Commit 208611a4 authored by wangtai's avatar wangtai Committed by zhangwenwei
Browse files

Update configs/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_lyft-3d.py,...

Update configs/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_lyft-3d.py, configs/regnet/hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_lyft-3d.py, configs/_base_/datasets/lyft-3d.py files
parent 1185f0b1
...@@ -24,5 +24,6 @@ repos: ...@@ -24,5 +24,6 @@ repos:
- id: end-of-file-fixer - id: end-of-file-fixer
- id: requirements-txt-fixer - id: requirements-txt-fixer
- id: double-quote-string-fixer - id: double-quote-string-fixer
- id: check-merge-conflict
- id: fix-encoding-pragma - id: fix-encoding-pragma
args: ["--remove"] args: ["--remove"]
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-80, -80, -5, 80, 80, 3]
# For Lyft we usually do 9-class detection
class_names = [
'car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle', 'motorcycle',
'bicycle', 'pedestrian', 'animal'
]
dataset_type = 'LyftDataset'
data_root = 'data/lyft/'
# Input modality for Lyft dataset, this is consistent with the submission
# format which requires the information in input_modality.
input_modality = dict(
use_lidar=True,
use_camera=False,
use_radar=False,
use_map=False,
use_external=False)
file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
# for more details.
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/lyft/': 's3://lyft/lyft/',
# 'data/lyft/': 's3://lyft/lyft/'
# }))
train_pipeline = [
dict(
type='LoadPointsFromFile',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.3925, 0.3925],
scale_ratio_range=[0.95, 1.05],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'lyft_infos_train.pkl',
pipeline=train_pipeline,
classes=class_names,
modality=input_modality,
test_mode=False),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'lyft_infos_val.pkl',
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
test_mode=True),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'lyft_infos_test.pkl',
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
test_mode=True))
# For Lyft dataset, we usually evaluate the model at the end of training.
# Since the models are trained by 24 epochs by default, we set evaluation
# interval to be 24. Please change the interval accordingly if you do not
# use a default schedule.
evaluation = dict(interval=24)
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-100, -100, -5, 100, 100, 3]
# For Lyft we usually do 9-class detection
class_names = [
'car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle', 'motorcycle',
'bicycle', 'pedestrian', 'animal'
]
dataset_type = 'LyftDataset'
data_root = 'data/lyft/'
# Input modality for Lyft dataset, this is consistent with the submission
# format which requires the information in input_modality.
input_modality = dict(
use_lidar=True,
use_camera=False,
use_radar=False,
use_map=False,
use_external=False)
file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
# for more details.
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/lyft/': 's3://lyft/lyft/',
# 'data/lyft/': 's3://lyft/lyft/'
# }))
train_pipeline = [
dict(
type='LoadPointsFromFile',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.3925, 0.3925],
scale_ratio_range=[0.95, 1.05],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'lyft_infos_train.pkl',
pipeline=train_pipeline,
classes=class_names,
modality=input_modality,
test_mode=False),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'lyft_infos_val.pkl',
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
test_mode=True),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'lyft_infos_test.pkl',
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
test_mode=True))
# For Lyft dataset, we usually evaluate the model at the end of training.
# Since the models are trained by 24 epochs by default, we set evaluation
# interval to be 24. Please change the interval accordingly if you do not
# use a default schedule.
evaluation = dict(interval=24)
_base_ = './hv_pointpillars_fpn_nus.py'
# model settings (based on nuScenes model settings)
# Voxel size for voxel encoder
# Usually voxel size is changed consistently with the point cloud range
# If point cloud range is modified, do remember to change all related
# keys in the config.
model = dict(
pts_voxel_layer=dict(
max_num_points=20,
point_cloud_range=[-80, -80, -5, 80, 80, 3],
max_voxels=(60000, 60000)),
pts_voxel_encoder=dict(
feat_channels=[64], point_cloud_range=[-80, -80, -5, 80, 80, 3]),
pts_middle_encoder=dict(output_shape=[640, 640]),
pts_bbox_head=dict(
num_classes=9,
anchor_generator=dict(
ranges=[[-80, -80, -1.8, 80, 80, -1.8]], custom_values=[]),
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', code_size=7)))
# model training settings (based on nuScenes model settings)
train_cfg = dict(pts=dict(code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]))
_base_ = './hv_pointpillars_fpn_nus.py'
# model settings (based on nuScenes model settings)
# Voxel size for voxel encoder
# Usually voxel size is changed consistently with the point cloud range
# If point cloud range is modified, do remember to change all related
# keys in the config.
model = dict(
pts_voxel_layer=dict(
max_num_points=20,
point_cloud_range=[-100, -100, -5, 100, 100, 3],
max_voxels=(60000, 60000)),
pts_voxel_encoder=dict(
feat_channels=[64], point_cloud_range=[-100, -100, -5, 100, 100, 3]),
pts_middle_encoder=dict(output_shape=[800, 800]),
pts_bbox_head=dict(
num_classes=9,
anchor_generator=dict(
ranges=[[-100, -100, -1.8, 100, 100, -1.8]], custom_values=[]),
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', code_size=7)))
# model training settings (based on nuScenes model settings)
train_cfg = dict(pts=dict(code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]))
...@@ -25,7 +25,7 @@ The hyperparameters are specifically tuned according to the original paper. ...@@ -25,7 +25,7 @@ The hyperparameters are specifically tuned according to the original paper.
```python ```python
_base_ = [ _base_ = [
'../_base_/models/pointpillars_second_fpn.py', '../_base_/models/hv_pointpillars_fpn_lyft.py',
'../_base_/datasets/nus-3d.py', '../_base_/schedules/schedule_2x.py', '../_base_/datasets/nus-3d.py', '../_base_/schedules/schedule_2x.py',
'../_base_/default_runtime.py' '../_base_/default_runtime.py'
] ]
......
_base_ = [ _base_ = [
'../_base_/models/pointpillars_second_fpn.py', '../_base_/models/hv_pointpillars_fpn_lyft.py',
'../_base_/datasets/nus-3d.py', '../_base_/schedules/schedule_2x.py', '../_base_/datasets/nus-3d.py', '../_base_/schedules/schedule_2x.py',
'../_base_/default_runtime.py' '../_base_/default_runtime.py'
] ]
......
...@@ -30,3 +30,10 @@ We implement PointPillars and provide the results and checkpoints on KITTI and n ...@@ -30,3 +30,10 @@ We implement PointPillars and provide the results and checkpoints on KITTI and n
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | | :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
|[SECFPN](./hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py)|2x|16.4||35.17|49.7|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725-0817d270.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725.log.json)| |[SECFPN](./hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py)|2x|16.4||35.17|49.7|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725-0817d270.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725.log.json)|
|[FPN](./hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|16.4||40.0|53.3|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.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) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.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.log.json)| |[FPN](./hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|16.4||40.0|53.3|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.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) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.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.log.json)|
### Lyft
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | Private Score | Public Score | Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
|[SECFPN](./hv_pointpillars_secfpn_sbn-all_4x8_2x_lyft-3d.py)|2x|||13.4|13.4||
|[FPN](./hv_pointpillars_fpn_sbn-all_4x8_2x_lyft-3d.py)|2x|||14.0|14.2||
_base_ = [
'../_base_/models/hv_pointpillars_fpn_lyft.py',
'../_base_/datasets/lyft-3d.py', '../_base_/schedules/schedule_2x.py',
'../_base_/default_runtime.py'
]
_base_ = [ _base_ = [
'../_base_/models/pointpillars_second_fpn.py', '../_base_/models/hv_pointpillars_fpn_nus.py',
'../_base_/datasets/nus-3d.py', '../_base_/schedules/schedule_2x.py', '../_base_/datasets/nus-3d.py', '../_base_/schedules/schedule_2x.py',
'../_base_/default_runtime.py' '../_base_/default_runtime.py'
] ]
_base_ = [
'../_base_/models/hv_pointpillars_fpn_range100_lyft.py',
'../_base_/datasets/range100_lyft-3d.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
_base_ = [ _base_ = [
'../_base_/models/hv_pointpillars_secfpn.py', '../_base_/models/hv_pointpillars_secfpn_kitti.py',
'../_base_/datasets/kitti-3d-3class.py', '../_base_/datasets/kitti-3d-3class.py',
'../_base_/schedules/cyclic_40e.py', '../_base_/default_runtime.py' '../_base_/schedules/cyclic_40e.py', '../_base_/default_runtime.py'
] ]
......
_base_ = [
'../_base_/models/hv_pointpillars_fpn_lyft.py',
'../_base_/datasets/lyft-3d.py',
'../_base_/schedules/schedule_2x.py',
'../_base_/default_runtime.py',
]
# model settings
model = dict(
pts_neck=dict(
_delete_=True,
type='SECONDFPN',
norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
in_channels=[64, 128, 256],
upsample_strides=[1, 2, 4],
out_channels=[128, 128, 128]),
pts_bbox_head=dict(
in_channels=384,
feat_channels=384,
anchor_generator=dict(
_delete_=True,
type='AlignedAnchor3DRangeGenerator',
ranges=[[-80, -80, -1.0715024, -80, -80, -1.0715024],
[-80, -80, -0.3033737, -80, -80, -0.3033737],
[-80, -80, -0.3519405, -80, -80, -0.3519405],
[-80, -80, -0.8871424, -80, -80, -0.8871424],
[-80, -80, -0.6276341, -80, -80, -0.6276341],
[-80, -80, -1.3220503, -80, -80, -1.3220503],
[-80, -80, -1.0709302, -80, -80, -1.0709302],
[-80, -80, -0.9122268, -80, -80, -0.9122268],
[-80, -80, -1.8012227, -80, -80, -1.8012227]],
sizes=[
[1.92, 4.75, 1.71], # car
[2.84, 10.24, 3.44], # truck
[2.92, 12.70, 3.42], # bus
[2.42, 6.52, 2.34], # emergency vehicle
[2.75, 8.17, 3.20], # other vehicle
[0.96, 2.35, 1.59], # motorcycle
[0.63, 1.76, 1.44], # bicycle
[0.76, 0.80, 1.76], # pedestrian
[0.35, 0.73, 0.50] # animal
],
rotations=[0, 1.57],
reshape_out=True)))
_base_ = [ _base_ = [
'../_base_/models/pointpillars_second_fpn.py', '../_base_/models/hv_pointpillars_fpn_nus.py',
'../_base_/datasets/nus-3d.py', '../_base_/datasets/nus-3d.py',
'../_base_/schedules/schedule_2x.py', '../_base_/schedules/schedule_2x.py',
'../_base_/default_runtime.py', '../_base_/default_runtime.py',
......
_base_ = [
'../_base_/models/hv_pointpillars_fpn_range100_lyft.py',
'../_base_/datasets/range100_lyft-3d.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
pts_neck=dict(
_delete_=True,
type='SECONDFPN',
norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
in_channels=[64, 128, 256],
upsample_strides=[1, 2, 4],
out_channels=[128, 128, 128]),
pts_bbox_head=dict(
in_channels=384,
feat_channels=384,
anchor_generator=dict(
_delete_=True,
type='AlignedAnchor3DRangeGenerator',
ranges=[[-100, -100, -1.0715024, -100, -100, -1.0715024],
[-100, -100, -0.3033737, -100, -100, -0.3033737],
[-100, -100, -0.3519405, -100, -100, -0.3519405],
[-100, -100, -0.8871424, -100, -100, -0.8871424],
[-100, -100, -0.6276341, -100, -100, -0.6276341],
[-100, -100, -1.3220503, -100, -100, -1.3220503],
[-100, -100, -1.0709302, -100, -100, -1.0709302],
[-100, -100, -0.9122268, -100, -100, -0.9122268],
[-100, -100, -1.8012227, -100, -100, -1.8012227]],
sizes=[
[1.92, 4.75, 1.71], # car
[2.84, 10.24, 3.44], # truck
[2.92, 12.70, 3.42], # bus
[2.42, 6.52, 2.34], # emergency vehicle
[2.75, 8.17, 3.20], # other vehicle
[0.96, 2.35, 1.59], # motorcycle
[0.63, 1.76, 1.44], # bicycle
[0.76, 0.80, 1.76], # pedestrian
[0.35, 0.73, 0.50] # animal
],
rotations=[0, 1.57],
reshape_out=True)))
...@@ -47,7 +47,7 @@ For other pre-trained models or self-implemented regnet models, the users are re ...@@ -47,7 +47,7 @@ For other pre-trained models or self-implemented regnet models, the users are re
## Results ## Results
### PointPillars ### nuScenes
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP |NDS| Download | | Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP |NDS| Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | | :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
...@@ -56,3 +56,12 @@ For other pre-trained models or self-implemented regnet models, the users are re ...@@ -56,3 +56,12 @@ For other pre-trained models or self-implemented regnet models, the users are re
|[FPN](../pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|17.1||40.0|53.3|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.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) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.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.log.json)| |[FPN](../pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|17.1||40.0|53.3|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.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) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.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.log.json)|
|[RegNetX-400MF-FPN](./hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|17.3||44.8|56.4|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d_20200620_230239-c694dce7.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d_20200620_230239.log.json)| |[RegNetX-400MF-FPN](./hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|17.3||44.8|56.4|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d_20200620_230239-c694dce7.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d_20200620_230239.log.json)|
|[RegNetX-1.6gF-FPN](./hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|24.0||48.2|59.3|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d_20200629_050311-dcd4e090.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d_20200629_050311.log.json)| |[RegNetX-1.6gF-FPN](./hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|24.0||48.2|59.3|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d_20200629_050311-dcd4e090.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d_20200629_050311.log.json)|
### Lyft
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | Private Score | Public Score | Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
|[SECFPN](../pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_lyft-3d.py)|2x|||13.4|13.4||
|[RegNetX-400MF-SECFPN](./hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_lyft-3d.py)| 2x ||||||
|[FPN](../pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_lyft-3d.py)|2x|||14.0|14.2||
|[RegNetX-400MF-FPN](./hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_lyft-3d.py)|2x|||15.5|15.6||
_base_ = [ _base_ = [
'../_base_/models/pointpillars_second_fpn.py', '../_base_/models/hv_pointpillars_fpn_lyft.py',
'../_base_/datasets/nus-3d.py', '../_base_/datasets/nus-3d.py',
'../_base_/schedules/schedule_2x.py', '../_base_/schedules/schedule_2x.py',
'../_base_/default_runtime.py', '../_base_/default_runtime.py',
......
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