Support yolox pose refer from mmpose

.dev_scripts/gather_models.py

@@ -108,6 +108,7 @@ def get_dataset_name(config):
     name_map = dict(
         CityscapesDataset='Cityscapes',
         CocoDataset='COCO',
+        PoseCocoDataset='COCO Person',
         YOLOv5CocoDataset='COCO',
         CocoPanopticDataset='COCO',
         YOLOv5DOTADataset='DOTA 1.0',

configs/_base_/pose/coco.py

@@ -0,0 +1,181 @@
+dataset_info = dict(
+    dataset_name='coco',
+    paper_info=dict(
+        author='Lin, Tsung-Yi and Maire, Michael and '
+        'Belongie, Serge and Hays, James and '
+        'Perona, Pietro and Ramanan, Deva and '
+        r'Doll{\'a}r, Piotr and Zitnick, C Lawrence',
+        title='Microsoft coco: Common objects in context',
+        container='European conference on computer vision',
+        year='2014',
+        homepage='http://cocodataset.org/',
+    ),
+    keypoint_info={
+        0:
+        dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''),
+        1:
+        dict(
+            name='left_eye',
+            id=1,
+            color=[51, 153, 255],
+            type='upper',
+            swap='right_eye'),
+        2:
+        dict(
+            name='right_eye',
+            id=2,
+            color=[51, 153, 255],
+            type='upper',
+            swap='left_eye'),
+        3:
+        dict(
+            name='left_ear',
+            id=3,
+            color=[51, 153, 255],
+            type='upper',
+            swap='right_ear'),
+        4:
+        dict(
+            name='right_ear',
+            id=4,
+            color=[51, 153, 255],
+            type='upper',
+            swap='left_ear'),
+        5:
+        dict(
+            name='left_shoulder',
+            id=5,
+            color=[0, 255, 0],
+            type='upper',
+            swap='right_shoulder'),
+        6:
+        dict(
+            name='right_shoulder',
+            id=6,
+            color=[255, 128, 0],
+            type='upper',
+            swap='left_shoulder'),
+        7:
+        dict(
+            name='left_elbow',
+            id=7,
+            color=[0, 255, 0],
+            type='upper',
+            swap='right_elbow'),
+        8:
+        dict(
+            name='right_elbow',
+            id=8,
+            color=[255, 128, 0],
+            type='upper',
+            swap='left_elbow'),
+        9:
+        dict(
+            name='left_wrist',
+            id=9,
+            color=[0, 255, 0],
+            type='upper',
+            swap='right_wrist'),
+        10:
+        dict(
+            name='right_wrist',
+            id=10,
+            color=[255, 128, 0],
+            type='upper',
+            swap='left_wrist'),
+        11:
+        dict(
+            name='left_hip',
+            id=11,
+            color=[0, 255, 0],
+            type='lower',
+            swap='right_hip'),
+        12:
+        dict(
+            name='right_hip',
+            id=12,
+            color=[255, 128, 0],
+            type='lower',
+            swap='left_hip'),
+        13:
+        dict(
+            name='left_knee',
+            id=13,
+            color=[0, 255, 0],
+            type='lower',
+            swap='right_knee'),
+        14:
+        dict(
+            name='right_knee',
+            id=14,
+            color=[255, 128, 0],
+            type='lower',
+            swap='left_knee'),
+        15:
+        dict(
+            name='left_ankle',
+            id=15,
+            color=[0, 255, 0],
+            type='lower',
+            swap='right_ankle'),
+        16:
+        dict(
+            name='right_ankle',
+            id=16,
+            color=[255, 128, 0],
+            type='lower',
+            swap='left_ankle')
+    },
+    skeleton_info={
+        0:
+        dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),
+        1:
+        dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),
+        2:
+        dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]),
+        3:
+        dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]),
+        4:
+        dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]),
+        5:
+        dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]),
+        6:
+        dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]),
+        7:
+        dict(
+            link=('left_shoulder', 'right_shoulder'),
+            id=7,
+            color=[51, 153, 255]),
+        8:
+        dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]),
+        9:
+        dict(
+            link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]),
+        10:
+        dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]),
+        11:
+        dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
+        12:
+        dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]),
+        13:
+        dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),
+        14:
+        dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),
+        15:
+        dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]),
+        16:
+        dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]),
+        17:
+        dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]),
+        18:
+        dict(
+            link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255])
+    },
+    joint_weights=[
+        1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5,
+        1.5
+    ],
+    sigmas=[
+        0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062,
+        0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089
+    ])

configs/yolox/README.md

@@ -45,6 +45,32 @@ The modified training parameters are as follows：
 1. The test score threshold is 0.001.
 2. Due to the need for pre-training weights, we cannot reproduce the performance of the `yolox-nano` model. Please refer to https://github.com/Megvii-BaseDetection/YOLOX/issues/674 for more information.
 
+## YOLOX-Pose
+
+Based on [MMPose](https://github.com/open-mmlab/mmpose/blob/main/projects/yolox-pose/README.md), we have implemented a YOLOX-based human pose estimator, utilizing the approach outlined in **YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss (CVPRW 2022)**. This pose estimator is lightweight and quick, making it well-suited for crowded scenes.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/26127467/226655503-3cee746e-6e42-40be-82ae-6e7cae2a4c7e.jpg"/>
+</div>
+
+### Results
+
+|  Backbone  | Size | Batch Size | AMP | RTMDet-Hyp | Mem (GB) |  AP  |                             Config                             |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Download                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+|:----------:|:----:|:----------:|:---:|:----------:|:--------:|:----:|:--------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| YOLOX-tiny | 416  |   8xb32    | Yes |    Yes     |   5.3    | 52.8 | [config](./pose/yolox-pose_tiny_8xb32-300e-rtmdet-hyp_coco.py) |                                                                                                                                                                                                                                                                                                                                                                                                                                                                      [model](https://download.openmmlab.com/mmyolo/v0/yolox/pose/yolox-pose_tiny_8xb32-300e-rtmdet-hyp_coco/yolox-pose_tiny_8xb32-300e-rtmdet-hyp_coco_20230427_080351-2117af67.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/pose/yolox-pose_tiny_8xb32-300e-rtmdet-hyp_coco/yolox-pose_tiny_8xb32-300e-rtmdet-hyp_coco_20230427_080351.log.json)                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+|  YOLOX-s   | 640  |   8xb32    | Yes |    Yes     |   10.7   | 63.7 |  [config](./pose/yolox-pose_s_8xb32-300e-rtmdet-hyp_coco.py)   |                                                                                                                                                                                                                                                                                                                                                                                                                                                                            [model](https://download.openmmlab.com/mmyolo/v0/yolox/pose/yolox-pose_s_8xb32-300e-rtmdet-hyp_coco/yolox-pose_s_8xb32-300e-rtmdet-hyp_coco_20230427_005150-e87d843a.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/pose/yolox-pose_s_8xb32-300e-rtmdet-hyp_coco/yolox-pose_s_8xb32-300e-rtmdet-hyp_coco_20230427_005150.log.json)                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+|  YOLOX-m   | 640  |   8xb32    | Yes |    Yes     |   19.2   | 69.3 |  [config](./pose/yolox-pose_m_8xb32-300e-rtmdet-hyp_coco.py)   |                                                                                                                                                                                                                                                                                                                                                                                                                                                                            [model](https://download.openmmlab.com/mmyolo/v0/yolox/pose/yolox-pose_m_8xb32-300e-rtmdet-hyp_coco/yolox-pose_m_8xb32-300e-rtmdet-hyp_coco_20230427_094024-bbeacc1c.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/pose/yolox-pose_m_8xb32-300e-rtmdet-hyp_coco/yolox-pose_m_8xb32-300e-rtmdet-hyp_coco_20230427_094024.log.json)                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+|  YOLOX-l   | 640  |   8xb32    | Yes |    Yes     |   30.3   | 71.1 |  [config](./pose/yolox-pose_l_8xb32-300e-rtmdet-hyp_coco.py)   |                                                                                                                                                                                                                                                                                                                                                                                                                                                                            [model](https://download.openmmlab.com/mmyolo/v0/yolox/pose/yolox-pose_l_8xb32-300e-rtmdet-hyp_coco/yolox-pose_l_8xb32-300e-rtmdet-hyp_coco_20230427_041140-82d65ac8.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/pose/yolox-pose_l_8xb32-300e-rtmdet-hyp_coco/yolox-pose_l_8xb32-300e-rtmdet-hyp_coco_20230427_041140.log.json)                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+
+**Note**
+
+1. The performance is unstable and may fluctuate and the highest performance weight in `COCO` training may not be the last epoch. The performance shown above is the best model.
+
+
+### Installation
+
+
+
 ## Citation
 
 ```latex

configs/yolox/metafile.yml

@@ -116,3 +116,51 @@ Models:
         Metrics:
           box AP: 47.5
     Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco_20230210_144328-e657e182.pth
+  - Name: yolox-pose_tiny_8xb32-300e-rtmdet-hyp_coco
+    In Collection: YOLOX
+    Config: yolox-pose_tiny_8xb32-300e-rtmdet-hyp_coco.py
+    Metadata:
+      Training Memory (GB): 5.3
+      Epochs: 300
+    Results:
+      - Task: Human Pose Estimation
+        Dataset: COCO
+        Metrics:
+          AP: 52.8
+    Weights: https://download.openmmlab.com/mmyolo/v0/yolox/pose/yolox-pose_tiny_8xb32-300e-rtmdet-hyp_coco/yolox-pose_tiny_8xb32-300e-rtmdet-hyp_coco_20230427_080351-2117af67.pth
+  - Name: yolox-pose_s_8xb32-300e-rtmdet-hyp_coco
+    In Collection: YOLOX
+    Config: yolox-pose_s_8xb32-300e-rtmdet-hyp_coco.py
+    Metadata:
+      Training Memory (GB): 10.7
+      Epochs: 300
+    Results:
+      - Task: Human Pose Estimation
+        Dataset: COCO
+        Metrics:
+          AP: 63.7
+    Weights: https://download.openmmlab.com/mmyolo/v0/yolox/pose/yolox-pose_s_8xb32-300e-rtmdet-hyp_coco/yolox-pose_s_8xb32-300e-rtmdet-hyp_coco_20230427_005150-e87d843a.pth
+  - Name: yolox-pose_m_8xb32-300e-rtmdet-hyp_coco
+    In Collection: YOLOX
+    Config: yolox-pose_m_8xb32-300e-rtmdet-hyp_coco.py
+    Metadata:
+      Training Memory (GB): 19.2
+      Epochs: 300
+    Results:
+      - Task: Human Pose Estimation
+        Dataset: COCO
+        Metrics:
+          AP: 69.3
+    Weights: https://download.openmmlab.com/mmyolo/v0/yolox/pose/yolox-pose_m_8xb32-300e-rtmdet-hyp_coco/yolox-pose_m_8xb32-300e-rtmdet-hyp_coco_20230427_094024-bbeacc1c.pth
+  - Name: yolox-pose_l_8xb32-300e-rtmdet-hyp_coco
+    In Collection: YOLOX
+    Config: yolox-pose_l_8xb32-300e-rtmdet-hyp_coco.py
+    Metadata:
+      Training Memory (GB): 30.3
+      Epochs: 300
+    Results:
+      - Task: Human Pose Estimation
+        Dataset: COCO
+        Metrics:
+          AP: 71.1
+    Weights: https://download.openmmlab.com/mmyolo/v0/yolox/pose/yolox-pose_l_8xb32-300e-rtmdet-hyp_coco/yolox-pose_l_8xb32-300e-rtmdet-hyp_coco_20230427_041140-82d65ac8.pth

configs/yolox/pose/yolox-pose_l_8xb32-300e-rtmdet-hyp_coco.py

@@ -0,0 +1,14 @@
+_base_ = ['./yolox-pose_m_8xb32-300e-rtmdet-hyp_coco.py']
+
+load_from = 'https://download.openmmlab.com/mmyolo/v0/yolox/yolox_l_fast_8xb8-300e_coco/yolox_l_fast_8xb8-300e_coco_20230213_160715-c731eb1c.pth'  # noqa
+
+# ========================modified parameters======================
+deepen_factor = 1.0
+widen_factor = 1.0
+
+# =======================Unmodified in most cases==================
+# model settings
+model = dict(
+    backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
+    neck=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
+    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))

configs/yolox/pose/yolox-pose_m_8xb32-300e-rtmdet-hyp_coco.py

@@ -0,0 +1,14 @@
+_base_ = ['./yolox-pose_s_8xb32-300e-rtmdet-hyp_coco.py']
+
+load_from = 'https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco_20230210_144328-e657e182.pth'  # noqa
+
+# ========================modified parameters======================
+deepen_factor = 0.67
+widen_factor = 0.75
+
+# =======================Unmodified in most cases==================
+# model settings
+model = dict(
+    backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
+    neck=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
+    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))