[Fix] Update links of configs and restrict the version of MMCV (#2337)

* update config link in readme

* update version restriction

* update

* fix some configs error in metafile

* add pvrcnn and fcaf3d in model-index.yml

configs/centerpoint/README.md

@@ -105,21 +105,21 @@ data = dict(
 ### CenterPoint
 
 |                                           Backbone                                           | Voxel type (voxel size) | Dcn | Circular nms | Mem (GB) | Inf time (fps) |  mAP  |  NDS  |                                                                                                                                                                                                                                               Download                                                                                                                                                                                                                                                |
-| :---------------------------------------------------------------------------------: | :---------------------: | :-: | :----------: | :------: | :------------: | :---: | :---: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-|    [SECFPN](./centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus.py)    |       voxel (0.1)       |  ✗  |      ✓       |   5.2    |                | 56.11 | 64.61 |             [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220810_030004-9061688e.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220810_030004.log)             |
+| :------------------------------------------------------------------------------------------: | :---------------------: | :-: | :----------: | :------: | :------------: | :---: | :---: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    [SECFPN](./centerpoint_voxel01_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d.py)    |       voxel (0.1)       |  ✗  |      ✓       |   5.2    |                | 56.11 | 64.61 |             [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220810_030004-9061688e.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220810_030004.log)             |
 |                                     above w/o circle nms                                     |       voxel (0.1)       |  ✗  |      ✗       |          |                |   x   |   x   |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-|  [SECFPN](./centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus.py)  |       voxel (0.1)       |  ✓  |      ✓       |   5.5    |                | 56.10 | 64.69 |     [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20220810_052355-a6928835.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20220810_052355.log)     |
+|  [SECFPN](./centerpoint_voxel01_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py)  |       voxel (0.1)       |  ✓  |      ✓       |   5.5    |                | 56.10 | 64.69 |     [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20220810_052355-a6928835.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20220810_052355.log)     |
 |                                     above w/o circle nms                                     |       voxel (0.1)       |  ✓  |      ✗       |          |                |   x   |   x   |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-|   [SECFPN](./centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus.py)   |      voxel (0.075)      |  ✗  |      ✓       |   8.2    |                | 56.54 | 65.17 |         [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220810_011659-04cb3a3b.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220810_011659.log)         |
+|   [SECFPN](./centerpoint_voxel0075_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d.py)   |      voxel (0.075)      |  ✗  |      ✓       |   8.2    |                | 56.54 | 65.17 |         [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220810_011659-04cb3a3b.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220810_011659.log)         |
 |                                     above w/o circle nms                                     |      voxel (0.075)      |  ✗  |      ✗       |          |                | 57.63 | 65.39 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-| [SECFPN](./centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus.py) |      voxel (0.075)      |  ✓  |      ✓       |   8.7    |                | 56.92 | 65.27 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20220810_025930-657f67e0.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20220810_025930.log) |
+| [SECFPN](./centerpoint_voxel0075_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py) |      voxel (0.075)      |  ✓  |      ✓       |   8.7    |                | 56.92 | 65.27 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20220810_025930-657f67e0.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20220810_025930.log) |
 |                                     above w/o circle nms                                     |      voxel (0.075)      |  ✓  |      ✗       |          |                | 57.43 | 65.63 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
 |                                     above w/ double flip                                     |      voxel (0.075)      |  ✓  |      ✗       |          |                | 59.73 | 67.39 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
 |                                      above w/ scale tta                                      |      voxel (0.075)      |  ✓  |      ✗       |          |                | 60.43 | 67.65 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
 |                              above w/ circle nms w/o scale tta                               |      voxel (0.075)      |  ✓  |      ✗       |          |                | 59.52 | 67.24 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-|   [SECFPN](./centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus.py)    |      pillar (0.2)       |  ✗  |      ✓       |   4.6    |                | 48.70 | 59.62 |           [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220811_031844-191a3822.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220811_031844.log)           |
+|   [SECFPN](./centerpoint_pillar02_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d.py)    |      pillar (0.2)       |  ✗  |      ✓       |   4.6    |                | 48.70 | 59.62 |           [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220811_031844-191a3822.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220811_031844.log)           |
 |                                     above w/o circle nms                                     |      pillar (0.2)       |  ✗  |      ✗       |          |                | 49.12 | 59.66 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-|      [SECFPN](./centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus.py)       |      pillar (0.2)       |  ✓  |      ✗       |   4.9    |                | 48.38 | 59.79 |                       [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus_20220811_045458-808e69ad.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus_20220811_045458.log)                       |
+|      [SECFPN](./centerpoint_pillar02_second_secfpn_head-dcn_8xb4-cyclic-20e_nus-3d.py)       |      pillar (0.2)       |  ✓  |      ✗       |   4.9    |                | 48.38 | 59.79 |                       [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus_20220811_045458-808e69ad.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus_20220811_045458.log)                       |
 |                                     above w/ circle nms                                      |      pillar (0.2)       |  ✓  |      ✓       |          |                | 48.79 | 59.65 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
 
 **Note:** The model performance after coordinate refactor is slightly different (+/- 0.5 - 1 mAP/NDS) from the performance before coordinate refactor in v0.x branch. We are exploring the reason behind.                                                                                                                                                                                                                                                                                                                                                                                                                                                         |

configs/fcos3d/README.md

@@ -52,7 +52,7 @@ We also provide visualization functions to show the monocular 3D detection resul
 
 |                                        Backbone                                         | Lr schd | Mem (GB) | Inf time (fps) | mAP  | NDS  |                                                                                                                                                                                                                             Download                                                                                                                                                                                                                             |
 | :-------------------------------------------------------------------------------------: | :-----: | :------: | :------------: | :--: | :--: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-|     \[ResNet101 w/ DCN\](./fcos3d_r101-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d.py)     |   1x    |   8.69   |                | 29.8 | 37.7 |                   [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210715_235813-4bed5239.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210715_235813.log.json)                   |
+|      [ResNet101 w/ DCN](./fcos3d_r101-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d.py)      |   1x    |   8.69   |                | 29.8 | 37.7 |                   [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210715_235813-4bed5239.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210715_235813.log.json)                   |
 | [above w/ finetune](./fcos3d_r101-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d_finetune.py) |   1x    |   8.69   |                | 32.1 | 39.5 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210717_095645-8d806dc2.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210717_095645.log.json) |
 |                                      above w/ tta                                       |   1x    |   8.69   |                | 33.1 | 40.3 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
 

configs/pointpillars/README.md

@@ -30,9 +30,9 @@ We implement PointPillars and provide the results and checkpoints on KITTI, nuSc
 |                                Backbone                                 | Lr schd | Mem (GB) | Inf time (fps) |  mAP  |  NDS  |                                                                                                                                                                                                     Download                                                                                                                                                                                                     |
 | :---------------------------------------------------------------------: | :-----: | :------: | :------------: | :---: | :---: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
 |      [SECFPN](./pointpillars_hv_secfpn_sbn-all_8xb4-2x_nus-3d.py)       |   2x    |   16.4   |                | 34.33 | 49.1  |   [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20210826_225857-f19d00a3.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20210826_225857.log.json)   |
-| [SECFPN (FP16)](./hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d.py) |   2x    |   8.37   |                | 35.19 | 50.27 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d_20201020_222626-c3f0483e.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d_20201020_222626.log.json) |
+| [SECFPN (FP16)](./pointpillars_hv_secfpn_sbn-all_8xb2-amp-2x_nus-3d.py) |   2x    |   8.37   |                | 35.19 | 50.27 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d_20201020_222626-c3f0483e.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d_20201020_222626.log.json) |
 |         [FPN](./pointpillars_hv_fpn_sbn-all_8xb4-2x_nus-3d.py)          |   2x    |   16.3   |                | 39.7  | 53.2  |         [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20210826_104936-fca299c1.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20210826_104936.log.json)         |
-|    [FPN (FP16)](./hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d.py)    |   2x    |   8.40   |                | 39.26 | 53.26 |       [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d_20201021_120719-269f9dd6.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d_20201021_120719.log.json)       |
+|    [FPN (FP16)](./pointpillars_hv_fpn_sbn-all_8xb2-amp-2x_nus-3d.py)    |   2x    |   8.40   |                | 39.26 | 53.26 |       [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d_20201021_120719-269f9dd6.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d_20201021_120719.log.json)       |
 
 ### Lyft
 

configs/pointpillars/metafile.yml

@@ -147,9 +147,9 @@ Models:
           Public Score: 15.0
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d_20210822_095429-0b3d6196.pth
 
-  - Name: hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-car
+  - Name: pointpillars_hv_secfpn_sbn_2x16_2x_waymoD5-3d-car
     In Collection: PointPillars
-    Config: configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-car.py
+    Config: configs/pointpillars/pointpillars_hv_secfpn_sbn_2x16_2x_waymoD5-3d-car.py
     Metadata:
       Training Data: Waymo
       Training Memory (GB): 7.76
@@ -164,7 +164,7 @@ Models:
           mAPH@L2: 62.1
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-car/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-car_20200901_204315-302fc3e7.pth
 
-  - Name: hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class
+  - Name: pointpillars_hv_secfpn_sbn_2x16_2x_waymoD5-3d-3class
     Alias: pointpillars_waymod5-3class
     In Collection: PointPillars
     Config: configs/pointpillars/pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymoD5-3d-3class.py
@@ -182,7 +182,7 @@ Models:
           mAPH@L2: 52.1
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class_20200831_204144-d1a706b1.pth
 
-  - Name: hv_pointpillars_secfpn_sbn_2x16_2x_waymo-3d-car
+  - Name: pointpillars_hv_secfpn_sbn_2x16_2x_waymo-3d-car
     In Collection: PointPillars
     Config: configs/pointpillars/pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymo-3d-car.py
     Metadata:
@@ -198,7 +198,7 @@ Models:
           mAP@L2: 63.6
           mAPH@L2: 63.1
 
-  - Name: hv_pointpillars_secfpn_sbn_2x16_2x_waymo-3d-3class
+  - Name: pointpillars_hv_secfpn_sbn_2x16_2x_waymo-3d-3class
     In Collection: PointPillars
     Config: configs/pointpillars/pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymo-3d-3class.py
     Metadata:

configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb4-amp-2x_nus-3d.py → configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-amp-2x_nus-3d.py

-_base_ = 'pointpillars_hv_fpn_sbn-all_8xb4-2x_nus-3d.py'
-
+_base_ = './pointpillars_hv_fpn_sbn-all_8xb4-2x_nus-3d.py'
+train_dataloader = dict(batch_size=2, num_workers=2)
 # schedule settings
 optim_wrapper = dict(type='AmpOptimWrapper', loss_scale=512.)

configs/pointpillars/pointpillars_hv_secfpn_sbn-all_8xb4-amp-2x_nus-3d.py → configs/pointpillars/pointpillars_hv_secfpn_sbn-all_8xb2-amp-2x_nus-3d.py

-_base_ = 'pointpillars_hv_secfpn_sbn-all_8xb4-2x_nus-3d.py'
-
+_base_ = './pointpillars_hv_secfpn_sbn-all_8xb4-2x_nus-3d.py'
+train_dataloader = dict(batch_size=2, num_workers=2)
 # schedule settings
 optim_wrapper = dict(type='AmpOptimWrapper', loss_scale=512.)

configs/pv_rcnn/README.md

@@ -4,7 +4,7 @@
 
 <!-- [ALGORITHM] -->
 
-## Introduction
+## Abstract
 
 3D object detection has been receiving increasing attention from both industry and academia thanks to its wide applications in various fields such as autonomous driving and robotics. LiDAR sensors are widely adopted in autonomous driving vehicles and robots for capturing 3D scene information as sparse and irregular point clouds, which provide vital cues for 3D scene perception and understanding. In this paper, we propose to achieve high performance 3D object detection by designing novel point-voxel integrated networks to learn better 3D features from irregular point clouds.
 

configs/regnet/metafile.yml

 Models:
-  - Name: hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d
+  - Name: pointpillars_hv_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d
     In Collection: PointPillars
-    Config: configs/regnet/hv_pointpillars_regnet-400mf_secfpn_sbn-all_8xb4-2x_nus-3d.py
+    Config: configs/regnet/pointpillars_hv_regnet-400mf_secfpn_sbn-all_8xb4-2x_nus-3d.py
     Metadata:
       Training Data: nuScenes
       Training Memory (GB): 16.4
@@ -16,9 +16,9 @@ Models:
           NDS: 55.2
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230334-53044f32.pth
 
-  - Name: hv_pointpillars_regnet-400mf_fpn_sbn-all_8xb4-2x_nus-3d
+  - Name: pointpillars_hv_regnet-400mf_fpn_sbn-all_8xb4-2x_nus-3d
     In Collection: PointPillars
-    Config: configs/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_8xb4-2x_nus-3d.py
+    Config: configs/regnet/pointpillars_hv_regnet-400mf_fpn_sbn-all_8xb4-2x_nus-3d.py
     Metadata:
       Training Data: nuScenes
       Training Memory (GB): 17.3
@@ -33,9 +33,9 @@ Models:
           NDS: 56.4
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d_20200620_230239-c694dce7.pth
 
-  - Name: hv_pointpillars_regnet-1.6gf_fpn_sbn-all_8xb4-2x_nus-3d
+  - Name: pointpillars_hv_regnet-1.6gf_fpn_sbn-all_8xb4-2x_nus-3d
     In Collection: PointPillars
-    Config: configs/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_8xb4-2x_nus-3d.py
+    Config: configs/regnet/pointpillars_hv_regnet-1.6gf_fpn_sbn-all_8xb4-2x_nus-3d.py
     Metadata:
       Training Data: nuScenes
       Training Memory (GB): 24.0
@@ -50,9 +50,9 @@ Models:
           NDS: 59.3
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d_20200629_050311-dcd4e090.pth
 
-  - Name: hv_pointpillars_regnet-400mf_secfpn_sbn-all_2x8_2x_lyft-3d
+  - Name: pointpillars_hv_regnet-400mf_secfpn_sbn-all_2x8_2x_lyft-3d
     In Collection: PointPillars
-    Config: configs/regnet/hv_pointpillars_regnet-400mf_secfpn_sbn-all_2x8_2x_lyft-3d.py
+    Config: configs/regnet/pointpillars_hv_regnet-400mf_secfpn_sbn-all_2x8_2x_lyft-3d.py
     Metadata:
       Training Data: Lyft
       Training Memory (GB): 15.9
@@ -67,9 +67,9 @@ Models:
           Public Score: 15.1
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_secfpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_regnet-400mf_secfpn_sbn-all_2x8_2x_lyft-3d_20210524_092151-42513826.pth
 
-  - Name: hv_pointpillars_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d
+  - Name: pointpillars_hv_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d
     In Collection: PointPillars
-    Config: configs/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d.py
+    Config: configs/regnet/pointpillars_hv_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d.py
     Metadata:
       Training Data: Lyft
       Training Memory (GB): 13.0

configs/second/metafile.yml

@@ -60,7 +60,7 @@ Models:
 
   - Name: second_hv_secfpn_8xb6-amp-80e_kitti-3d-car
     In Collection: SECOND
-    Config: configs/second/hv_second_secfpn_8xb6-amp-80e_kitti-3d-car.py
+    Config: configs/second/second_hv_secfpn_8xb6-amp-80e_kitti-3d-car.py
     Metadata:
       Training Techniques:
         - AdamW
@@ -79,7 +79,7 @@ Models:
 
   - Name: second_hv_secfpn_8xb6-amp-80e_kitti-3d-3class
     In Collection: SECOND
-    Config: configs/second/hv_second_secfpn_8xb6-amp-80e_kitti-3d-3class.py
+    Config: configs/second/second_hv_secfpn_8xb6-amp-80e_kitti-3d-3class.py
     Metadata:
       Training Techniques:
         - AdamW

configs/smoke/metafile.yml

@@ -17,9 +17,9 @@ Collections:
       Version: v1.0.0
 
 Models:
-  - Name: smoke_dla34_pytorch_dlaneck_gn-all_4xb8-6x_kitti-mono3d
+  - Name: smoke_dla34_dlaneck_gn-all_4xb8-6x_kitti-mono3d
     In Collection: SMOKE
-    Config: configs/smoke/smoke_dla34_pytorch_dlaneck_gn-all_4xb8-6x_kitti-mono3d.py
+    Config: configs/smoke/smoke_dla34_dlaneck_gn-all_4xb8-6x_kitti-mono3d.py
     Metadata:
       Training Memory (GB): 9.6
     Results:

docs/en/notes/faq.md

@@ -11,8 +11,8 @@ We list some potential troubles encountered by users and developers, along with
   | MMDetection3D version |     MMEngine version     |      MMCV version       |   MMDetection version    |
   | --------------------- | :----------------------: | :---------------------: | :----------------------: |
   | dev-1.x               | mmengine>=0.6.0, \<1.0.0 | mmcv>=2.0.0rc4, \<2.1.0 | mmdet>=3.0.0rc0, \<3.1.0 |
-  | v1.1.0rc3             | mmengine>=0.1.0, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 | mmdet>=3.0.0rc0, \<3.1.0 |
-  | v1.1.0rc2             | mmengine>=0.1.0, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 | mmdet>=3.0.0rc0, \<3.1.0 |
+  | v1.1.0rc3             | mmengine>=0.1.0, \<1.0.0 | mmcv>=2.0.0rc3, \<2.1.0 | mmdet>=3.0.0rc0, \<3.1.0 |
+  | v1.1.0rc2             | mmengine>=0.1.0, \<1.0.0 | mmcv>=2.0.0rc3, \<2.1.0 | mmdet>=3.0.0rc0, \<3.1.0 |
   | v1.1.0rc1             | mmengine>=0.1.0, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 | mmdet>=3.0.0rc0, \<3.1.0 |
   | v1.1.0rc0             | mmengine>=0.1.0, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 | mmdet>=3.0.0rc0, \<3.1.0 |
 

docs/zh_cn/get_started.md

@@ -44,7 +44,7 @@ conda install pytorch torchvision cpuonly -c pytorch
 ```shell
 pip install -U openmim
 mim install mmengine
-mim install 'mmcv>=2.0.0rc0'
+mim install 'mmcv>=2.0.0rc4'
 mim install 'mmdet>=3.0.0rc0'
 ```
 

model-index.yml

@@ -23,3 +23,5 @@ Import:
   - configs/smoke/metafile.yml
   - configs/ssn/metafile.yml
   - configs/votenet/metafile.yml
+  - configs/pv_rcnn/metafile.yml
+  - configs/fcaf3d/metafile.yml

projects/PETR/README.md

@@ -35,8 +35,8 @@ python tools/test.py projects/PETR/config/petr/petr_vovnet_gridmask_p4_800x320.p
 This Result is trained by petr_vovnet_gridmask_p4_800x320.py and use [weights](https://drive.google.com/file/d/1ABI5BoQCkCkP4B0pO5KBJ3Ni0tei0gZi/view?usp=sharing) as pretrain weight.
 
 |                                   Backbone                                    | Lr schd | Mem (GB) | Inf time (fps) | mAP  | NDS  |         Download         |
-| :----------------------------------------------------------------------------------------------: | :-----: | :------: | :------------: | :--: | :--: | :----------------------: |
-| [petr_vovnet_gridmask_p4_800x320](projects/PETR/configs/petr/petr_vovnet_gridmask_p4_800x320.py) |   1x    |   7.62   |      18.7      | 38.3 | 43.5 | [model](<>) \| [log](<>) |
+| :---------------------------------------------------------------------------: | :-----: | :------: | :------------: | :--: | :--: | :----------------------: |
+| [petr_vovnet_gridmask_p4_800x320](configs/petr_vovnet_gridmask_p4_800x320.py) |   1x    |   7.62   |      18.7      | 38.3 | 43.5 | [model](<>) \| [log](<>) |
 
 ```
 mAP: 0.3830