[Docs] Update all the metafiles acoording to README (#2006)

* update centerpoint, dgcnn, fcos3d and free_anchor metafile

* check all metafile in configs

* update centerpoint, pointpillars and second yml

* rename PartA2 to parta2 in the name of config

* update metafile in nuimages

* update readme in nuimages

.dev_scripts/test_benchmark.sh

@@ -73,11 +73,11 @@ GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION dv_mvx-f
 $CHECKPOINT_DIR/configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py/latest.pth --eval map \
 2>&1|tee $CHECKPOINT_DIR/configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py/FULL_LOG.txt &
 
-echo 'configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py' &
-mkdir -p $CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py
-GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py \
-$CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/latest.pth --eval map \
-2>&1|tee $CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/FULL_LOG.txt &
+echo 'configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py' &
+mkdir -p $CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py \
+$CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/latest.pth --eval map \
+2>&1|tee $CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/FULL_LOG.txt &
 
 echo 'configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py' &
 mkdir -p $CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py

.dev_scripts/train_benchmark.sh

@@ -73,11 +73,11 @@ GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION dv_mvx-
 $CHECKPOINT_DIR/configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
 2>&1|tee $CHECKPOINT_DIR/configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py/FULL_LOG.txt &
 
-echo 'configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py' &
-mkdir -p $CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py
-GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py \
-$CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
-2>&1|tee $CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/FULL_LOG.txt &
+echo 'configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py' &
+mkdir -p $CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py \
+$CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/FULL_LOG.txt &
 
 echo 'configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py' &
 mkdir -p $CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py

configs/centerpoint/README.md

@@ -105,23 +105,25 @@ data = dict(
 ### CenterPoint
 
 |                                      Backbone                                       | Voxel type (voxel size) | Dcn | Circular nms | Mem (GB) | Inf time (fps) |  mAP  |  NDS  |                                                                                                                                                                                                                                               Download                                                                                                                                                                                                                                                |
-| :----------------------------------------------------------------------------------------: | :---------------------: | :-: | :----------: | :------: | :------------: | :---: | :---: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-|   [SECFPN](./centerpoint_voxel01_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d.py)   |       voxel (0.1)       |  ✗  |      ✓       |   4.9    |                | 56.19 | 64.43 |             [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_20210815_085857-9ba7f3a5.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_20210815_085857.log.json)             |
-|                                    above w/o circle nms                                    |       voxel (0.1)       |  ✗  |      ✗       |          |                | 56.56 | 64.46 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
-| [SECFPN](./centerpoint_voxel01_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py) |       voxel (0.1)       |  ✓  |      ✓       |   5.2    |                | 56.34 | 64.81 |     [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_20210814_060754-c9d535d2.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_20210814_060754.log.json)     |
-|                                    above w/o circle nms                                    |       voxel (0.1)       |  ✓  |      ✗       |          |                | 56.60 | 64.90 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
-|  [SECFPN](./centerpoint_voxel0075_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d.py)  |      voxel (0.075)      |  ✗  |      ✓       |   7.8    |                | 57.34 | 65.23 |         [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_20210814_113418-76ae0cf0.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_20210814_113418.log.json)         |
+| :---------------------------------------------------------------------------------: | :---------------------: | :-: | :----------: | :------: | :------------: | :---: | :---: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    [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)             |
+|                                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)     |
+|                                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)         |
 |                                above w/o circle nms                                 |      voxel (0.075)      |  ✗  |      ✗       |          |                | 57.63 | 65.39 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-| [SECFPN](./centerpoint_voxel0075-second-secfpn-head-dcn=circlenms_8xb4-cyclic-20e_nus.py)  |      voxel (0.075)      |  ✓  |      ✓       |   8.5    |                | 57.27 | 65.58 | [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_20210827_161135-1782af3e.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_20210827_161135.log.json) |
+| [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) |
 |                                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_pillar02_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d.py)   |      pillar (0.2)       |  ✗  |      ✓       |   4.4    |                | 49.07 | 59.66 |           [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_20210816_064624-0f3299c0.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_20210816_064624.log.json)           |
+|   [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)           |
 |                                above w/o circle nms                                 |      pillar (0.2)       |  ✗  |      ✗       |          |                | 49.12 | 59.66 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-|     [SECFPN](./centerpoint_pillar02_second_secfpn_head-dcn_8xb4-cyclic-20e_nus-3d.py)      |      pillar (0.2)       |  ✓  |      ✗       |   4.6    |                | 48.8  | 59.67 |                       [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_20210815_202702-f03ab9e4.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_20210815_202702.log.json)                       |
+|      [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)                       |
 |                                 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.                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
+
 ## Citation
 
 ```latex

configs/centerpoint/metafile.yml

@@ -16,80 +16,80 @@ Collections:
       Version: v0.6.0
 
 Models:
-  - Name: centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus
+  - Name: centerpoint_voxel01_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d
     In Collection: CenterPoint
     Config: configs/centerpoint/centerpoint_voxel01_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d.py
-    Metadata:
-      Training Memory (GB): 4.9
+    metadata:
+      Training Memory (GB): 5.2
     Results:
       - Task: 3D Object Detection
         Dataset: nuScenes
         Metrics:
-          mAP: 56.19
-          NDS: 64.43
-    Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20201001_135205-5db91e00.pth
+          mAP: 56.11
+          NDS: 64.61
+    Weights: 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
 
-  - Name: centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus
+  - Name: centerpoint_voxel01_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d
     In Collection: CenterPoint
     Config: configs/centerpoint/centerpoint_voxel01_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py
     Metadata:
-      Training Memory (GB): 5.2
+      Training Memory (GB): 5.5
     Results:
       - Task: 3D Object Detection
         Dataset: nuScenes
         Metrics:
-          mAP: 56.34
-          NDS: 64.81
-    Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20201004_075317-26d8176c.pth
+          mAP: 56.10
+          NDS: 64.69
+    Weights: 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
 
-  - Name: centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus
+  - Name: centerpoint_voxel0075_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d
     In Collection: CenterPoint
     Config: configs/centerpoint/centerpoint_voxel0075_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d.py
     Metadata:
-      Training Memory (GB): 7.8
+      Training Memory (GB): 8.2
     Results:
       - Task: 3D Object Detection
         Dataset: nuScenes
         Metrics:
-          mAP: 57.34
-          NDS: 65.23
-    Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20200925_230905-358fbe3b.pth
+          mAP: 56.54
+          NDS: 65.17
+    Weights: 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
 
-  - Name: centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus
+  - Name: centerpoint_voxel0075_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d
     In Collection: CenterPoint
     Config: configs/centerpoint/centerpoint_voxel0075_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py
     Metadata:
-      Training Memory (GB): 8.5
+      Training Memory (GB): 8.7
     Results:
       - Task: 3D Object Detection
         Dataset: nuScenes
         Metrics:
-          mAP: 57.27
-          NDS: 65.58
-    Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20200930_201619-67c8496f.pth
+          mAP: 56.92
+          NDS: 65.27
+    Weights: 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
 
-  - Name: centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus
+  - Name: centerpoint_pillar02_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d
     In Collection: CenterPoint
     Config: configs/centerpoint/centerpoint_pillar02_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d.py
     Metadata:
-      Training Memory (GB): 4.4
+      Training Memory (GB): 4.6
     Results:
       - Task: 3D Object Detection
         Dataset: nuScenes
         Metrics:
-          mAP: 49.07
-          NDS: 59.66
-    Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20201004_170716-a134a233.pth
+          mAP: 48.70
+          NDS: 59.62
+    Weights: 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
 
-  - Name: centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus
+  - Name: centerpoint_pillar02_second_secfpn_head-dcn_8xb4-cyclic-20e_nus-3d
     In Collection: CenterPoint
     Config: configs/centerpoint/centerpoint_pillar02_second_secfpn_head-dcn_8xb4-cyclic-20e_nus-3d.py
     Metadata:
-      Training Memory (GB): 4.6
+      Training Memory (GB): 4.9
     Results:
       - Task: 3D Object Detection
         Dataset: nuScenes
         Metrics:
-          mAP: 48.8
-          NDS: 59.67
-    Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus_20200930_103722-3bb135f2.pth
+          mAP: 48.38
+          NDS: 59.79
+    Weights: 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

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- fpn-head-gn-dcn_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/fcos3d/metafile.yml

@@ -27,7 +27,7 @@ Models:
         Metrics:
           mAP: 29.9
           NDS: 37.3
-    Weights: 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_20210425_181341-8d5a21fe.pth
+    Weights: 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
 
   - Name: fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune
     In Collection: FCOS3D
@@ -40,4 +40,4 @@ Models:
         Metrics:
           mAP: 32.1
           NDS: 39.3
-    Weights: 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_20210427_091419-35aaaad0.pth
+    Weights: 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

configs/free_anchor/metafile.yml

@@ -17,7 +17,20 @@ Collections:
       Version: v0.5.0
 
 Models:
-  - Name: hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d
+  - Name: pointpillars_hv_fpn_sbn-all_8xb4-2x_nus-3d
+    In Collection: FreeAnchor
+    Config: pointpillars/pointpillars_hv_fpn_sbn-all_8xb4-2x_nus-3d.py
+    Metadata:
+      Training Memory (GB): 17.1
+    Results:
+      - Task: 3D Object Detection
+        Dataset: nuScenes
+        Metrics:
+          mAP: 40.0
+          NDS: 53.3
+    Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405-2fa62f3d.pth
+
+  - Name: pointpillars_hv_fpn_head-free-anchor_sbn-all_8xb4-2x_nus-3d
     In Collection: FreeAnchor
     Config: free_anchor/pointpillars_hv_fpn_head-free-anchor_sbn-all_8xb4-2x_nus-3d.py
     Metadata:
@@ -28,9 +41,22 @@ Models:
         Metrics:
           mAP: 43.82
           NDS: 54.86
+    Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210816_163441-ae0897e7.pth
+
+  - Name: pointpillars_hv_regnet-400mf_fpn_sbn-all_8xb4-2x_nus-3d
+    In Collection: FreeAnchor
+    Config: configs/regnet/pointpillars_hv_regnet-400mf_fpn_sbn-all_8xb4-2x_nus-3d.py
+    Metadata:
+      Training Memory (GB): 17.3
+    Results:
+      - Task: 3D Object Detection
+        Dataset: nuScenes
+        Metrics:
+          mAP: 44.8
+          NDS: 56.4
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210816_163441-ae0897e7.pth
 
-  - Name: hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d
+  - Name: pointpillars_hv_regnet-400mf_fpn_head-free-anchor_sbn-all_8xb4-2x_nus-3d
     In Collection: FreeAnchor
     Config: configs/free_anchor/pointpillars_hv_regnet-400mf_fpn_head-free-anchor_sbn-all_8xb4-2x_nus-3d.py
     Metadata:
@@ -56,7 +82,7 @@ Models:
           NDS: 61.49
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210828_025608-bfbd506e.pth
 
-  - Name: hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d
+  - Name: pointpillars_hv_regnet-1.6gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d
     In Collection: FreeAnchor
     Config: configs/free_anchor/pointpillars_hv_regnet-1.6gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d.py
     Metadata:
@@ -69,7 +95,7 @@ Models:
           NDS: 62.45
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d_20210827_184909-14d2dbd1.pth
 
-  - Name: hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d
+  - Name: pointpillars_hv_regnet-3.2gf_fpn_head-free-anchor_sbn-all_8xb4-2x_nus-3d
     In Collection: FreeAnchor
     Config: configs/free_anchor/pointpillars_hv_regnet-3.2gf_fpn_head-free-anchor_sbn-all_8xb4-2x_nus-3d.py
     Metadata:
@@ -82,7 +108,7 @@ Models:
           NDS: 61.94
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210827_181237-e385c35a.pth
 
-  - Name: hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d
+  - Name: pointpillars_hv_regnet-3.2gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d
     In Collection: FreeAnchor
     Config: configs/free_anchor/pointpillars_hv_regnet-3.2gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d.py
     Metadata:

configs/monoflex/metafile.yml

@@ -26,5 +26,5 @@ Models:
       - Task: 3D Object Detection
         Dataset: KITTI
         Metrics:
-          mAP: 21.98
+          mAP: 21.86
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/monoflex/monoflex_dla34_pytorch_dlaneck_gn-all_2x4_6x_kitti-mono3d_20211228_027553-d46d9bb0.pth

configs/nuimages/README.md

-# NuImages Results
+# Mask R-CNN
 
-<!-- [DATASET] -->
+> [Mask R-CNN](https://arxiv.org/abs/1703.06870)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without bells and whistles, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143967081-c2552bed-9af2-46c4-ae44-5b3b74e5679f.png"/>
+</div>
 
 ## Introduction
 

configs/nuimages/metafile.yml

+Collections:
+  - Name: Mask R-CNN
+    Metadata:
+      Training Data: nuImages
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x TITAN Xp
+      Architecture:
+        - Softmax
+        - RPN
+        - Convolution
+        - Dense Connections
+        - FPN
+        - ResNet
+        - RoIAlign
+    Paper:
+      URL: https://arxiv.org/abs/1703.06870v3
+      Title: "Mask R-CNN"
+    README: configs/nuimages/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/mask_rcnn.py#L6
+      Version: v2.0.0
+
 Models:
   - Name: mask_rcnn_r50_fpn_1x_nuim
     In Collection: Mask R-CNN

configs/paconv/metafile.yml

@@ -27,3 +27,16 @@ Models:
         Metrics:
           mIoU: 66.65
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/paconv/paconv_ssg_8x8_cosine_150e_s3dis_seg-3d-13class/paconv_ssg_8x8_cosine_150e_s3dis_seg-3d-13class_20210729_200615-2147b2d1.pth
+
+  - Name: paconv_ssg-cuda_8xb8-cosine-200e_s3dis-seg
+    In Collection: PAConv
+    Config: configs/paconv/paconv_ssg-cuda_8xb8-cosine-200e_s3dis-seg.py
+    Metadata:
+      Training Data: S3DIS
+      Training Memory (GB): 5.8
+    Results:
+      - Task: 3D Semantic Segmentation
+        Dataset: S3DIS
+        Metrics:
+          mIoU: 66.65
+    Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/paconv/paconv_cuda_ssg_8x8_cosine_200e_s3dis_seg-3d-13class/paconv_cuda_ssg_8x8_cosine_200e_s3dis_seg-3d-13class_20210802_171802-e5ea9bb9.pth

configs/parta2/README.md

@@ -22,8 +22,8 @@ We implement Part-A^2 and provide its results and checkpoints on KITTI dataset.
 
 |                            Backbone                             |  Class  |  Lr schd   | Mem (GB) | Inf time (fps) |  mAP  |                                                                                                                                                                                                   Download                                                                                                                                                                                                   |
 | :-------------------------------------------------------------: | :-----: | :--------: | :------: | :------------: | :---: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| [SECFPN](./PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py) | 3 Class | cyclic 80e |   4.1    |                | 68.33 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class_20210831_022017-454a5344.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class_20210831_022017.log.json) |
-|  [SECFPN](./PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-car.py)   |   Car   | cyclic 80e |   4.0    |                | 79.08 |       [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car_20210831_022017-cb7ff621.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car_20210831_022017.log.json)       |
+| [SECFPN](./parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py) | 3 Class | cyclic 80e |   4.1    |                | 68.33 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class_20210831_022017-454a5344.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class_20210831_022017.log.json) |
+|  [SECFPN](./parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-car.py)   |   Car   | cyclic 80e |   4.0    |                | 79.08 |       [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car_20210831_022017-cb7ff621.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car_20210831_022017.log.json)       |
 
 ## Citation
 

configs/parta2/metafile.yml

@@ -16,9 +16,9 @@ Collections:
       Version: v0.5.0
 
 Models:
-  - Name: hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class
+  - Name: parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class
     In Collection: Part-A^2
-    Config: configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py
+    Config: configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py
     Metadata:
       Training Memory (GB): 4.1
     Results:
@@ -28,9 +28,9 @@ Models:
           mAP: 68.33
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class_20210831_022017-454a5344.pth
 
-  - Name: hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car
+  - Name: parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-car
     In Collection: Part-A^2
-    Config: configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-car.py
+    Config: configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-car.py
     Metadata:
       Training Memory (GB): 4.0
     Results:

configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-car.py → configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-car.py

-_base_ = './PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py'
+_base_ = './parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py'
 
 point_cloud_range = [0, -40, -3, 70.4, 40, 1]  # velodyne coordinates, x, y, z
 

configs/pgd/metafile.yml

@@ -16,7 +16,7 @@ Collections:
       Version: v1.0.0
 
 Models:
-  - Name: pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d
+  - Name: pgd_r101-caffe_fpn_head-gn_4xb3-4x_kitti-mono3d
     In Collection: PGD
     Config: configs/pgd/pgd_r101-caffe_fpn_head-gn_4xb3-4x_kitti-mono3d.py
     Metadata:
@@ -28,7 +28,7 @@ Models:
           mAP: 18.33
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d_20211022_102608-8a97533b.pth
 
-  - Name: pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d
+  - Name: pgd_r101-caffe_fpn_head-gn_16xb2-1x_nus-mono3d
     In Collection: PGD
     Config: configs/pgd/pgd_r101-caffe_fpn_head-gn_16xb2-1x_nus-mono3d.py
     Metadata:
@@ -41,7 +41,7 @@ Models:
           NDS: 39.3
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_20211116_195350-f4b5eec2.pth
 
-  - Name: pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_finetune
+  - Name: pgd_r101-caffe_fpn_head-gn_16xb2-1x_nus-mono3d_finetune
     In Collection: PGD
     Config: configs/pgd/pgd_r101-caffe_fpn_head-gn_16xb2-1x_nus-mono3d_finetune.py
     Metadata:
@@ -54,7 +54,7 @@ Models:
           NDS: 41.1
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_finetune/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_finetune_20211118_093245-fd419681.pth
 
-  - Name: pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d
+  - Name: pgd_r101-caffe_fpn_head-gn_16xb2-2x_nus-mono3d
     In Collection: PGD
     Config: configs/pgd/pgd_r101-caffe_fpn_head-gn_16xb2-2x_nus-mono3d.py
     Metadata:
@@ -67,7 +67,7 @@ Models:
           NDS: 40.9
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_20211112_125314-cb677266.pth
 
-  - Name: pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_finetune
+  - Name: pgd_r101-caffe_fpn_head-gn_16xb2-2x_nus-mono3d_finetune
     In Collection: PGD
     Config: configs/pgd/pgd_r101-caffe_fpn_head-gn_16xb2-2x_nus-mono3d_finetune.py
     Metadata:

configs/point_rcnn/metafile.yml

@@ -16,7 +16,7 @@ Collections:
       Version: v1.0.0
 
 Models:
-  - Name: point-rcnn_8xb2_kitti-3d-3class.py
+  - Name: point-rcnn_8xb2_kitti-3d-3class
     In Collection: PointRCNN
     Config: configs/point_rcnn/point-rcnn_8xb2_kitti-3d-3class.py
     Metadata:

configs/pointnet2/metafile.yml

@@ -15,7 +15,7 @@ Collections:
       Version: v0.14.0
 
 Models:
-  - Name: pointnet2_ssg_2xb16-cosine-200e_scannet-seg-xyz-only.py
+  - Name: pointnet2_ssg_2xb16-cosine-200e_scannet-seg-xyz-only
     In Collection: PointNet++
     Config: configs/pointnet/pointnet2_ssg_2xb16-cosine-200e_scannet-seg-xyz-only.py
     Metadata:
@@ -28,7 +28,7 @@ Models:
           mIoU: 53.91
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_xyz-only_16x2_cosine_200e_scannet_seg-3d-20class/pointnet2_ssg_xyz-only_16x2_cosine_200e_scannet_seg-3d-20class_20210514_143628-4e341a48.pth
 
-  - Name: pointnet2_ssg_2xb16-cosine-200e_scannet-seg.py
+  - Name: pointnet2_ssg_2xb16-cosine-200e_scannet-seg
     In Collection: PointNet++
     Config: configs/pointnet/pointnet2_ssg_2xb16-cosine-200e_scannet-seg.py
     Metadata:
@@ -41,7 +41,7 @@ Models:
           mIoU: 54.44
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class_20210514_143644-ee73704a.pth
 
-  - Name: pointnet2_msg_2xb16-cosine-250e_scannet-seg-xyz-only.py
+  - Name: pointnet2_msg_2xb16-cosine-250e_scannet-seg-xyz-only
     In Collection: PointNet++
     Config: configs/pointnet/pointnet2_msg_2xb16-cosine-250e_scannet-seg-xyz-only.py
     Metadata:
@@ -54,7 +54,7 @@ Models:
           mIoU: 54.26
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_msg_xyz-only_16x2_cosine_250e_scannet_seg-3d-20class/pointnet2_msg_xyz-only_16x2_cosine_250e_scannet_seg-3d-20class_20210514_143838-b4a3cf89.pth
 
-  - Name: pointnet2_msg_2xb16-cosine-250e_scannet-seg.py
+  - Name: pointnet2_msg_2xb16-cosine-250e_scannet-seg
     In Collection: PointNet++
     Config: configs/pointnet/pointnet2_msg_2xb16-cosine-250e_scannet-seg.py
     Metadata:
@@ -67,7 +67,7 @@ Models:
           mIoU: 55.05
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class/pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class_20210514_144009-24477ab1.pth
 
-  - Name: pointnet2_ssg_2xb16-cosine-50e_s3dis-seg.py
+  - Name: pointnet2_ssg_2xb16-cosine-50e_s3dis-seg
     In Collection: PointNet++
     Config: configs/pointnet/pointnet2_ssg_2xb16-cosine-50e_s3dis-seg.py
     Metadata:
@@ -80,7 +80,7 @@ Models:
           mIoU: 56.93
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_16x2_cosine_50e_s3dis_seg-3d-13class/pointnet2_ssg_16x2_cosine_50e_s3dis_seg-3d-13class_20210514_144205-995d0119.pth
 
-  - Name: pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py
+  - Name: pointnet2_msg_2xb16-cosine-80e_s3dis-seg
     In Collection: PointNet++
     Config: configs/pointnet/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py
     Metadata:

configs/pointpillars/metafile.yml

@@ -14,7 +14,7 @@ Collections:
       Version: v0.6.0
 
 Models:
-  - Name: hv_pointpillars_secfpn_6x8_160e_kitti-3d-car
+  - Name: pointpillars_hv_secfpn_8xb6-160e_kitti-3d-car
     In Collection: PointPillars
     Config: configs/pointpillars/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-car.py
     Metadata:
@@ -28,7 +28,7 @@ Models:
           AP: 77.6
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20220331_134606-d42d15ed.pth
 
-  - Name: hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class
+  - Name: pointpillars_hv_secfpn_8xb6-160e_kitti-3d-3class
     In Collection: PointPillars
     Config: configs/pointpillars/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-3class.py
     Metadata:
@@ -42,7 +42,7 @@ Models:
           AP: 64.07
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class_20220301_150306-37dc2420.pth
 
-  - Name: hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d
+  - Name: pointpillars_hv_secfpn_sbn-all_8xb4-2x_nus-3d
     In Collection: PointPillars
     Config: configs/pointpillars/pointpillars_hv_secfpn_sbn-all_8xb4-2x_nus-3d.py
     Metadata:
@@ -57,7 +57,29 @@ Models:
           NDS: 49.1
     Weights: 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
 
-  - Name: hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d
+  - Name: pointpillars_hv_secfpn_sbn-all_8xb4-amp-2x_nus-3d
+    In Collection: PointPillars
+    Config: configs/pointpillars/pointpillars_hv_secfpn_sbn-all_8xb4-amp-2x_nus-3d.py
+    Metadata:
+      Training Techniques:
+        - AdamW
+        - Mixed Precision Training
+      Training Resources: 8x TITAN Xp
+      Architecture:
+        - Hard Voxelization
+      Training Data: nuScenes
+      Training Memory (GB): 8.37
+    Results:
+      - Task: 3D Object Detection
+        Dataset: nuScenes
+        Metrics:
+          mAP: 35.19
+          NDS: 50.27
+    Weights: 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
+    Code:
+      Version: v0.7.0
+
+  - Name: pointpillars_hv_fpn_sbn-all_8xb4-2x_nus-3d
     In Collection: PointPillars
     Config: configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb4-2x_nus-3d.py
     Metadata:
@@ -72,7 +94,29 @@ Models:
           NDS: 53.15
     Weights: 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
 
-  - Name: hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d
+  - Name: pointpillars_hv_fpn_sbn-all_8xb4-amp-2x_nus-3d
+    In Collection: PointPillars
+    Config: configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb4-amp-2x_nus-3d.py
+    Metadata:
+      Training Techniques:
+        - AdamW
+        - Mixed Precision Training
+      Training Resources: 8x TITAN Xp
+      Architecture:
+        - Hard Voxelization
+      Training Data: nuScenes
+      Training Memory (GB): 8.40
+    Results:
+      - Task: 3D Object Detection
+        Dataset: nuScenes
+        Metrics:
+          mAP: 39.26
+          NDS: 53.26
+    Weights: 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
+    Code:
+      Version: v0.7.0
+
+  - Name: pointpillars_hv_secfpn_sbn-all_8xb2-2x_lyft-3d
     In Collection: PointPillars
     Config: configs/pointpillars/pointpillars_hv_secfpn_sbn-all_8xb2-2x_lyft-3d.py
     Metadata:
@@ -87,7 +131,7 @@ Models:
           Public Score: 14.1
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d_20210829_100455-82b81c39.pth
 
-  - Name: hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d
+  - Name: pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d
     In Collection: PointPillars
     Config: configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d.py
     Metadata:
@@ -167,47 +211,3 @@ Models:
           mAPH@L1: 63.3
           mAP@L2: 62.6
           mAPH@L2: 57.6
-
-  - Name: hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d
-    In Collection: PointPillars
-    Config: configs/pointpillars/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d.py
-    Metadata:
-      Training Techniques:
-        - AdamW
-        - Mixed Precision Training
-      Training Resources: 8x TITAN Xp
-      Architecture:
-        - Hard Voxelization
-      Training Data: nuScenes
-      Training Memory (GB): 8.37
-    Results:
-      - Task: 3D Object Detection
-        Dataset: nuScenes
-        Metrics:
-          mAP: 35.19
-          NDS: 50.27
-    Weights: 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
-    Code:
-      Version: v0.7.0
-
-  - Name: hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d
-    In Collection: PointPillars
-    Config: configs/pointpillars/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d.py
-    Metadata:
-      Training Techniques:
-        - AdamW
-        - Mixed Precision Training
-      Training Resources: 8x TITAN Xp
-      Architecture:
-        - Hard Voxelization
-      Training Data: nuScenes
-      Training Memory (GB): 8.40
-    Results:
-      - Task: 3D Object Detection
-        Dataset: nuScenes
-        Metrics:
-          mAP: 39.26
-          NDS: 53.26
-    Weights: 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
-    Code:
-      Version: v0.7.0

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

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