[Doc] Update demos

configs/body_2d_keypoint/rtmpose/body8/rtmpose_body8-coco.yml

@@ -41,6 +41,10 @@ Models:
   Weights: https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-body7_pt-body7_420e-256x192-acd4a1ef_20230504.pth
 - Config: configs/body_2d_keypoint/rtmpose/body8/rtmpose-m_8xb256-420e_body8-256x192.py
   In Collection: RTMPose
+  Alias:
+    - human
+    - body
+    - body17
   Metadata:
     Architecture: *id001
     Training Data: *id002

configs/body_2d_keypoint/rtmpose/body8/rtmpose_body8-halpe26.yml

@@ -41,6 +41,7 @@ Models:
   Weights: https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-body7_pt-body7-halpe26_700e-256x192-7f134165_20230605.pth
 - Config: configs/body_2d_keypoint/rtmpose/body8/rtmpose-m_8xb512-700e_body8-halpe26-256x192.py
   In Collection: RTMPose
+  Alias:  body26
   Metadata:
     Architecture: *id001
     Training Data: *id002

configs/body_2d_keypoint/rtmpose/coco/rtmpose_coco.yml

@@ -106,7 +106,6 @@ Models:
   Weights: https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth
 - Config: configs/body_2d_keypoint/rtmpose/coco/rtmpose-m_8xb256-420e_aic-coco-256x192.py
   In Collection: RTMPose
-  Alias: human
   Metadata:
     Architecture: *id001
     Training Data: *id002

configs/body_3d_keypoint/pose_lift/h36m/motionbert_h36m.yml

@@ -5,7 +5,7 @@ Collections:
     URL: https://arxiv.org/abs/2210.06551
   README: https://github.com/open-mmlab/mmpose/blob/main/docs/en/papers/algorithms/motionbert.md
 Models:
-- Config: configs/body_3d_keypoint/pose_lift/h36m/pose-lift_motionbert_8xb32-240e_h36m.py
+- Config: configs/body_3d_keypoint/pose_lift/h36m/pose-lift_motionbert-243frm_8xb32-240e_h36m.py
   In Collection: MotionBERT
   Metadata:
     Architecture: &id001
@@ -24,8 +24,9 @@ Models:
       P-MPJPE: 33.4
     Task: Body 3D Keypoint
   Weights: https://download.openmmlab.com/mmpose/v1/body_3d_keypoint/pose_lift/h36m/motionbert_h36m-f554954f_20230531.pth
-- Config: configs/body_3d_keypoint/pose_lift/h36m/pose-lift_motionbert-ft_8xb32-120e_h36m.py
+- Config: configs/body_3d_keypoint/pose_lift/h36m/pose-lift_motionbert-ft-243frm_8xb32-120e_h36m.py
   In Collection: MotionBERT
+  Alias: human3d
   Metadata:
     Architecture: *id001
     Training Data: Human3.6M (MotionBERT)

configs/body_3d_keypoint/pose_lift/h36m/videopose3d_h36m.yml

@@ -61,7 +61,6 @@ Models:
   Weights: https://download.openmmlab.com/mmpose/body3d/videopose/videopose_h36m_1frame_fullconv_supervised_cpn_ft-5c3afaed_20210527.pth
 - Config: configs/body_3d_keypoint/pose_lift/h36m/pose-lift_videopose3d-243frm-supv-cpn-ft_8xb128-200e_h36m.py
   In Collection: VideoPose3D
-  Alias: human3d
   Metadata:
     Architecture: *id001
     Training Data: Human3.6M

configs/face_2d_keypoint/rtmpose/face6/rtmpose_face6.md

@@ -66,6 +66,6 @@
 
 |                                    Config                                    | Input Size | NME<sup><br>(LaPa) | FLOPS<sup><br>(G) |                                    Download                                     |
 | :--------------------------------------------------------------------------: | :--------: | :----------------: | :---------------: | :-----------------------------------------------------------------------------: |
-| [RTMPose-t\*](./rtmpose/face_2d_keypoint/rtmpose-t_8xb256-120e_lapa-256x256.py) |  256x256   |        1.67        |       0.652       | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-t_simcc-face6_pt-in1k_120e-256x256-df79d9a5_20230529.pth) |
-| [RTMPose-s\*](./rtmpose/face_2d_keypoint/rtmpose-m_8xb256-120e_lapa-256x256.py) |  256x256   |        1.59        |       1.119       | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-face6_pt-in1k_120e-256x256-d779fdef_20230529.pth) |
-| [RTMPose-m\*](./rtmpose/face_2d_keypoint/rtmpose-m_8xb256-120e_lapa-256x256.py) |  256x256   |        1.44        |       2.852       | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-face6_pt-in1k_120e-256x256-72a37400_20230529.pth) |
+| [RTMPose-t\*](./rtmpose/face_2d_keypoint/rtmpose-t_8xb256-120e_face6-256x256.py) |  256x256   |        1.67        |       0.652       | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-t_simcc-face6_pt-in1k_120e-256x256-df79d9a5_20230529.pth) |
+| [RTMPose-s\*](./rtmpose/face_2d_keypoint/rtmpose-s_8xb256-120e_face6-256x256.py) |  256x256   |        1.59        |       1.119       | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-face6_pt-in1k_120e-256x256-d779fdef_20230529.pth) |
+| [RTMPose-m\*](./rtmpose/face_2d_keypoint/rtmpose-m_8xb256-120e_face6-256x256.py) |  256x256   |        1.44        |       2.852       | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-face6_pt-in1k_120e-256x256-72a37400_20230529.pth) |

configs/face_2d_keypoint/rtmpose/face6/rtmpose_face6.yml

@@ -42,6 +42,7 @@ Models:
     Architecture: *id001
     Training Data: *id002
   Name: rtmpose-m_8xb256-120e_face6-256x256
+  Alias: face
   Results:
   - Dataset: Face6
     Metrics:

configs/face_2d_keypoint/rtmpose/wflw/rtmpose_wflw.yml

@@ -1,7 +1,6 @@
 Models:
 - Config: configs/face_2d_keypoint/rtmpose/wflw/rtmpose-m_8xb64-60e_wflw-256x256.py
   In Collection: RTMPose
-  Alias: face
   Metadata:
     Architecture:
     - RTMPose

configs/hand_2d_keypoint/rtmpose/coco_wholebody_hand/rtmpose_coco_wholebody_hand.yml

@@ -1,7 +1,6 @@
 Models:
 - Config: configs/hand_2d_keypoint/rtmpose/coco_wholebody_hand/rtmpose-m_8xb32-210e_coco-wholebody-hand-256x256.py
   In Collection: RTMPose
-  Alias: hand
   Metadata:
     Architecture:
     - RTMPose

configs/hand_2d_keypoint/rtmpose/hand5/rtmpose_hand5.yml

@@ -7,6 +7,7 @@ Collections:
 Models:
 - Config: configs/hand_2d_keypoint/rtmpose/hand5/rtmpose-m_8xb256-210e_hand5-256x256.py
   In Collection: RTMPose
+  Alias: hand
   Metadata:
     Architecture: &id001
     - RTMPose

demo/body3d_pose_lifter_demo.py

@@ -206,6 +206,7 @@ def process_one_image(args, detector, frame, frame_idx, pose_estimator,
         next_id (int): The next track id to be used.
     """
     pose_lift_dataset = pose_lifter.cfg.test_dataloader.dataset
+    pose_lift_dataset_name = pose_lifter.dataset_meta['dataset_name']
 
     # First stage: conduct 2D pose detection in a Topdown manner
     # use detector to obtain person bounding boxes
@@ -226,7 +227,7 @@ def process_one_image(args, detector, frame, frame_idx, pose_estimator,
     else:
         _track = _track_by_iou
 
-    pose_det_dataset = pose_estimator.cfg.test_dataloader.dataset
+    pose_det_dataset_name = pose_estimator.dataset_meta['dataset_name']
     pose_est_results_converted = []
 
     # convert 2d pose estimation results into the format for pose-lifting
@@ -278,8 +279,8 @@ def process_one_image(args, detector, frame, frame_idx, pose_estimator,
         pose_est_result_converted.set_field(
             pose_est_results[i].gt_instances.clone(), 'gt_instances')
         keypoints = convert_keypoint_definition(keypoints,
-                                                pose_det_dataset['type'],
-                                                pose_lift_dataset['type'])
+                                                pose_det_dataset_name,
+                                                pose_lift_dataset_name)
         pose_est_result_converted.pred_instances.set_field(
             keypoints, 'keypoints')
         pose_est_result_converted.set_field(pose_est_results[i].track_id,
@@ -348,8 +349,8 @@ def process_one_image(args, detector, frame, frame_idx, pose_estimator,
             data_sample=pred_3d_data_samples,
             det_data_sample=det_data_sample,
             draw_gt=False,
-            dataset_2d=pose_det_dataset['type'],
-            dataset_3d=pose_lift_dataset['type'],
+            dataset_2d=pose_det_dataset_name,
+            dataset_3d=pose_lift_dataset_name,
             show=args.show,
             draw_bbox=True,
             kpt_thr=args.kpt_thr,

demo/docs/en/2d_animal_demo.md

@@ -20,8 +20,8 @@ Take [animalpose model](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet
 
 ```shell
 python demo/topdown_demo_with_mmdet.py \
-    demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \
-    https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
+    demo/mmdetection_cfg/rtmdet_m_8xb32-300e_coco.py \
+    https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_m_8xb32-300e_coco/rtmdet_m_8xb32-300e_coco_20220719_112220-229f527c.pth \
     configs/animal_2d_keypoint/topdown_heatmap/animalpose/td-hm_hrnet-w32_8xb64-210e_animalpose-256x256.py \
     https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth \
     --input tests/data/animalpose/ca110.jpeg \
@@ -39,14 +39,14 @@ The augement `--det-cat-id=15` selected detected bounding boxes with label 'cat'
 **COCO-animals**
 In COCO dataset, there are 80 object categories, including 10 common `animal` categories (14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe').
 
-For other animals, we have also provided some pre-trained animal detection models (1-class models). Supported models can be found in [detection model zoo](/demo/docs/en/mmdet_modelzoo.md).
+For other animals, we have also provided some pre-trained animal detection models. Supported models can be found in [detection model zoo](/demo/docs/en/mmdet_modelzoo.md).
 
 To save visualized results on disk:
 
 ```shell
 python demo/topdown_demo_with_mmdet.py \
-    demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \
-    https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
+    demo/mmdetection_cfg/rtmdet_m_8xb32-300e_coco.py \
+    https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_m_8xb32-300e_coco/rtmdet_m_8xb32-300e_coco_20220719_112220-229f527c.pth \
     configs/animal_2d_keypoint/topdown_heatmap/animalpose/td-hm_hrnet-w32_8xb64-210e_animalpose-256x256.py \
     https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth \
     --input tests/data/animalpose/ca110.jpeg \
@@ -57,8 +57,8 @@ To save predicted results on disk:
 
 ```shell
 python demo/topdown_demo_with_mmdet.py \
-    demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \
-    https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
+    demo/mmdetection_cfg/rtmdet_m_8xb32-300e_coco.py \
+    https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_m_8xb32-300e_coco/rtmdet_m_8xb32-300e_coco_20220719_112220-229f527c.pth \
     configs/animal_2d_keypoint/topdown_heatmap/animalpose/td-hm_hrnet-w32_8xb64-210e_animalpose-256x256.py \
     https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth \
     --input tests/data/animalpose/ca110.jpeg \
@@ -69,8 +69,8 @@ To run demos on CPU:
 
 ```shell
 python demo/topdown_demo_with_mmdet.py \
-    demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \
-    https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
+    demo/mmdetection_cfg/rtmdet_tiny_8xb32-300e_coco.py \
+    https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_tiny_8xb32-300e_coco/rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth \
     configs/animal_2d_keypoint/topdown_heatmap/animalpose/td-hm_hrnet-w32_8xb64-210e_animalpose-256x256.py \
     https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth \
     --input tests/data/animalpose/ca110.jpeg \
@@ -85,8 +85,8 @@ For example,
 
 ```shell
 python demo/topdown_demo_with_mmdet.py \
-    demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \
-    https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
+    demo/mmdetection_cfg/rtmdet_m_8xb32-300e_coco.py \
+    https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_m_8xb32-300e_coco/rtmdet_m_8xb32-300e_coco_20220719_112220-229f527c.pth \
     configs/animal_2d_keypoint/topdown_heatmap/animalpose/td-hm_hrnet-w32_8xb64-210e_animalpose-256x256.py \
     https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth \
     --input demo/resources/<demo_dog.mp4> \

demo/docs/en/2d_face_demo.md

@@ -23,15 +23,15 @@ Take [aflw model](https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18
 python demo/topdown_demo_with_mmdet.py \
     demo/mmdetection_cfg/yolox-s_8xb8-300e_coco-face.py \
     https://download.openmmlab.com/mmpose/mmdet_pretrained/yolo-x_8xb8-300e_coco-face_13274d7c.pth \
-    configs/face_2d_keypoint/topdown_heatmap/aflw/td-hm_hrnetv2-w18_8xb64-60e_aflw-256x256.py \
-    https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth \
+    configs/face_2d_keypoint/rtmpose/face6/rtmpose-m_8xb256-120e_face6-256x256.py \
+    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-face6_pt-in1k_120e-256x256-72a37400_20230529.pth \
     --input tests/data/cofw/001766.jpg \
     --show --draw-heatmap
 ```
 
 Visualization result:
 
-<img src="https://user-images.githubusercontent.com/26127467/220538388-582ce90d-751a-40dd-ac06-3bc078b773a0.jpg" height="500px" alt><br>
+<img src="https://github.com/open-mmlab/mmpose/assets/26127467/d5f4a947-b6a7-465b-b54d-0ffa2f6d353a" height="500px" alt><br>
 
 If you use a heatmap-based model and set argument `--draw-heatmap`, the predicted heatmap will be visualized together with the keypoints.
 
@@ -41,8 +41,8 @@ To save visualized results on disk:
 python demo/topdown_demo_with_mmdet.py \
     demo/mmdetection_cfg/yolox-s_8xb8-300e_coco-face.py \
     https://download.openmmlab.com/mmpose/mmdet_pretrained/yolo-x_8xb8-300e_coco-face_13274d7c.pth \
-    configs/face_2d_keypoint/topdown_heatmap/aflw/td-hm_hrnetv2-w18_8xb64-60e_aflw-256x256.py \
-    https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth \
+    configs/face_2d_keypoint/rtmpose/face6/rtmpose-m_8xb256-120e_face6-256x256.py \
+    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-face6_pt-in1k_120e-256x256-72a37400_20230529.pth \
     --input tests/data/cofw/001766.jpg \
     --draw-heatmap --output-root vis_results
 ```
@@ -55,8 +55,8 @@ To run demos on CPU:
 python demo/topdown_demo_with_mmdet.py \
     demo/mmdetection_cfg/yolox-s_8xb8-300e_coco-face.py \
     https://download.openmmlab.com/mmpose/mmdet_pretrained/yolo-x_8xb8-300e_coco-face_13274d7c.pth \
-    configs/face_2d_keypoint/topdown_heatmap/aflw/td-hm_hrnetv2-w18_8xb64-60e_aflw-256x256.py \
-    https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth \
+    configs/face_2d_keypoint/rtmpose/face6/rtmpose-m_8xb256-120e_face6-256x256.py \
+    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-face6_pt-in1k_120e-256x256-72a37400_20230529.pth \
     --input tests/data/cofw/001766.jpg \
     --show --draw-heatmap --device=cpu
 ```
@@ -69,13 +69,13 @@ Videos share the same interface with images. The difference is that the `${INPUT
 python demo/topdown_demo_with_mmdet.py \
     demo/mmdetection_cfg/yolox-s_8xb8-300e_coco-face.py \
     https://download.openmmlab.com/mmpose/mmdet_pretrained/yolo-x_8xb8-300e_coco-face_13274d7c.pth \
-    configs/face_2d_keypoint/topdown_heatmap/aflw/td-hm_hrnetv2-w18_8xb64-60e_aflw-256x256.py \
-    https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth \
+    configs/face_2d_keypoint/rtmpose/face6/rtmpose-m_8xb256-120e_face6-256x256.py \
+    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-face6_pt-in1k_120e-256x256-72a37400_20230529.pth \
     --input demo/resources/<demo_face.mp4> \
-    --show --draw-heatmap --output-root vis_results
+    --show --output-root vis_results --radius 1
 ```
 
-<img src="https://user-images.githubusercontent.com/26127467/220541430-6ade5a59-3d06-466a-a94d-00c82ff96a00.gif" height="500px" alt><br>
+<img src="https://github.com/open-mmlab/mmpose/assets/26127467/5883f014-d0be-4796-a30e-f1b5dcb6e85d" height="300px" alt><br>
 
 The original video can be downloaded from [Google Drive](https://drive.google.com/file/d/1kQt80t6w802b_vgVcmiV_QfcSJ3RWzmb/view?usp=sharing).