  120.3G   |     53.2%     | 7.28ms |
-| YOLOv10-X |   640  |     29.5M    |   160.4G   |     54.4%     | 10.70ms |
+|:---------------|:----:|:---:|:--:|:--:|:--:|
+| [YOLOv10-N]() |   640  |     2.3M    |   6.7G   |     38.5%     | 1.84ms |
+| [YOLOv10-S]() |   640  |     7.2M    |   21.6G  |     46.3%     | 2.49ms |
+| [YOLOv10-M]() |   640  |     15.4M   |   59.1G  |     51.1%     | 4.74ms |
+| [YOLOv10-B]() |   640  |     19.1M   |  92.0G |     52.5%     | 5.74ms |
+| [YOLOv10-L]() |   640  |     24.4M   |  120.3G   |     53.2%     | 7.28ms |
+| [YOLOv10-X]() |   640  |     29.5M    |   160.4G   |     54.4%     | 10.70ms |
 
 ## Installation
 `conda` virtual environment is recommended. 
@@ -43,6 +43,7 @@ pip install -e .
 ```
 
 ## Validation
+[`yolov10n.pt`]()  [`yolov10s.pt`]()  [`yolov10m.pt`]()  [`yolov10b.pt`]()  [`yolov10l.pt`]()  [`yolov10x.pt`]()  
 ```
 yolo val model=yolov10n/s/m/b/l/x.pt data=coco.yaml batch=256
 ```