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Unverified Commit d7067e44 authored by Wenwei Zhang's avatar Wenwei Zhang Committed by GitHub
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Bump version to v1.1.0rc2 

Bump to v1.1.0rc2
parents 28fe73d2 fb0e57e5
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configs/centerpoint/README.md
View file @ d7067e44
......@@ -91,12 +91,8 @@ test_pipeline = [
dict(type='RandomFlip3D', sync_2d=False),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
data = dict(
......@@ -109,23 +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/centerpoint_pillar02_second_secfpn_8xb4-cyclic-20e_nus-3d.py
View file @ d7067e44
......@@ -7,6 +7,9 @@ _base_ = [
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
# Using calibration info convert the Lidar-coordinate point cloud range to the
# ego-coordinate point cloud range could bring a little promotion in nuScenes.
# point_cloud_range = [-51.2, -52, -5.0, 51.2, 50.4, 3.0]
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
......@@ -126,7 +129,7 @@ train_dataloader = dict(
data_root=data_root,
ann_file='nuscenes_infos_train.pkl',
pipeline=train_pipeline,
metainfo=dict(CLASSES=class_names),
metainfo=dict(classes=class_names),
test_mode=False,
data_prefix=data_prefix,
use_valid_flag=True,
......@@ -134,8 +137,8 @@ train_dataloader = dict(
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='LiDAR')))
test_dataloader = dict(
dataset=dict(pipeline=test_pipeline, metainfo=dict(CLASSES=class_names)))
dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=class_names)))
val_dataloader = dict(
dataset=dict(pipeline=test_pipeline, metainfo=dict(CLASSES=class_names)))
dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=class_names)))
train_cfg = dict(val_interval=20)
configs/centerpoint/centerpoint_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d.py
View file @ d7067e44
......@@ -4,6 +4,9 @@ _base_ = ['./centerpoint_voxel01_second_secfpn_8xb4-cyclic-20e_nus-3d.py']
# cloud range accordingly
voxel_size = [0.075, 0.075, 0.2]
point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
# Using calibration info convert the Lidar-coordinate point cloud range to the
# ego-coordinate point cloud range could bring a little promotion in nuScenes.
# point_cloud_range = [-54, -54.8, -5.0, 54, 53.2, 3.0]
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
......@@ -89,7 +92,6 @@ train_pipeline = [
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectNameFilter', classes=class_names),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
......@@ -122,8 +124,8 @@ test_pipeline = [
train_dataloader = dict(
dataset=dict(
dataset=dict(
pipeline=train_pipeline, metainfo=dict(CLASSES=class_names))))
pipeline=train_pipeline, metainfo=dict(classes=class_names))))
test_dataloader = dict(
dataset=dict(pipeline=test_pipeline, metainfo=dict(CLASSES=class_names)))
dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=class_names)))
val_dataloader = dict(
dataset=dict(pipeline=test_pipeline, metainfo=dict(CLASSES=class_names)))
dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=class_names)))
configs/centerpoint/centerpoint_voxel0075_second_secfpn_head-dcn-circlenms_8xb4-flip-tta-cyclic-20e_nus-3d.py
View file @ d7067e44
......@@ -2,6 +2,9 @@ _base_ = './centerpoint_voxel0075_second_secfpn_' \
'head-dcn-circlenms_8xb4_cyclic-20e_nus-3d.py'
point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
# Using calibration info convert the Lidar-coordinate point cloud range to the
# ego-coordinate point cloud range could bring a little promotion in nuScenes.
# point_cloud_range = [-54, -54.8, -5.0, 54, 53.2, 3.0]
file_client_args = dict(backend='disk')
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
......
configs/centerpoint/centerpoint_voxel0075_second_secfpn_head-dcn_8xb4-flip-tta-cyclic-20e_nus-3d.py
View file @ d7067e44
......@@ -2,6 +2,9 @@ _base_ = './centerpoint_voxel0075_second_secfpn' \
'_head-dcn_8xb4-cyclic-20e_nus-3d.py'
point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
# Using calibration info convert the Lidar-coordinate point cloud range to the
# ego-coordinate point cloud range could bring a little promotion in nuScenes.
# point_cloud_range = [-54, -54.8, -5.0, 54, 53.2, 3.0]
file_client_args = dict(backend='disk')
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
......
configs/centerpoint/centerpoint_voxel0075_second_secfpn_head-dcn_8xb4-tta-cyclic-20e_nus-3d.py
View file @ d7067e44
......@@ -4,6 +4,9 @@ _base_ = './centerpoint_voxel0075_second_secfpn' \
model = dict(test_cfg=dict(pts=dict(use_rotate_nms=True, max_num=500)))
point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
# Using calibration info convert the Lidar-coordinate point cloud range to the
# ego-coordinate point cloud range could bring a little promotion in nuScenes.
# point_cloud_range = [-54, -54.8, -5.0, 54, 53.2, 3.0]
file_client_args = dict(backend='disk')
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
......
configs/centerpoint/centerpoint_voxel01_second_secfpn_8xb4-cyclic-20e_nus-3d.py
View file @ d7067e44
......@@ -7,6 +7,9 @@ _base_ = [
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
# Using calibration info convert the Lidar-coordinate point cloud range to the
# ego-coordinate point cloud range could bring a little promotion in nuScenes.
# point_cloud_range = [-51.2, -52, -5.0, 51.2, 50.4, 3.0]
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
......@@ -127,7 +130,7 @@ train_dataloader = dict(
data_root=data_root,
ann_file='nuscenes_infos_train.pkl',
pipeline=train_pipeline,
metainfo=dict(CLASSES=class_names),
metainfo=dict(classes=class_names),
test_mode=False,
data_prefix=data_prefix,
use_valid_flag=True,
......@@ -135,8 +138,8 @@ train_dataloader = dict(
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='LiDAR')))
test_dataloader = dict(
dataset=dict(pipeline=test_pipeline, metainfo=dict(CLASSES=class_names)))
dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=class_names)))
val_dataloader = dict(
dataset=dict(pipeline=test_pipeline, metainfo=dict(CLASSES=class_names)))
dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=class_names)))
train_cfg = dict(val_interval=20)
configs/centerpoint/metafile.yml
View file @ d7067e44
......@@ -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/dgcnn/README.md
View file @ d7067e44
......@@ -23,14 +23,14 @@ We implement DGCNN and provide the results and checkpoints on S3DIS dataset.
### S3DIS
| Method | Split | Lr schd | Mem (GB) | Inf time (fps) | mIoU (Val set) | Download |
| :---------------------------------------------: | :----: | :---------: | :------: | :------------: | :------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg.py) | Area_1 | cosine 100e | 13.1 | | 68.33 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area1/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_000734-39658f14.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area1/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_000734.log.json) |
| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg.py) | Area_2 | cosine 100e | 13.1 | | 40.68 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area2/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_144648-aea9ecb6.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area2/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_144648.log.json) |
| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg.py) | Area_3 | cosine 100e | 13.1 | | 69.38 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area3/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210801_154629-2ff50ee0.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area3/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210801_154629.log.json) |
| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg.py) | Area_4 | cosine 100e | 13.1 | | 50.07 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area4/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_073551-dffab9cd.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area4/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_073551.log.json) |
| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg.py) | Area_5 | cosine 100e | 13.1 | | 50.59 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area5/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210730_235824-f277e0c5.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area5/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210730_235824.log.json) |
| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg.py) | Area_6 | cosine 100e | 13.1 | | 77.94 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area6/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_154317-e3511b32.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area6/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_154317.log.json) |
| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg.py) | 6-fold | | | | 59.43 | |
| :--------------------------------------------------------: | :----: | :---------: | :------: | :------------: | :------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg_test-area1.py) | Area_1 | cosine 100e | 13.1 | | 68.33 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area1/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_000734-39658f14.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area1/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_000734.log.json) |
| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg_test-area2.py) | Area_2 | cosine 100e | 13.1 | | 40.68 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area2/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_144648-aea9ecb6.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area2/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_144648.log.json) |
| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg_test-area3.py) | Area_3 | cosine 100e | 13.1 | | 69.38 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area3/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210801_154629-2ff50ee0.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area3/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210801_154629.log.json) |
| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg_test-area4.py) | Area_4 | cosine 100e | 13.1 | | 50.07 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area4/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_073551-dffab9cd.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area4/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_073551.log.json) |
| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py) | Area_5 | cosine 100e | 13.1 | | 50.59 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area5/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210730_235824-f277e0c5.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area5/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210730_235824.log.json) |
| [DGCNN](./dgcnn_4xb32-cosine-100e_s3dis-seg_test-area6.py) | Area_6 | cosine 100e | 13.1 | | 77.94 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area6/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_154317-e3511b32.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area6/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_154317.log.json) |
| DGCNN | 6-fold | | | | 59.43 | |
**Notes:**
......
configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area1.py 0 → 100644
View file @ d7067e44
_base_ = './dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py'
# data settings
train_area = [2, 3, 4, 5, 6]
test_area = 1
train_dataloader = dict(
batch_size=32,
dataset=dict(
ann_files=[f's3dis_infos_Area_{i}.pkl' for i in train_area],
scene_idxs=[
f'seg_info/Area_{i}_resampled_scene_idxs.npy' for i in train_area
]))
test_dataloader = dict(
dataset=dict(
ann_files=f's3dis_infos_Area_{test_area}.pkl',
scene_idxs=f'seg_info/Area_{test_area}_resampled_scene_idxs.npy'))
val_dataloader = test_dataloader
configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area2.py 0 → 100644
View file @ d7067e44
_base_ = './dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py'
# data settings
train_area = [1, 3, 4, 5, 6]
test_area = 2
train_dataloader = dict(
batch_size=32,
dataset=dict(
ann_files=[f's3dis_infos_Area_{i}.pkl' for i in train_area],
scene_idxs=[
f'seg_info/Area_{i}_resampled_scene_idxs.npy' for i in train_area
]))
test_dataloader = dict(
dataset=dict(
ann_files=f's3dis_infos_Area_{test_area}.pkl',
scene_idxs=f'seg_info/Area_{test_area}_resampled_scene_idxs.npy'))
val_dataloader = test_dataloader
configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area3.py 0 → 100644
View file @ d7067e44
_base_ = './dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py'
# data settings
train_area = [1, 2, 4, 5, 6]
test_area = 3
train_dataloader = dict(
batch_size=32,
dataset=dict(
ann_files=[f's3dis_infos_Area_{i}.pkl' for i in train_area],
scene_idxs=[
f'seg_info/Area_{i}_resampled_scene_idxs.npy' for i in train_area
]))
test_dataloader = dict(
dataset=dict(
ann_files=f's3dis_infos_Area_{test_area}.pkl',
scene_idxs=f'seg_info/Area_{test_area}_resampled_scene_idxs.npy'))
val_dataloader = test_dataloader
configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area4.py 0 → 100644
View file @ d7067e44
_base_ = './dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py'
# data settings
train_area = [1, 2, 3, 5, 6]
test_area = 4
train_dataloader = dict(
batch_size=32,
dataset=dict(
ann_files=[f's3dis_infos_Area_{i}.pkl' for i in train_area],
scene_idxs=[
f'seg_info/Area_{i}_resampled_scene_idxs.npy' for i in train_area
]))
test_dataloader = dict(
dataset=dict(
ann_files=f's3dis_infos_Area_{test_area}.pkl',
scene_idxs=f'seg_info/Area_{test_area}_resampled_scene_idxs.npy'))
val_dataloader = test_dataloader
configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg.py → configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py
View file @ d7067e44
......@@ -16,6 +16,6 @@ model = dict(
use_normalized_coord=True,
batch_size=24))
default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=2), )
default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=2))
train_dataloader = dict(batch_size=32)
val_cfg = dict(interval=2)
train_cfg = dict(val_interval=2)
configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area6.py 0 → 100644
View file @ d7067e44
_base_ = './dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py'
# data settings
train_area = [1, 2, 3, 4, 5]
test_area = 6
train_dataloader = dict(
batch_size=32,
dataset=dict(
ann_files=[f's3dis_infos_Area_{i}.pkl' for i in train_area],
scene_idxs=[
f'seg_info/Area_{i}_resampled_scene_idxs.npy' for i in train_area
]))
test_dataloader = dict(
dataset=dict(
ann_files=f's3dis_infos_Area_{test_area}.pkl',
scene_idxs=f'seg_info/Area_{test_area}_resampled_scene_idxs.npy'))
val_dataloader = test_dataloader
configs/dgcnn/metafile.yml
View file @ d7067e44
......@@ -10,15 +10,80 @@ Collections:
README: configs/dgcnn/README.md
Models:
- Name: dgcnn_4xb32-cosine-100e_s3dis-seg.py
- Name: dgcnn_4xb32-cosine-100e_s3dis-seg_test-area1.py
In Collection: DGCNN
Config: configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg.py
Config: configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area1.py
Metadata:
Training Data: S3DIS
Training Memory (GB): 13.3
Results:
- Task: 3D Semantic Segmentation
Dataset: S3DIS
Dataset: S3DIS Area1
Metrics:
mIoU: 68.33
Weights: https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area1/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_000734-39658f14.pth
- Name: dgcnn_4xb32-cosine-100e_s3dis-seg_test-area2.py
In Collection: DGCNN
Config: configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area2.py
Metadata:
Training Data: S3DIS
Training Memory (GB): 13.3
Results:
- Task: 3D Semantic Segmentation
Dataset: S3DIS Area2
Metrics:
mIoU: 40.68
Weights: https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area2/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_144648-aea9ecb6.pth
- Name: dgcnn_4xb32-cosine-100e_s3dis-seg_test-area3.py
In Collection: DGCNN
Config: configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area3.py
Metadata:
Training Data: S3DIS
Training Memory (GB): 13.3
Results:
- Task: 3D Semantic Segmentation
Dataset: S3DIS Area3
Metrics:
mIoU: 69.38
Weights: https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area3/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210801_154629-2ff50ee0.pth
- Name: dgcnn_4xb32-cosine-100e_s3dis-seg_test-area4.py
In Collection: DGCNN
Config: configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area4.py
Metadata:
Training Data: S3DIS
Training Memory (GB): 13.3
Results:
- Task: 3D Semantic Segmentation
Dataset: S3DIS Area4
Metrics:
mIoU: 50.07
Weights: https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area4/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_073551-dffab9cd.pth
- Name: dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py
In Collection: DGCNN
Config: configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py
Metadata:
Training Data: S3DIS
Training Memory (GB): 13.3
Results:
- Task: 3D Semantic Segmentation
Dataset: S3DIS Area5
Metrics:
mIoU: 50.59
Weights: https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area5/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210730_235824-f277e0c5.pth
- Name: dgcnn_4xb32-cosine-100e_s3dis-seg_test-area6.py
In Collection: DGCNN
Config: configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area6.py
Metadata:
Training Data: S3DIS
Training Memory (GB): 13.3
Results:
- Task: 3D Semantic Segmentation
Dataset: S3DIS Area6
Metrics:
mIoU: 77.94
Weights: https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area6/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_154317-e3511b32.pth
configs/fcaf3d/README.md 0 → 100644
View file @ d7067e44
# FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection
> [FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection](https://arxiv.org/abs/2112.00322)
<!-- [ALGORITHM] -->
## Abstract
Recently, promising applications in robotics and augmented reality have attracted considerable attention to 3D object detection from point clouds. In this paper, we present FCAF3D --- a first-in-class fully convolutional anchor-free indoor 3D object detection method. It is a simple yet effective method that uses a voxel representation of a point cloud and processes voxels with sparse convolutions. FCAF3D can handle large-scale scenes with minimal runtime through a single fully convolutional feed-forward pass. Existing 3D object detection methods make prior assumptions on the geometry of objects, and we argue that it limits their generalization ability. To eliminate prior assumptions, we propose a novel parametrization of oriented bounding boxes that allows obtaining better results in a purely data-driven way. The proposed method achieves state-of-the-art 3D object detection results in terms of mAP@0.5 on ScanNet V2 (+4.5), SUN RGB-D (+3.5), and S3DIS (+20.5) datasets.
<div align="center">
<img src="https://user-images.githubusercontent.com/6030962/182842796-98c10576-d39c-4c2b-a15a-a04c9870919c.png" width="800"/>
</div>
## Introduction
We implement FCAF3D and provide the result and checkpoints on the ScanNet and SUN RGB-D dataset.
## Results and models
### ScanNet
| Backbone | Mem (GB) | Inf time (fps) | AP@0.25 | AP@0.5 | Download |
| :------------------------------------------------: | :------: | :------------: | :----------: | :----------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [MinkResNet34](./fcaf3d_8x2_scannet-3d-18class.py) | 10.5 | 8.0 | 69.7(70.7\*) | 55.2(56.0\*) | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_scannet-3d-18class/fcaf3d_8x2_scannet-3d-18class_20220805_084956.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_scannet-3d-18class/fcaf3d_8x2_scannet-3d-18class_20220805_084956.log.json) |
### SUN RGB-D
| Backbone | Mem (GB) | Inf time (fps) | AP@0.25 | AP@0.5 | Download |
| :------------------------------------------------: | :------: | :------------: | :----------: | :----------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [MinkResNet34](./fcaf3d_8x2_sunrgbd-3d-10class.py) | 6.3 | 15.6 | 63.8(63.8\*) | 47.3(48.2\*) | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_sunrgbd-3d-10class/fcaf3d_8x2_sunrgbd-3d-10class_20220805_165017.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_sunrgbd-3d-10class/fcaf3d_8x2_sunrgbd-3d-10class_20220805_165017.log.json) |
### S3DIS
| Backbone | Mem (GB) | Inf time (fps) | AP@0.25 | AP@0.5 | Download |
| :----------------------------------------------: | :------: | :------------: | :----------: | :----------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [MinkResNet34](./fcaf3d_2xb8_s3dis-3d-5class.py) | 23.5 | 4.2 | 67.4(64.9\*) | 45.7(43.8\*) | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_s3dis-3d-5class/fcaf3d_8x2_s3dis-3d-5class_20220805_121957.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_s3dis-3d-5class/fcaf3d_8x2_s3dis-3d-5class_20220805_121957.log.json) |
**Note**
- We report the results across 5 train runs followed by 5 test runs. * means the results reported in the paper.
- Inference time is given for a single NVidia GeForce GTX 1080 Ti GPU. All models are trained on 2 GPUs.
## Citation
```latex
@inproceedings{rukhovich2022fcaf3d,
title={FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection},
author={Danila Rukhovich, Anna Vorontsova, Anton Konushin},
booktitle={European conference on computer vision},
year={2022}
}
```
configs/fcaf3d/fcaf3d_2xb8_s3dis-3d-5class.py 0 → 100644
View file @ d7067e44
_base_ = [
'../_base_/models/fcaf3d.py', '../_base_/default_runtime.py',
'../_base_/datasets/s3dis-3d.py'
]
model = dict(bbox_head=dict(num_classes=5))
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=0.001, weight_decay=0.0001),
clip_grad=dict(max_norm=10, norm_type=2))
# learning rate
param_scheduler = dict(
type='MultiStepLR',
begin=0,
end=12,
by_epoch=True,
milestones=[8, 11],
gamma=0.1)
custom_hooks = [dict(type='EmptyCacheHook', after_iter=True)]
# training schedule for 1x
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=12)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
configs/fcaf3d/fcaf3d_2xb8_scannet-3d-18class.py 0 → 100644
View file @ d7067e44
_base_ = [
'../_base_/models/fcaf3d.py', '../_base_/default_runtime.py',
'../_base_/datasets/scannet-3d.py'
]
n_points = 100000
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(type='LoadAnnotations3D'),
dict(type='GlobalAlignment', rotation_axis=2),
dict(type='PointSample', num_points=n_points),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.087266, 0.087266],
scale_ratio_range=[.9, 1.1],
translation_std=[.1, .1, .1],
shift_height=False),
dict(type='NormalizePointsColor', color_mean=None),
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(type='GlobalAlignment', rotation_axis=2),
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',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(type='PointSample', num_points=n_points),
dict(type='NormalizePointsColor', color_mean=None),
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
dataset=dict(
type='RepeatDataset',
times=10,
dataset=dict(pipeline=train_pipeline, filter_empty_gt=True)))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=0.001, weight_decay=0.0001),
clip_grad=dict(max_norm=10, norm_type=2))
# learning rate
param_scheduler = dict(
type='MultiStepLR',
begin=0,
end=12,
by_epoch=True,
milestones=[8, 11],
gamma=0.1)
custom_hooks = [dict(type='EmptyCacheHook', after_iter=True)]
# training schedule for 1x
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=12)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
configs/fcaf3d/fcaf3d_2xb8_sunrgbd-3d-10class.py 0 → 100644
View file @ d7067e44
_base_ = [
'../_base_/models/fcaf3d.py', '../_base_/default_runtime.py',
'../_base_/datasets/sunrgbd-3d.py'
]
n_points = 100000
model = dict(
bbox_head=dict(
num_classes=10,
num_reg_outs=8,
bbox_loss=dict(type='RotatedIoU3DLoss')))
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(type='LoadAnnotations3D'),
dict(type='PointSample', num_points=n_points),
dict(type='RandomFlip3D', sync_2d=False, flip_ratio_bev_horizontal=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.523599, 0.523599],
scale_ratio_range=[0.85, 1.15],
translation_std=[.1, .1, .1],
shift_height=False),
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
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',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(type='PointSample', num_points=n_points)
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
batch_size=8,
dataset=dict(
type='RepeatDataset',
times=3,
dataset=dict(pipeline=train_pipeline, filter_empty_gt=True)))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=0.001, weight_decay=0.0001),
clip_grad=dict(max_norm=10, norm_type=2))
# learning rate
param_scheduler = dict(
type='MultiStepLR',
begin=0,
end=12,
by_epoch=True,
milestones=[8, 11],
gamma=0.1)
custom_hooks = [dict(type='EmptyCacheHook', after_iter=True)]
# training schedule for 1x
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=12)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
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