update model links and project index

projects/README.md

@@ -30,20 +30,22 @@ We also provide some documentation listed below to help you get started:
 
 ## Project List
 
-- [MMPose4AIGC](./mmpose4aigc)
+- **[:zap:RTMPose](./rtmpose)**: Real-Time Multi-Person Pose Estimation toolkit based on MMPose
 
-  This project will demonstrate how to use MMPose to generate skeleton images for pose guided AI image generation.
+  <div align="center">
+  <img src="https://user-images.githubusercontent.com/15977946/225229448-36ff568d-a723-4248-bb19-2df4044ff8e8.png" width=800 height=200 />
+  </div><br/>
 
-  <div align=center>
-  <img src="https://user-images.githubusercontent.com/13503330/222403836-c65ba905-4bdd-4a44-834c-ff8d5959649d.png" width=1000 height=200/>
-  </div>
+- **[:art:MMPose4AIGC](./mmpose4aigc)**: Guide AI image generation with MMPose
 
-- [RTMPose](./rtmpose)
+  <div align=center>
+  <img src="https://user-images.githubusercontent.com/13503330/222403836-c65ba905-4bdd-4a44-834c-ff8d5959649d.png" width="800"/>
+  </div><br/>
 
-  Real-Time Multi-Person Pose Estimation toolkit based on MMPose
+- **[:bulb:YOLOX-Pose](./yolox-pose)**: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss
 
-  <div align="center">
-  <img width=1000 height=200 src="https://user-images.githubusercontent.com/15977946/225229448-36ff568d-a723-4248-bb19-2df4044ff8e8.png"/>
-  </div>
+  <div align=center>
+  <img src="https://user-images.githubusercontent.com/26127467/226550744-3dd948f4-cc5a-4a2f-a737-c595fc6dfe4d.jpg" width="800" style="width: 800px; height: 200px; object-fit: cover"/>
+  </div><br/>
 
 - **And we can't wait to see what you contribute next!**

projects/yolox-pose/README.md

@@ -39,7 +39,7 @@ Here's an example code:
 ```shell
 python demo/inferencer_demo.py ../../tests/data/coco/000000000785.jpg \
     --pose2d configs/yolox-pose_s_8xb32-300e_coco.py \
-    --pose2d-weights https://download.openmmlab.com/mmpose/v1/yolox-pose/yolox-pose_s_8xb32-300e_coco-9f5e3924_20230321.pth \
+    --pose2d-weights https://download.openmmlab.com/mmpose/v1/projects/yolox-pose/yolox-pose_s_8xb32-300e_coco-9f5e3924_20230321.pth \
     --scope mmyolo --vis-out-dir vis_results
 ```
 
@@ -91,10 +91,10 @@ Results on COCO val2017
 
 |                              Model                              | Input Size |  AP   | AP<sup>50</sup> | AP<sup>75</sup> |  AR   | AR<sup>50</sup> |                                 Download                                 |
 | :-------------------------------------------------------------: | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :----------------------------------------------------------------------: |
-| [YOLOX-tiny-Pose](./configs/yolox-pose_tiny_4xb64-300e_coco.py) |    640     | 0.477 |      0.756      |      0.506      | 0.547 |      0.802      | [model](https://download.openmmlab.com/mmpose/v1/yolox-pose/yolox-pose_tiny_4xb64-300e_coco-c47dd83b_20230321.pth) \| [log](https://download.openmmlab.com/mmpose/v1/yolox-pose/yolox-pose_tiny_4xb64-300e_coco_20230321.json) |
-|    [YOLOX-s-Pose](./configs/yolox-pose_s_8xb32-300e_coco.py)    |    640     | 0.595 |      0.836      |      0.653      | 0.658 |      0.878      | [model](https://download.openmmlab.com/mmpose/v1/yolox-pose/yolox-pose_s_8xb32-300e_coco-9f5e3924_20230321.pth) \| [log](https://download.openmmlab.com/mmpose/v1/yolox-pose/yolox-pose_s_8xb32-300e_coco_20230321.json) |
-|    [YOLOX-m-Pose](./configs/yolox-pose_m_4xb64-300e_coco.py)    |    640     | 0.659 |      0.870      |      0.729      | 0.713 |      0.903      | [model](https://download.openmmlab.com/mmpose/v1/yolox-pose/yolox-pose_m_4xb64-300e_coco-cbd11d30_20230321.pth) \| [log](https://download.openmmlab.com/mmpose/v1/yolox-pose/yolox-pose_m_4xb64-300e_coco_20230321.json) |
-|    [YOLOX-l-Pose](./configs/yolox-pose_l_4xb64-300e_coco.py)    |    640     | 0.679 |      0.882      |      0.749      | 0.733 |      0.911      | [model](https://download.openmmlab.com/mmpose/v1/yolox-pose/yolox-pose_l_4xb64-300e_coco-122e4cf8_20230321.pth) \| [log](https://download.openmmlab.com/mmpose/v1/yolox-pose/yolox-pose_l_4xb64-300e_coco_20230321.json) |
+| [YOLOX-tiny-Pose](./configs/yolox-pose_tiny_4xb64-300e_coco.py) |    640     | 0.477 |      0.756      |      0.506      | 0.547 |      0.802      | [model](https://download.openmmlab.com/mmpose/v1/projects/yolox-pose/yolox-pose_tiny_4xb64-300e_coco-c47dd83b_20230321.pth) \| [log](https://download.openmmlab.com/mmpose/v1/projects/yolox-pose/yolox-pose_tiny_4xb64-300e_coco_20230321.json) |
+|    [YOLOX-s-Pose](./configs/yolox-pose_s_8xb32-300e_coco.py)    |    640     | 0.595 |      0.836      |      0.653      | 0.658 |      0.878      | [model](https://download.openmmlab.com/mmpose/v1/projects/yolox-pose/yolox-pose_s_8xb32-300e_coco-9f5e3924_20230321.pth) \| [log](https://download.openmmlab.com/mmpose/v1/projects/yolox-pose/yolox-pose_s_8xb32-300e_coco_20230321.json) |
+|    [YOLOX-m-Pose](./configs/yolox-pose_m_4xb64-300e_coco.py)    |    640     | 0.659 |      0.870      |      0.729      | 0.713 |      0.903      | [model](https://download.openmmlab.com/mmpose/v1/projects/yolox-pose/yolox-pose_m_4xb64-300e_coco-cbd11d30_20230321.pth) \| [log](https://download.openmmlab.com/mmpose/v1/projects/yolox-pose/yolox-pose_m_4xb64-300e_coco_20230321.json) |
+|    [YOLOX-l-Pose](./configs/yolox-pose_l_4xb64-300e_coco.py)    |    640     | 0.679 |      0.882      |      0.749      | 0.733 |      0.911      | [model](https://download.openmmlab.com/mmpose/v1/projects/yolox-pose/yolox-pose_l_4xb64-300e_coco-122e4cf8_20230321.pth) \| [log](https://download.openmmlab.com/mmpose/v1/projects/yolox-pose/yolox-pose_l_4xb64-300e_coco_20230321.json) |
 
 We have only trained models with an input size of 640, as we couldn't replicate the performance enhancement mentioned in the paper when increasing the input size from 640 to 960. We warmly welcome any contributions if you can successfully reproduce the results from the paper!