[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/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.)