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mmdetection3d/configs/pointnet2/pointnet2_ssg_2xb16-cosine-200e_scannet-seg.py 0 → 100644
View file @ 7aa442d5
_base_ = [
'../_base_/datasets/scannet-seg.py', '../_base_/models/pointnet2_ssg.py',
'../_base_/schedules/seg-cosine-200e.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
decode_head=dict(
num_classes=20,
ignore_index=20,
# `class_weight` is generated in data pre-processing, saved in
# `data/scannet/seg_info/train_label_weight.npy`
# you can copy paste the values here, or input the file path as
# `class_weight=data/scannet/seg_info/train_label_weight.npy`
loss_decode=dict(class_weight=[
2.389689, 2.7215734, 4.5944676, 4.8543367, 4.096086, 4.907941,
4.690836, 4.512031, 4.623311, 4.9242644, 5.358117, 5.360071,
5.019636, 4.967126, 5.3502126, 5.4023647, 5.4027233, 5.4169416,
5.3954206, 4.6971426
])),
test_cfg=dict(
num_points=8192,
block_size=1.5,
sample_rate=0.5,
use_normalized_coord=False,
batch_size=24))
# data settings
train_dataloader = dict(batch_size=16)
# runtime settings
default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=5))
train_cfg = dict(val_interval=5)
mmdetection3d/configs/pointnet2/pointnet2_ssg_2xb16-cosine-50e_s3dis-seg.py 0 → 100644
View file @ 7aa442d5
_base_ = [
'../_base_/datasets/s3dis-seg.py', '../_base_/models/pointnet2_ssg.py',
'../_base_/schedules/seg-cosine-50e.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
backbone=dict(in_channels=9), # [xyz, rgb, normalized_xyz]
decode_head=dict(
num_classes=13, ignore_index=13,
loss_decode=dict(class_weight=None)), # S3DIS doesn't use class_weight
test_cfg=dict(
num_points=4096,
block_size=1.0,
sample_rate=0.5,
use_normalized_coord=True,
batch_size=24))
# data settings
train_dataloader = dict(batch_size=16)
# runtime settings
default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=2))
train_cfg = dict(val_interval=2)
mmdetection3d/configs/pointpillars/README.md 0 → 100644
View file @ 7aa442d5
# PointPillars: Fast Encoders for Object Detection from Point Clouds
> [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://arxiv.org/abs/1812.05784)
<!-- [ALGORITHM] -->
## Abstract
Object detection in point clouds is an important aspect of many robotics applications such as autonomous driving. In this paper we consider the problem of encoding a point cloud into a format appropriate for a downstream detection pipeline. Recent literature suggests two types of encoders; fixed encoders tend to be fast but sacrifice accuracy, while encoders that are learned from data are more accurate, but slower. In this work we propose PointPillars, a novel encoder which utilizes PointNets to learn a representation of point clouds organized in vertical columns (pillars). While the encoded features can be used with any standard 2D convolutional detection architecture, we further propose a lean downstream network. Extensive experimentation shows that PointPillars outperforms previous encoders with respect to both speed and accuracy by a large margin. Despite only using lidar, our full detection pipeline significantly outperforms the state of the art, even among fusion methods, with respect to both the 3D and bird's eye view KITTI benchmarks. This detection performance is achieved while running at 62 Hz: a 2 - 4 fold runtime improvement. A faster version of our method matches the state of the art at 105 Hz. These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds.
<div align=center>
<img src="https://user-images.githubusercontent.com/79644370/143885905-aab6ffcf-7727-495e-90ca-edb8dd5e324b.png" width="800"/>
</div>
## Introduction
We implement PointPillars and provide the results and checkpoints on KITTI, nuScenes, Lyft and Waymo datasets.
## Results and models
### KITTI
| Backbone | Class | Lr schd | Mem (GB) | Inf time (fps) | AP | Download |
| :-------------------------------------------------------------: | :-----: | :---------: | :------: | :------------: | :---: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [SECFPN](./pointpillars_hv_secfpn_8xb6-160e_kitti-3d-car.py) | Car | cyclic 160e | 5.4 | | 77.6 | [model](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) \| [log](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.log.json) |
| [SECFPN](./pointpillars_hv_secfpn_8xb6-160e_kitti-3d-3class.py) | 3 Class | cyclic 160e | 5.5 | | 64.07 | [model](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) \| [log](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.log.json) |
### nuScenes
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP | NDS | Download |
| :---------------------------------------------------------------------: | :-----: | :------: | :------------: | :---: | :---: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [SECFPN](./pointpillars_hv_secfpn_sbn-all_8xb4-2x_nus-3d.py) | 2x | 16.4 | | 34.33 | 49.1 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20210826_225857-f19d00a3.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20210826_225857.log.json) |
| [SECFPN (FP16)](./pointpillars_hv_secfpn_sbn-all_8xb2-amp-2x_nus-3d.py) | 2x | 8.37 | | 35.19 | 50.27 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d_20201020_222626-c3f0483e.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_fp16_2x8_2x_nus-3d_20201020_222626.log.json) |
| [FPN](./pointpillars_hv_fpn_sbn-all_8xb4-2x_nus-3d.py) | 2x | 16.3 | | 39.7 | 53.2 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20210826_104936-fca299c1.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20210826_104936.log.json) |
| [FPN (FP16)](./pointpillars_hv_fpn_sbn-all_8xb2-amp-2x_nus-3d.py) | 2x | 8.40 | | 39.26 | 53.26 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d_20201021_120719-269f9dd6.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d_20201021_120719.log.json) |
### Lyft
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | Private Score | Public Score | Download |
| :-----------------------------------------------------------: | :-----: | :------: | :------------: | :-----------: | :----------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [SECFPN](./pointpillars_hv_secfpn_sbn-all_8xb2-2x_lyft-3d.py) | 2x | 12.2 | | 13.8 | 14.1 | [model](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) \| [log](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.log.json) |
| [FPN](./pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d.py) | 2x | 9.2 | | 14.8 | 15.0 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d_20210822_095429-0b3d6196.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d_20210822_095429.log.json) |
### Waymo
| Backbone | Load Interval | Class | Lr schd | Mem (GB) | Inf time (fps) | mAP@L1 | mAPH@L1 | mAP@L2 | **mAPH@L2** | Download |
| :----------------------------------------------------------------------: | :-----------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :----: | :---------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [SECFPN](./pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymoD5-3d-car.py) | 5 | Car | 2x | 7.76 | | 70.2 | 69.6 | 62.6 | 62.1 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-car/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-car_20200901_204315-302fc3e7.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-car/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-car_20200901_204315.log.json) |
| [SECFPN](./pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymoD5-3d-3class.py) | 5 | 3 Class | 2x | 8.12 | | 64.7 | 57.6 | 58.4 | 52.1 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class_20200831_204144-d1a706b1.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class_20200831_204144.log.json) |
| above @ Car | | | 2x | 8.12 | | 68.5 | 67.9 | 60.1 | 59.6 | |
| above @ Pedestrian | | | 2x | 8.12 | | 67.8 | 50.6 | 59.6 | 44.3 | |
| above @ Cyclist | | | 2x | 8.12 | | 57.7 | 54.4 | 55.5 | 52.4 | |
| [SECFPN](./pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymo-3d-car.py) | 1 | Car | 2x | 7.76 | | 72.1 | 71.5 | 63.6 | 63.1 | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymo-3d-car/hv_pointpillars_secfpn_sbn_2x16_2x_waymo-3d-car.log.json) |
| [SECFPN](./pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymo-3d-3class.py) | 1 | 3 Class | 2x | 8.12 | | 68.8 | 63.3 | 62.6 | 57.6 | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymo-3d-3class/hv_pointpillars_secfpn_sbn_2x16_2x_waymo-3d-3class.log.json) |
| above @ Car | | | 2x | 8.12 | | 71.6 | 71.0 | 63.1 | 62.5 | |
| above @ Pedestrian | | | 2x | 8.12 | | 70.6 | 56.7 | 62.9 | 50.2 | |
| above @ Cyclist | | | 2x | 8.12 | | 64.4 | 62.3 | 61.9 | 59.9 | |
#### Note:
- **Metric**: For model trained with 3 classes, the average APH@L2 (mAPH@L2) of all the categories is reported and used to rank the model. For model trained with only 1 class, the APH@L2 is reported and used to rank the model.
- **Data Split**: Here we provide several baselines for waymo dataset, among which D5 means that we divide the dataset into 5 folds and only use one fold for efficient experiments. Using the complete dataset can boost the performance a lot, especially for the detection of cyclist and pedestrian, where more than 5 mAP or mAPH improvement can be expected.
- **Implementation Details**: We basically follow the implementation in the [paper](https://arxiv.org/pdf/1912.04838.pdf) in terms of the network architecture (having a
stride of 1 for the first convolutional block). Different settings of voxelization, data augmentation and hyper parameters make these baselines outperform those in the paper by about 7 mAP for car and 4 mAP for pedestrian with only a subset of the whole dataset. All of these results are achieved without bells-and-whistles, e.g. ensemble, multi-scale training and test augmentation.
- **License Aggrement**: To comply the [license agreement of Waymo dataset](https://waymo.com/open/terms/), the pre-trained models on Waymo dataset are not released. We still release the training log as a reference to ease the future research.
- `FP16` means Mixed Precision (FP16) is adopted in training. With mixed precision training, we can train PointPillars with nuScenes dataset on 8 Titan XP GPUS with batch size of 2. This will cause OOM error without mixed precision training. The loss scale for PointPillars on nuScenes dataset is specifically tuned to avoid the loss to be Nan. We find 32 is more stable than 512, though loss scale 32 still cause Nan sometimes.
## Citation
```latex
@inproceedings{lang2019pointpillars,
title={Pointpillars: Fast encoders for object detection from point clouds},
author={Lang, Alex H and Vora, Sourabh and Caesar, Holger and Zhou, Lubing and Yang, Jiong and Beijbom, Oscar},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={12697--12705},
year={2019}
}
```
mmdetection3d/configs/pointpillars/metafile.yml 0 → 100644
View file @ 7aa442d5
Collections:
- Name: PointPillars
Metadata:
Training Techniques:
- AdamW
Architecture:
- Feature Pyramid Network
Paper:
URL: https://arxiv.org/abs/1812.05784
Title: 'PointPillars: Fast Encoders for Object Detection from Point Clouds'
README: configs/pointpillars/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/models/voxel_encoders/pillar_encoder.py#L13
Version: v0.6.0
Models:
- 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:
Training Data: KITTI
Training Memory (GB): 5.4
Training Resources: 8x V100 GPUs
Results:
- Task: 3D Object Detection
Dataset: KITTI
Metrics:
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: pointpillars_hv_secfpn_8xb6-160e_kitti-3d-3class
Alias: pointpillars_kitti-3class
In Collection: PointPillars
Config: configs/pointpillars/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-3class.py
Metadata:
Training Data: KITTI
Training Memory (GB): 5.5
Training Resources: 8x V100 GPUs
Results:
- Task: 3D Object Detection
Dataset: KITTI
Metrics:
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: 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:
Training Data: nuScenes
Training Memory (GB): 16.4
Training Resources: 8x V100 GPUs
Results:
- Task: 3D Object Detection
Dataset: nuScenes
Metrics:
mAP: 34.33
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: 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:
Training Data: nuScenes
Training Memory (GB): 16.3
Training Resources: 8x V100 GPUs
Results:
- Task: 3D Object Detection
Dataset: nuScenes
Metrics:
mAP: 39.71
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: 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:
Training Data: Lyft
Training Memory (GB): 12.2
Training Resources: 8x V100 GPUs
Results:
- Task: 3D Object Detection
Dataset: Lyft
Metrics:
Private Score: 13.8
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: 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:
Training Data: Lyft
Training Memory (GB): 9.2
Training Resources: 8x V100 GPUs
Results:
- Task: 3D Object Detection
Dataset: Lyft
Metrics:
Private Score: 14.0
Public Score: 15.0
Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d_20210822_095429-0b3d6196.pth
- Name: pointpillars_hv_secfpn_sbn_2x16_2x_waymoD5-3d-car
In Collection: PointPillars
Config: configs/pointpillars/pointpillars_hv_secfpn_sbn_2x16_2x_waymoD5-3d-car.py
Metadata:
Training Data: Waymo
Training Memory (GB): 7.76
Training Resources: 8x GeForce GTX 1080 Ti
Results:
- Task: 3D Object Detection
Dataset: Waymo
Metrics:
mAP@L1: 70.2
mAPH@L1: 69.6
mAP@L2: 62.6
mAPH@L2: 62.1
Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-car/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-car_20200901_204315-302fc3e7.pth
- Name: pointpillars_hv_secfpn_sbn_2x16_2x_waymoD5-3d-3class
Alias: pointpillars_waymod5-3class
In Collection: PointPillars
Config: configs/pointpillars/pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymoD5-3d-3class.py
Metadata:
Training Data: Waymo
Training Memory (GB): 8.12
Training Resources: 8x GeForce GTX 1080 Ti
Results:
- Task: 3D Object Detection
Dataset: Waymo
Metrics:
mAP@L1: 64.7
mAPH@L1: 57.6
mAP@L2: 58.4
mAPH@L2: 52.1
Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class_20200831_204144-d1a706b1.pth
- Name: pointpillars_hv_secfpn_sbn_2x16_2x_waymo-3d-car
In Collection: PointPillars
Config: configs/pointpillars/pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymo-3d-car.py
Metadata:
Training Data: Waymo
Training Memory (GB): 7.76
Training Resources: 8x GeForce GTX 1080 Ti
Results:
- Task: 3D Object Detection
Dataset: Waymo
Metrics:
mAP@L1: 72.1
mAPH@L1: 71.5
mAP@L2: 63.6
mAPH@L2: 63.1
- Name: pointpillars_hv_secfpn_sbn_2x16_2x_waymo-3d-3class
In Collection: PointPillars
Config: configs/pointpillars/pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymo-3d-3class.py
Metadata:
Training Data: Waymo
Training Memory (GB): 8.12
Training Resources: 8x GeForce GTX 1080 Ti
Results:
- Task: 3D Object Detection
Dataset: Waymo
Metrics:
mAP@L1: 68.8
mAPH@L1: 63.3
mAP@L2: 62.6
mAPH@L2: 57.6
mmdetection3d/configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d-range100.py 0 → 100644
View file @ 7aa442d5
_base_ = [
'../_base_/models/pointpillars_hv_fpn_range100_lyft.py',
'../_base_/datasets/lyft-3d-range100.py',
'../_base_/schedules/schedule-2x.py', '../_base_/default_runtime.py'
]
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)
mmdetection3d/configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d.py 0 → 100644
View file @ 7aa442d5
_base_ = [
'../_base_/models/pointpillars_hv_fpn_lyft.py',
'../_base_/datasets/lyft-3d.py', '../_base_/schedules/schedule-2x.py',
'../_base_/default_runtime.py'
]
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)
mmdetection3d/configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-amp-2x_nus-3d.py 0 → 100644
View file @ 7aa442d5
_base_ = './pointpillars_hv_fpn_sbn-all_8xb4-2x_nus-3d.py'
train_dataloader = dict(batch_size=2, num_workers=2)
# schedule settings
optim_wrapper = dict(type='AmpOptimWrapper', loss_scale=4096.)
mmdetection3d/configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb4-2x_nus-3d.py 0 → 100644
View file @ 7aa442d5
_base_ = [
'../_base_/models/pointpillars_hv_fpn_nus.py',
'../_base_/datasets/nus-3d.py', '../_base_/schedules/schedule-2x.py',
'../_base_/default_runtime.py'
]
# For nuScenes 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.
train_cfg = dict(val_interval=24)
mmdetection3d/configs/pointpillars/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-3class.py 0 → 100644
View file @ 7aa442d5
_base_ = [
'../_base_/models/pointpillars_hv_secfpn_kitti.py',
'../_base_/datasets/kitti-3d-3class.py',
'../_base_/schedules/cyclic-40e.py', '../_base_/default_runtime.py'
]
point_cloud_range = [0, -39.68, -3, 69.12, 39.68, 1]
# dataset settings
data_root = 'data/kitti/'
class_names = ['Pedestrian', 'Cyclist', 'Car']
metainfo = dict(classes=class_names)
backend_args = None
# PointPillars adopted a different sampling strategies among classes
db_sampler = dict(
data_root=data_root,
info_path=data_root + 'kitti_dbinfos_train.pkl',
rate=1.0,
prepare=dict(
filter_by_difficulty=[-1],
filter_by_min_points=dict(Car=5, Pedestrian=5, Cyclist=5)),
classes=class_names,
sample_groups=dict(Car=15, Pedestrian=15, Cyclist=15),
points_loader=dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
backend_args=backend_args),
backend_args=backend_args)
# PointPillars uses different augmentation hyper parameters
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
backend_args=backend_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(type='ObjectSample', db_sampler=db_sampler, use_ground_plane=True),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.78539816, 0.78539816],
scale_ratio_range=[0.95, 1.05]),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_labels_3d', 'gt_bboxes_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
backend_args=backend_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='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
dataset=dict(dataset=dict(pipeline=train_pipeline, metainfo=metainfo)))
test_dataloader = dict(dataset=dict(pipeline=test_pipeline, metainfo=metainfo))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline, metainfo=metainfo))
# In practice PointPillars also uses a different schedule
# optimizer
lr = 0.001
epoch_num = 80
optim_wrapper = dict(
optimizer=dict(lr=lr), clip_grad=dict(max_norm=35, norm_type=2))
param_scheduler = [
dict(
type='CosineAnnealingLR',
T_max=epoch_num * 0.4,
eta_min=lr * 10,
begin=0,
end=epoch_num * 0.4,
by_epoch=True,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=epoch_num * 0.6,
eta_min=lr * 1e-4,
begin=epoch_num * 0.4,
end=epoch_num * 1,
by_epoch=True,
convert_to_iter_based=True),
dict(
type='CosineAnnealingMomentum',
T_max=epoch_num * 0.4,
eta_min=0.85 / 0.95,
begin=0,
end=epoch_num * 0.4,
by_epoch=True,
convert_to_iter_based=True),
dict(
type='CosineAnnealingMomentum',
T_max=epoch_num * 0.6,
eta_min=1,
begin=epoch_num * 0.4,
end=epoch_num * 1,
convert_to_iter_based=True)
]
# max_norm=35 is slightly better than 10 for PointPillars in the earlier
# development of the codebase thus we keep the setting. But we does not
# specifically tune this parameter.
# PointPillars usually need longer schedule than second, we simply double
# the training schedule. Do remind that since we use RepeatDataset and
# repeat factor is 2, so we actually train 160 epochs.
train_cfg = dict(by_epoch=True, max_epochs=epoch_num, val_interval=2)
val_cfg = dict()
test_cfg = dict()
mmdetection3d/configs/pointpillars/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-car.py 0 → 100644
View file @ 7aa442d5
# model settings
_base_ = './pointpillars_hv_secfpn_8xb6-160e_kitti-3d-3class.py'
# dataset settings
dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Car']
metainfo = dict(classes=class_names)
backend_args = None
point_cloud_range = [0, -39.68, -3, 69.12, 39.68, 1]
model = dict(
bbox_head=dict(
type='Anchor3DHead',
num_classes=1,
anchor_generator=dict(
_delete_=True,
type='AlignedAnchor3DRangeGenerator',
ranges=[[0, -39.68, -1.78, 69.12, 39.68, -1.78]],
sizes=[[3.9, 1.6, 1.56]],
rotations=[0, 1.57],
reshape_out=True)),
# model training and testing settings
train_cfg=dict(
_delete_=True,
assigner=dict(
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.6,
neg_iou_thr=0.45,
min_pos_iou=0.45,
ignore_iof_thr=-1),
allowed_border=0,
pos_weight=-1,
debug=False))
db_sampler = dict(
data_root=data_root,
info_path=data_root + 'kitti_dbinfos_train.pkl',
rate=1.0,
prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
classes=class_names,
sample_groups=dict(Car=15),
points_loader=dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
backend_args=backend_args),
backend_args=backend_args)
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
backend_args=backend_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(type='ObjectSample', db_sampler=db_sampler, use_ground_plane=True),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.78539816, 0.78539816],
scale_ratio_range=[0.95, 1.05]),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_labels_3d', 'gt_bboxes_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
backend_args=backend_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='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
dataset=dict(dataset=dict(pipeline=train_pipeline, metainfo=metainfo)))
test_dataloader = dict(dataset=dict(pipeline=test_pipeline, metainfo=metainfo))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline, metainfo=metainfo))
mmdetection3d/configs/pointpillars/pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymo-3d-3class.py 0 → 100644
View file @ 7aa442d5
_base_ = [
'../_base_/models/pointpillars_hv_secfpn_waymo.py',
'../_base_/datasets/waymoD5-3d-3class.py',
'../_base_/schedules/schedule-2x.py',
'../_base_/default_runtime.py',
]
# data settings
train_dataloader = dict(dataset=dict(dataset=dict(load_interval=1)))
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (16 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=32)
mmdetection3d/configs/pointpillars/pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymo-3d-car.py 0 → 100644
View file @ 7aa442d5
_base_ = [
'../_base_/models/pointpillars_hv_secfpn_waymo.py',
'../_base_/datasets/waymoD5-3d-car.py',
'../_base_/schedules/schedule-2x.py',
'../_base_/default_runtime.py',
]
# data settings
train_dataloader = dict(dataset=dict(dataset=dict(load_interval=1)))
# model settings
model = dict(
type='MVXFasterRCNN',
pts_bbox_head=dict(
type='Anchor3DHead',
num_classes=1,
anchor_generator=dict(
type='AlignedAnchor3DRangeGenerator',
ranges=[[-74.88, -74.88, -0.0345, 74.88, 74.88, -0.0345]],
sizes=[[4.73, 2.08, 1.77]],
rotations=[0, 1.57],
reshape_out=True)),
# model training and testing settings
train_cfg=dict(
_delete_=True,
pts=dict(
assigner=dict(
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.55,
neg_iou_thr=0.4,
min_pos_iou=0.4,
ignore_iof_thr=-1),
allowed_border=0,
code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
pos_weight=-1,
debug=False)))
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (16 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=32)
mmdetection3d/configs/pointpillars/pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymoD5-3d-3class.py 0 → 100644
View file @ 7aa442d5
_base_ = [
'../_base_/models/pointpillars_hv_secfpn_waymo.py',
'../_base_/datasets/waymoD5-3d-3class.py',
'../_base_/schedules/schedule-2x.py',
'../_base_/default_runtime.py',
]
mmdetection3d/configs/pointpillars/pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymoD5-3d-car.py 0 → 100644
View file @ 7aa442d5
_base_ = [
'../_base_/models/pointpillars_hv_secfpn_waymo.py',
'../_base_/datasets/waymoD5-3d-car.py',
'../_base_/schedules/schedule-2x.py',
'../_base_/default_runtime.py',
]
# model settings
model = dict(
type='MVXFasterRCNN',
pts_bbox_head=dict(
type='Anchor3DHead',
num_classes=1,
anchor_generator=dict(
type='AlignedAnchor3DRangeGenerator',
ranges=[[-74.88, -74.88, -0.0345, 74.88, 74.88, -0.0345]],
sizes=[[4.73, 2.08, 1.77]],
rotations=[0, 1.57],
reshape_out=True)),
# model training and testing settings
train_cfg=dict(
_delete_=True,
pts=dict(
assigner=dict(
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.55,
neg_iou_thr=0.4,
min_pos_iou=0.4,
ignore_iof_thr=-1),
allowed_border=0,
code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
pos_weight=-1,
debug=False)))
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (16 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=32)
mmdetection3d/configs/pointpillars/pointpillars_hv_secfpn_sbn-all_8xb2-2x_lyft-3d-range100.py 0 → 100644
View file @ 7aa442d5
_base_ = [
'../_base_/models/pointpillars_hv_fpn_range100_lyft.py',
'../_base_/datasets/lyft-3d-range100.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=[
[4.75, 1.92, 1.71], # car
[10.24, 2.84, 3.44], # truck
[12.70, 2.92, 3.42], # bus
[6.52, 2.42, 2.34], # emergency vehicle
[8.17, 2.75, 3.20], # other vehicle
[2.35, 0.96, 1.59], # motorcycle
[1.76, 0.63, 1.44], # bicycle
[0.80, 0.76, 1.76], # pedestrian
[0.73, 0.35, 0.50] # animal
],
rotations=[0, 1.57],
reshape_out=True)))
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)
mmdetection3d/configs/pointpillars/pointpillars_hv_secfpn_sbn-all_8xb2-2x_lyft-3d.py 0 → 100644
View file @ 7aa442d5
_base_ = [
'../_base_/models/pointpillars_hv_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=[
[4.75, 1.92, 1.71], # car
[10.24, 2.84, 3.44], # truck
[12.70, 2.92, 3.42], # bus
[6.52, 2.42, 2.34], # emergency vehicle
[8.17, 2.75, 3.20], # other vehicle
[2.35, 0.96, 1.59], # motorcycle
[1.76, 0.63, 1.44], # bicycle
[0.80, 0.76, 1.76], # pedestrian
[0.73, 0.35, 0.50] # animal
],
rotations=[0, 1.57],
reshape_out=True)))
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)
mmdetection3d/configs/pointpillars/pointpillars_hv_secfpn_sbn-all_8xb2-amp-2x_nus-3d.py 0 → 100644
View file @ 7aa442d5
_base_ = './pointpillars_hv_secfpn_sbn-all_8xb4-2x_nus-3d.py'
train_dataloader = dict(batch_size=2, num_workers=2)
# schedule settings
optim_wrapper = dict(type='AmpOptimWrapper', loss_scale=4096.)
mmdetection3d/configs/pointpillars/pointpillars_hv_secfpn_sbn-all_8xb4-2x_nus-3d.py 0 → 100644
View file @ 7aa442d5
_base_ = [
'../_base_/models/pointpillars_hv_fpn_nus.py',
'../_base_/datasets/nus-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=[
[-49.6, -49.6, -1.80032795, 49.6, 49.6, -1.80032795],
[-49.6, -49.6, -1.74440365, 49.6, 49.6, -1.74440365],
[-49.6, -49.6, -1.68526504, 49.6, 49.6, -1.68526504],
[-49.6, -49.6, -1.67339111, 49.6, 49.6, -1.67339111],
[-49.6, -49.6, -1.61785072, 49.6, 49.6, -1.61785072],
[-49.6, -49.6, -1.80984986, 49.6, 49.6, -1.80984986],
[-49.6, -49.6, -1.763965, 49.6, 49.6, -1.763965],
],
sizes=[
[4.60718145, 1.95017717, 1.72270761], # car
[6.73778078, 2.4560939, 2.73004906], # truck
[12.01320693, 2.87427237, 3.81509561], # trailer
[1.68452161, 0.60058911, 1.27192197], # bicycle
[0.7256437, 0.66344886, 1.75748069], # pedestrian
[0.40359262, 0.39694519, 1.06232151], # traffic_cone
[0.48578221, 2.49008838, 0.98297065], # barrier
],
custom_values=[0, 0],
rotations=[0, 1.57],
reshape_out=True)))
# For nuScenes 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.
train_cfg = dict(val_interval=24)
mmdetection3d/configs/pv_rcnn/README.md 0 → 100644
View file @ 7aa442d5
# PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
> [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://arxiv.org/abs/1912.13192)
<!-- [ALGORITHM] -->
## Abstract
3D object detection has been receiving increasing attention from both industry and academia thanks to its wide applications in various fields such as autonomous driving and robotics. LiDAR sensors are widely adopted in autonomous driving vehicles and robots for capturing 3D scene information as sparse and irregular point clouds, which provide vital cues for 3D scene perception and understanding. In this paper, we propose to achieve high performance 3D object detection by designing novel point-voxel integrated networks to learn better 3D features from irregular point clouds.
<div align=center>
<img src="https://user-images.githubusercontent.com/88368822/202114244-ccf52f56-b8c9-4f1b-9cc2-80c7a9952c99.png" width="800"/>
</div>
## Results and models
### KITTI
| Backbone | Class | Lr schd | Mem (GB) | Inf time (fps) | mAP | Download |
| :---------------------------------------------: | :-----: | :--------: | :------: | :------------: | :---: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [SECFPN](./pv_rcnn_8xb2-80e_kitti-3d-3class.py) | 3 Class | cyclic 80e | 5.4 | | 72.28 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/pv_rcnn/pv_rcnn_8xb2-80e_kitti-3d-3class/pv_rcnn_8xb2-80e_kitti-3d-3class_20221117_234428-b384d22f.pth) \\ [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/pv_rcnn/pv_rcnn_8xb2-80e_kitti-3d-3class/pv_rcnn_8xb2-80e_kitti-3d-3class_20221117_234428.json) |
Note: mAP represents AP11 results on 3 Class under the moderate setting.
Detailed performance on KITTI 3D detection (3D) is as follows, evaluated by AP11 metric:
| | Easy | Moderate | Hard |
| ---------- | :---: | :------: | :---: |
| Car | 89.20 | 83.72 | 78.79 |
| Pedestrian | 66.64 | 59.84 | 55.33 |
| Cyclist | 87.25 | 73.27 | 69.61 |
## Citation
```latex
@article{ShaoshuaiShi2020PVRCNNPF,
title={PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection},
author={Shaoshuai Shi and Chaoxu Guo and Li Jiang and Zhe Wang and Jianping Shi and Xiaogang Wang and Hongsheng Li},
journal={computer vision and pattern recognition},
year={2020}
}
```
mmdetection3d/configs/pv_rcnn/metafile.yml 0 → 100644
View file @ 7aa442d5
Collections:
- Name: PV-RCNN
Metadata:
Training Data: KITTI
Training Techniques:
- AdamW
Training Resources: 8x A100 GPUs
Architecture:
- Feature Pyramid Network
Paper:
URL: https://arxiv.org/abs/1912.13192
Title: 'PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection'
README: configs/pv_rcnn/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/mmdet3d/models/detectors/pv_rcnn.py#L12
Version: v1.1.0rc2
Models:
- Name: pv_rcnn_8xb2-80e_kitti-3d-3class
In Collection: PV-RCNN
Config: configs/pv_rcnn/pv_rcnn_8xb2-80e_kitti-3d-3class.py
Metadata:
Training Memory (GB): 5.4
Results:
- Task: 3D Object Detection
Dataset: KITTI
Metrics:
mAP: 72.28
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/pv_rcnn/pv_rcnn_8xb2-80e_kitti-3d-3class/pv_rcnn_8xb2-80e_kitti-3d-3class_20221117_234428-b384d22f.pth
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