Skip to content

GitLab

Unverified Commit 06b56888 authored by Jingwei Zhang's avatar Jingwei Zhang Committed by GitHub
Browse files

[Fix] Update links of configs and restrict the version of MMCV (#2337) 

* update config link in readme

* update version restriction

* update

* fix some configs error in metafile

* add pvrcnn and fcaf3d in model-index.yml
parent e943e84d
Hide whitespace changes
Inline Side-by-side
Showing with 56 additions and 46 deletions
configs/centerpoint/README.md
View file @ 06b56888
......@@ -104,23 +104,23 @@ data = dict(
### CenterPoint
| Backbone | Voxel type (voxel size) | Dcn | Circular nms | Mem (GB) | Inf time (fps) | mAP | NDS | Download |
| :---------------------------------------------------------------------------------: | :---------------------: | :-: | :----------: | :------: | :------------: | :---: | :---: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [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_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_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_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 | |
| 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) | ✗ | ✓ | 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_voxel01_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.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_voxel0075_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d.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-3d.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) | ✗ | ✓ | 4.6 | | 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) | ✓ | ✗ | 4.9 | | 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. |
......
configs/fcos3d/README.md
View file @ 06b56888
......@@ -52,7 +52,7 @@ We also provide visualization functions to show the monocular 3D detection resul
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP | NDS | Download |
| :-------------------------------------------------------------------------------------: | :-----: | :------: | :------------: | :--: | :--: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| \[ResNet101 w/ DCN\](./fcos3d_r101-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d.py) | 1x | 8.69 | | 29.8 | 37.7 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210715_235813-4bed5239.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210715_235813.log.json) |
| [ResNet101 w/ DCN](./fcos3d_r101-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d.py) | 1x | 8.69 | | 29.8 | 37.7 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210715_235813-4bed5239.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210715_235813.log.json) |
| [above w/ finetune](./fcos3d_r101-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d_finetune.py) | 1x | 8.69 | | 32.1 | 39.5 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210717_095645-8d806dc2.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210717_095645.log.json) |
| above w/ tta | 1x | 8.69 | | 33.1 | 40.3 | |
......
configs/pointpillars/README.md
View file @ 06b56888
......@@ -30,9 +30,9 @@ We implement PointPillars and provide the results and checkpoints on KITTI, nuSc
| 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)](./hv_pointpillars_secfpn_sbn-all_fp16_2x8_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) |
| [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)](./hv_pointpillars_fpn_sbn-all_fp16_2x8_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) |
| [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
......
configs/pointpillars/metafile.yml
View file @ 06b56888
......@@ -147,9 +147,9 @@ Models:
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: hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-car
- Name: pointpillars_hv_secfpn_sbn_2x16_2x_waymoD5-3d-car
In Collection: PointPillars
Config: configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-car.py
Config: configs/pointpillars/pointpillars_hv_secfpn_sbn_2x16_2x_waymoD5-3d-car.py
Metadata:
Training Data: Waymo
Training Memory (GB): 7.76
......@@ -164,7 +164,7 @@ Models:
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: hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class
- 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
......@@ -182,7 +182,7 @@ Models:
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: hv_pointpillars_secfpn_sbn_2x16_2x_waymo-3d-car
- 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:
......@@ -198,7 +198,7 @@ Models:
mAP@L2: 63.6
mAPH@L2: 63.1
- Name: hv_pointpillars_secfpn_sbn_2x16_2x_waymo-3d-3class
- 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:
......
configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb4-amp-2x_nus-3d.py → configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-amp-2x_nus-3d.py
View file @ 06b56888
_base_ = 'pointpillars_hv_fpn_sbn-all_8xb4-2x_nus-3d.py'
_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=512.)
configs/pointpillars/pointpillars_hv_secfpn_sbn-all_8xb4-amp-2x_nus-3d.py → configs/pointpillars/pointpillars_hv_secfpn_sbn-all_8xb2-amp-2x_nus-3d.py
View file @ 06b56888
_base_ = 'pointpillars_hv_secfpn_sbn-all_8xb4-2x_nus-3d.py'
_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=512.)
configs/pv_rcnn/README.md
View file @ 06b56888
......@@ -4,7 +4,7 @@
<!-- [ALGORITHM] -->
## Introduction
## 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.
......
configs/regnet/metafile.yml
View file @ 06b56888
Models:
- Name: hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d
- Name: pointpillars_hv_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d
In Collection: PointPillars
Config: configs/regnet/hv_pointpillars_regnet-400mf_secfpn_sbn-all_8xb4-2x_nus-3d.py
Config: configs/regnet/pointpillars_hv_regnet-400mf_secfpn_sbn-all_8xb4-2x_nus-3d.py
Metadata:
Training Data: nuScenes
Training Memory (GB): 16.4
......@@ -16,9 +16,9 @@ Models:
NDS: 55.2
Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230334-53044f32.pth
- Name: hv_pointpillars_regnet-400mf_fpn_sbn-all_8xb4-2x_nus-3d
- Name: pointpillars_hv_regnet-400mf_fpn_sbn-all_8xb4-2x_nus-3d
In Collection: PointPillars
Config: configs/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_8xb4-2x_nus-3d.py
Config: configs/regnet/pointpillars_hv_regnet-400mf_fpn_sbn-all_8xb4-2x_nus-3d.py
Metadata:
Training Data: nuScenes
Training Memory (GB): 17.3
......@@ -33,9 +33,9 @@ Models:
NDS: 56.4
Weights: https://download.openmmlab.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
- Name: hv_pointpillars_regnet-1.6gf_fpn_sbn-all_8xb4-2x_nus-3d
- Name: pointpillars_hv_regnet-1.6gf_fpn_sbn-all_8xb4-2x_nus-3d
In Collection: PointPillars
Config: configs/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_8xb4-2x_nus-3d.py
Config: configs/regnet/pointpillars_hv_regnet-1.6gf_fpn_sbn-all_8xb4-2x_nus-3d.py
Metadata:
Training Data: nuScenes
Training Memory (GB): 24.0
......@@ -50,9 +50,9 @@ Models:
NDS: 59.3
Weights: https://download.openmmlab.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
- Name: hv_pointpillars_regnet-400mf_secfpn_sbn-all_2x8_2x_lyft-3d
- Name: pointpillars_hv_regnet-400mf_secfpn_sbn-all_2x8_2x_lyft-3d
In Collection: PointPillars
Config: configs/regnet/hv_pointpillars_regnet-400mf_secfpn_sbn-all_2x8_2x_lyft-3d.py
Config: configs/regnet/pointpillars_hv_regnet-400mf_secfpn_sbn-all_2x8_2x_lyft-3d.py
Metadata:
Training Data: Lyft
Training Memory (GB): 15.9
......@@ -67,9 +67,9 @@ Models:
Public Score: 15.1
Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_secfpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_regnet-400mf_secfpn_sbn-all_2x8_2x_lyft-3d_20210524_092151-42513826.pth
- Name: hv_pointpillars_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d
- Name: pointpillars_hv_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d
In Collection: PointPillars
Config: configs/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d.py
Config: configs/regnet/pointpillars_hv_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d.py
Metadata:
Training Data: Lyft
Training Memory (GB): 13.0
......
configs/second/metafile.yml
View file @ 06b56888
......@@ -60,7 +60,7 @@ Models:
- Name: second_hv_secfpn_8xb6-amp-80e_kitti-3d-car
In Collection: SECOND
Config: configs/second/hv_second_secfpn_8xb6-amp-80e_kitti-3d-car.py
Config: configs/second/second_hv_secfpn_8xb6-amp-80e_kitti-3d-car.py
Metadata:
Training Techniques:
- AdamW
......@@ -79,7 +79,7 @@ Models:
- Name: second_hv_secfpn_8xb6-amp-80e_kitti-3d-3class
In Collection: SECOND
Config: configs/second/hv_second_secfpn_8xb6-amp-80e_kitti-3d-3class.py
Config: configs/second/second_hv_secfpn_8xb6-amp-80e_kitti-3d-3class.py
Metadata:
Training Techniques:
- AdamW
......
configs/smoke/metafile.yml
View file @ 06b56888
......@@ -17,9 +17,9 @@ Collections:
Version: v1.0.0
Models:
- Name: smoke_dla34_pytorch_dlaneck_gn-all_4xb8-6x_kitti-mono3d
- Name: smoke_dla34_dlaneck_gn-all_4xb8-6x_kitti-mono3d
In Collection: SMOKE
Config: configs/smoke/smoke_dla34_pytorch_dlaneck_gn-all_4xb8-6x_kitti-mono3d.py
Config: configs/smoke/smoke_dla34_dlaneck_gn-all_4xb8-6x_kitti-mono3d.py
Metadata:
Training Memory (GB): 9.6
Results:
......
docs/en/notes/faq.md
View file @ 06b56888
......@@ -11,8 +11,8 @@ We list some potential troubles encountered by users and developers, along with
| MMDetection3D version | MMEngine version | MMCV version | MMDetection version |
| --------------------- | :----------------------: | :---------------------: | :----------------------: |
| dev-1.x | mmengine>=0.6.0, \<1.0.0 | mmcv>=2.0.0rc4, \<2.1.0 | mmdet>=3.0.0rc0, \<3.1.0 |
| v1.1.0rc3 | mmengine>=0.1.0, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 | mmdet>=3.0.0rc0, \<3.1.0 |
| v1.1.0rc2 | mmengine>=0.1.0, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 | mmdet>=3.0.0rc0, \<3.1.0 |
| v1.1.0rc3 | mmengine>=0.1.0, \<1.0.0 | mmcv>=2.0.0rc3, \<2.1.0 | mmdet>=3.0.0rc0, \<3.1.0 |
| v1.1.0rc2 | mmengine>=0.1.0, \<1.0.0 | mmcv>=2.0.0rc3, \<2.1.0 | mmdet>=3.0.0rc0, \<3.1.0 |
| v1.1.0rc1 | mmengine>=0.1.0, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 | mmdet>=3.0.0rc0, \<3.1.0 |
| v1.1.0rc0 | mmengine>=0.1.0, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 | mmdet>=3.0.0rc0, \<3.1.0 |
......
docs/zh_cn/get_started.md
View file @ 06b56888
......@@ -44,7 +44,7 @@ conda install pytorch torchvision cpuonly -c pytorch
```shell
pip install -U openmim
mim install mmengine
mim install 'mmcv>=2.0.0rc0'
mim install 'mmcv>=2.0.0rc4'
mim install 'mmdet>=3.0.0rc0'
```
......
model-index.yml
View file @ 06b56888
......@@ -23,3 +23,5 @@ Import:
- configs/smoke/metafile.yml
- configs/ssn/metafile.yml
- configs/votenet/metafile.yml
- configs/pv_rcnn/metafile.yml
- configs/fcaf3d/metafile.yml
projects/PETR/README.md
View file @ 06b56888
......@@ -34,9 +34,9 @@ python tools/test.py projects/PETR/config/petr/petr_vovnet_gridmask_p4_800x320.p
This Result is trained by petr_vovnet_gridmask_p4_800x320.py and use [weights](https://drive.google.com/file/d/1ABI5BoQCkCkP4B0pO5KBJ3Ni0tei0gZi/view?usp=sharing) as pretrain weight.
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP | NDS | Download |
| :----------------------------------------------------------------------------------------------: | :-----: | :------: | :------------: | :--: | :--: | :----------------------: |
| [petr_vovnet_gridmask_p4_800x320](projects/PETR/configs/petr/petr_vovnet_gridmask_p4_800x320.py) | 1x | 7.62 | 18.7 | 38.3 | 43.5 | [model](<>) \| [log](<>) |
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP | NDS | Download |
| :---------------------------------------------------------------------------: | :-----: | :------: | :------------: | :--: | :--: | :----------------------: |
| [petr_vovnet_gridmask_p4_800x320](configs/petr_vovnet_gridmask_p4_800x320.py) | 1x | 7.62 | 18.7 | 38.3 | 43.5 | [model](<>) \| [log](<>) |
```
mAP: 0.3830
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment