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Unverified Commit cf922153 authored by Shilong Zhang's avatar Shilong Zhang Committed by GitHub
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[Docs] Replace markdownlint with mdformat for avoiding installing ruby (#1489)

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configs/mvxnet/README.md
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...@@ -20,9 +20,9 @@ We implement MVX-Net and provide its results and models on KITTI dataset. ...@@ -20,9 +20,9 @@ We implement MVX-Net and provide its results and models on KITTI dataset.
### KITTI ### KITTI
| Backbone |Class| Lr schd | Mem (GB) | Inf time (fps) | mAP | Download | | Backbone | Class | Lr schd | Mem (GB) | Inf time (fps) | mAP | Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | | :-------------------------------------------------------------------: | :-----: | :--------: | :------: | :------------: | :---: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [SECFPN](./dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class.py)|3 Class|cosine 80e|6.7||63.22|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/mvxnet/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class_20210831_060805-83442923.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/mvxnet/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class_20210831_060805.log.json)| | [SECFPN](./dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class.py) | 3 Class | cosine 80e | 6.7 | | 63.22 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/mvxnet/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class_20210831_060805-83442923.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/mvxnet/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class_20210831_060805.log.json) |
## Citation ## Citation
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configs/nuimages/README.md
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configs/paconv/README.md
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...@@ -24,9 +24,9 @@ We implement PAConv and provide the result and checkpoints on S3DIS dataset. ...@@ -24,9 +24,9 @@ We implement PAConv and provide the result and checkpoints on S3DIS dataset.
### S3DIS ### S3DIS
| Method | Split | Lr schd | Mem (GB) | Inf time (fps) | mIoU (Val set) | Download | | Method | Split | Lr schd | Mem (GB) | Inf time (fps) | mIoU (Val set) | Download |
| :-------------------------------------------------------------------------: | :----: | :---------: | :------: | :------------: | :------------: | :----------------------: | | :-------------------------------------------------------------------------: | :----: | :---------: | :------: | :------------: | :------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [PAConv (SSG)](./paconv_ssg_8x8_cosine_150e_s3dis_seg-3d-13class.py) | Area_5 | cosine 150e | 5.8 | | 66.65 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/paconv/paconv_ssg_8x8_cosine_150e_s3dis_seg-3d-13class/paconv_ssg_8x8_cosine_150e_s3dis_seg-3d-13class_20210729_200615-2147b2d1.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/paconv/paconv_ssg_8x8_cosine_150e_s3dis_seg-3d-13class/paconv_ssg_8x8_cosine_150e_s3dis_seg-3d-13class_20210729_200615.log.json) | | [PAConv (SSG)](./paconv_ssg_8x8_cosine_150e_s3dis_seg-3d-13class.py) | Area_5 | cosine 150e | 5.8 | | 66.65 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/paconv/paconv_ssg_8x8_cosine_150e_s3dis_seg-3d-13class/paconv_ssg_8x8_cosine_150e_s3dis_seg-3d-13class_20210729_200615-2147b2d1.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/paconv/paconv_ssg_8x8_cosine_150e_s3dis_seg-3d-13class/paconv_ssg_8x8_cosine_150e_s3dis_seg-3d-13class_20210729_200615.log.json) |
| [PAConv\* (SSG)](./paconv_cuda_ssg_8x8_cosine_200e_s3dis_seg-3d-13class.py) | Area_5 | cosine 200e | 3.8 | | 65.33 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/paconv/paconv_cuda_ssg_8x8_cosine_200e_s3dis_seg-3d-13class/paconv_cuda_ssg_8x8_cosine_200e_s3dis_seg-3d-13class_20210802_171802-e5ea9bb9.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/paconv/paconv_cuda_ssg_8x8_cosine_200e_s3dis_seg-3d-13class/paconv_cuda_ssg_8x8_cosine_200e_s3dis_seg-3d-13class_20210802_171802.log.json) | | [PAConv\* (SSG)](./paconv_cuda_ssg_8x8_cosine_200e_s3dis_seg-3d-13class.py) | Area_5 | cosine 200e | 3.8 | | 65.33 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/paconv/paconv_cuda_ssg_8x8_cosine_200e_s3dis_seg-3d-13class/paconv_cuda_ssg_8x8_cosine_200e_s3dis_seg-3d-13class_20210802_171802-e5ea9bb9.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/paconv/paconv_cuda_ssg_8x8_cosine_200e_s3dis_seg-3d-13class/paconv_cuda_ssg_8x8_cosine_200e_s3dis_seg-3d-13class_20210802_171802.log.json) |
**Notes:** **Notes:**
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configs/parta2/README.md
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...@@ -20,10 +20,10 @@ We implement Part-A^2 and provide its results and checkpoints on KITTI dataset. ...@@ -20,10 +20,10 @@ We implement Part-A^2 and provide its results and checkpoints on KITTI dataset.
### KITTI ### KITTI
| Backbone |Class| Lr schd | Mem (GB) | Inf time (fps) | mAP | Download | | Backbone | Class | Lr schd | Mem (GB) | Inf time (fps) | mAP | Download |
| :---------: | :-----: |:-----: | :------: | :------------: | :----: |:----: | | :------------------------------------------------------------: | :-----: | :--------: | :------: | :------------: | :---: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [SECFPN](./hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py) |3 Class|cyclic 80e|4.1||68.33|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class_20210831_022017-454a5344.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class_20210831_022017.log.json)| | [SECFPN](./hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py) | 3 Class | cyclic 80e | 4.1 | | 68.33 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class_20210831_022017-454a5344.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class_20210831_022017.log.json) |
| [SECFPN](./hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car.py) |Car |cyclic 80e|4.0||79.08|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car_20210831_022017-cb7ff621.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car_20210831_022017.log.json)| | [SECFPN](./hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car.py) | Car | cyclic 80e | 4.0 | | 79.08 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car_20210831_022017-cb7ff621.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car_20210831_022017.log.json) |
## Citation ## Citation
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configs/pgd/README.md
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...@@ -27,13 +27,13 @@ A more extensive study based on FCOS3D and PGD is on-going. Please stay tuned. ...@@ -27,13 +27,13 @@ A more extensive study based on FCOS3D and PGD is on-going. Please stay tuned.
### KITTI ### KITTI
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP_11 / mAP_40 | Download | | Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP_11 / mAP_40 | Download |
| :---------: | :-----: | :------: | :------------: | :----: | :------: | | :--------------------------------------------------------------: | :-----: | :------: | :------------: | :-------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
|[ResNet101](./pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d.py)|4x|9.07||18.33 / 13.23|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d_20211022_102608-8a97533b.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d_20211022_102608.log.json)| | [ResNet101](./pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d.py) | 4x | 9.07 | | 18.33 / 13.23 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d_20211022_102608-8a97533b.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d_20211022_102608.log.json) |
Detailed performance on KITTI 3D detection (3D/BEV) is as follows, evaluated by AP11 and AP40 metric: Detailed performance on KITTI 3D detection (3D/BEV) is as follows, evaluated by AP11 and AP40 metric:
| | Easy | Moderate | Hard | | | Easy | Moderate | Hard |
|-------------|:-------------:|:--------------:|:-------------:| | ---------- | :-----------: | :-----------: | :-----------: |
| Car (AP11) | 24.09 / 30.11 | 18.33 / 23.46 | 16.90 / 19.33 | | Car (AP11) | 24.09 / 30.11 | 18.33 / 23.46 | 16.90 / 19.33 |
| Car (AP40) | 19.27 / 26.60 | 13.23 / 18.23 | 10.65 / 15.00 | | Car (AP40) | 19.27 / 26.60 | 13.23 / 18.23 | 10.65 / 15.00 |
...@@ -42,13 +42,13 @@ Note: mAP represents Car moderate 3D strict AP11 / AP40 results. Because of the ...@@ -42,13 +42,13 @@ Note: mAP represents Car moderate 3D strict AP11 / AP40 results. Because of the
### NuScenes ### NuScenes
| Backbone | Lr schd | Mem (GB) | mAP | NDS | Download | | Backbone | Lr schd | Mem (GB) | mAP | NDS | Download |
| :---------: | :-----: | :------: | :----: |:----: | :------: | | :------------------------------------------------------------------------------: | :-----: | :------: | :--: | :--: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
|[ResNet101 w/ DCN](./pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d.py)|1x|9.20|31.7|39.3|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_20211116_195350-f4b5eec2.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_20211116_195350.log.json)| | [ResNet101 w/ DCN](./pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d.py) | 1x | 9.20 | 31.7 | 39.3 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_20211116_195350-f4b5eec2.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_20211116_195350.log.json) |
|[above w/ finetune](./pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_finetune.py)|1x|9.20|34.6|41.1|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_finetune/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_finetune_20211118_093245-fd419681.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_finetune/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_finetune_20211118_093245.log.json)| | [above w/ finetune](./pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_finetune.py) | 1x | 9.20 | 34.6 | 41.1 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_finetune/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_finetune_20211118_093245-fd419681.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_finetune/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_finetune_20211118_093245.log.json) |
|above w/ tta|1x|9.20|35.5|41.8|| | above w/ tta | 1x | 9.20 | 35.5 | 41.8 | |
|[ResNet101 w/ DCN](./pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d.py)|2x|9.20|33.6|40.9|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_20211112_125314-cb677266.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_20211112_125314.log.json)| | [ResNet101 w/ DCN](./pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d.py) | 2x | 9.20 | 33.6 | 40.9 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_20211112_125314-cb677266.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_20211112_125314.log.json) |
|[above w/ finetune](./pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_finetune.py)|2x|9.20|35.8|42.5|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_finetune/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_finetune_20211114_162135-5ec7c1cd.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_finetune/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_finetune_20211114_162135.log.json)| | [above w/ finetune](./pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_finetune.py) | 2x | 9.20 | 35.8 | 42.5 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_finetune/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_finetune_20211114_162135-5ec7c1cd.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_finetune/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_finetune_20211114_162135.log.json) |
|above w/ tta|2x|9.20|36.8|43.1|| | above w/ tta | 2x | 9.20 | 36.8 | 43.1 | |
## Citation ## Citation
......
configs/point_rcnn/README.md
View file @ cf922153
...@@ -20,16 +20,16 @@ We implement PointRCNN and provide the result with checkpoints on KITTI dataset. ...@@ -20,16 +20,16 @@ We implement PointRCNN and provide the result with checkpoints on KITTI dataset.
### KITTI ### KITTI
| Backbone |Class| Lr schd | Mem (GB) | Inf time (fps) | mAP | Download | | Backbone | Class | Lr schd | Mem (GB) | Inf time (fps) | mAP | Download |
| :---------: | :-----: |:-----: | :------: | :------------: | :----: |:----: | | :-------------------------------------------------: | :-----: | :--------: | :------: | :------------: | :---: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [PointNet++](./point_rcnn_2x8_kitti-3d-3classes.py) |3 Class|cyclic 40e|4.6||70.83|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/point_rcnn/point_rcnn_2x8_kitti-3d-3classes_20211208_151344.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/point_rcnn/point_rcnn_2x8_kitti-3d-3classes_20211208_151344.log.json)| | [PointNet++](./point_rcnn_2x8_kitti-3d-3classes.py) | 3 Class | cyclic 40e | 4.6 | | 70.83 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/point_rcnn/point_rcnn_2x8_kitti-3d-3classes_20211208_151344.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/point_rcnn/point_rcnn_2x8_kitti-3d-3classes_20211208_151344.log.json) |
Note: mAP represents AP11 results on 3 Class under the moderate setting. 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: Detailed performance on KITTI 3D detection (3D) is as follows, evaluated by AP11 metric:
| | Easy | Moderate | Hard | | | Easy | Moderate | Hard |
|-------------|:-------------:|:--------------:|:------------:| | ---------- | :---: | :------: | :---: |
| Car | 89.13 | 78.72 | 78.24 | | Car | 89.13 | 78.72 | 78.24 |
| Pedestrian | 65.81 | 59.57 | 52.75 | | Pedestrian | 65.81 | 59.57 | 52.75 |
| Cyclist | 93.51 | 74.19 | 70.73 | | Cyclist | 93.51 | 74.19 | 70.73 |
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configs/pointnet2/README.md
View file @ cf922153
...@@ -23,16 +23,18 @@ We implement PointNet++ and provide the result and checkpoints on ScanNet and S3 ...@@ -23,16 +23,18 @@ We implement PointNet++ and provide the result and checkpoints on ScanNet and S3
### ScanNet ### ScanNet
| Method | Input | Lr schd | Mem (GB) | Inf time (fps) | mIoU (Val set) | mIoU (Test set) | Download | | Method | Input | Lr schd | Mem (GB) | Inf time (fps) | mIoU (Val set) | mIoU (Test set) | Download |
| :-------------------------------------------------------------------------------------: | :-------: | :---------: | :------: | :------------: | :------------: | :-------------: | ------------------------ | | :-------------------------------------------------------------------------------------: | :-------: | :---------: | :------: | :------------: | :------------: | :-------------: | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| [PointNet++ (SSG)](./pointnet2_ssg_xyz-only_16x2_cosine_200e_scannet_seg-3d-20class.py) | XYZ | cosine 200e | 1.9 | | 53.91 | | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_xyz-only_16x2_cosine_200e_scannet_seg-3d-20class/pointnet2_ssg_xyz-only_16x2_cosine_200e_scannet_seg-3d-20class_20210514_143628-4e341a48.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_xyz-only_16x2_cosine_200e_scannet_seg-3d-20class/pointnet2_ssg_xyz-only_16x2_cosine_200e_scannet_seg-3d-20class_20210514_143628.log.json) | | [PointNet++ (SSG)](./pointnet2_ssg_xyz-only_16x2_cosine_200e_scannet_seg-3d-20class.py) | XYZ | cosine 200e | 1.9 | | 53.91 | | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_xyz-only_16x2_cosine_200e_scannet_seg-3d-20class/pointnet2_ssg_xyz-only_16x2_cosine_200e_scannet_seg-3d-20class_20210514_143628-4e341a48.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_xyz-only_16x2_cosine_200e_scannet_seg-3d-20class/pointnet2_ssg_xyz-only_16x2_cosine_200e_scannet_seg-3d-20class_20210514_143628.log.json) |
| [PointNet++ (SSG)](./pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class.py) | XYZ+Color | cosine 200e | 1.9 | | 54.44 | | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class_20210514_143644-ee73704a.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class_20210514_143644.log.json) | | [PointNet++ (SSG)](./pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class.py) | XYZ+Color | cosine 200e | 1.9 | | 54.44 | | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class_20210514_143644-ee73704a.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class_20210514_143644.log.json) |
| [PointNet++ (MSG)](./pointnet2_msg_xyz-only_16x2_cosine_250e_scannet_seg-3d-20class.py) | XYZ | cosine 250e | 2.4 | | 54.26 | | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_msg_xyz-only_16x2_cosine_250e_scannet_seg-3d-20class/pointnet2_msg_xyz-only_16x2_cosine_250e_scannet_seg-3d-20class_20210514_143838-b4a3cf89.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_msg_xyz-only_16x2_cosine_250e_scannet_seg-3d-20class/pointnet2_msg_xyz-only_16x2_cosine_250e_scannet_seg-3d-20class_20210514_143838.log.json) | | [PointNet++ (MSG)](./pointnet2_msg_xyz-only_16x2_cosine_250e_scannet_seg-3d-20class.py) | XYZ | cosine 250e | 2.4 | | 54.26 | | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_msg_xyz-only_16x2_cosine_250e_scannet_seg-3d-20class/pointnet2_msg_xyz-only_16x2_cosine_250e_scannet_seg-3d-20class_20210514_143838-b4a3cf89.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_msg_xyz-only_16x2_cosine_250e_scannet_seg-3d-20class/pointnet2_msg_xyz-only_16x2_cosine_250e_scannet_seg-3d-20class_20210514_143838.log.json) |
| [PointNet++ (MSG)](./pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class.py) | XYZ+Color | cosine 250e | 2.4 | | 55.05 | | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class/pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class_20210514_144009-24477ab1.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class/pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class_20210514_144009.log.json) | | [PointNet++ (MSG)](./pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class.py) | XYZ+Color | cosine 250e | 2.4 | | 55.05 | | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class/pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class_20210514_144009-24477ab1.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class/pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class_20210514_144009.log.json) |
**Notes:** **Notes:**
- The original PointNet++ paper conducted experiments on the ScanNet V1 dataset, while later point cloud segmentor papers often used ScanNet V2. Following common practice, we report results on the ScanNet V2 dataset. - The original PointNet++ paper conducted experiments on the ScanNet V1 dataset, while later point cloud segmentor papers often used ScanNet V2. Following common practice, we report results on the ScanNet V2 dataset.
- Since ScanNet dataset doesn't provide ground-truth labels for the test set, users can only evaluate test set performance by submitting to its online benchmark [website](http://kaldir.vc.in.tum.de/scannet_benchmark/). However, users are only allowed to submit once every two weeks. Therefore, we currently report val set mIoU. Test set performance may be added in the future. - Since ScanNet dataset doesn't provide ground-truth labels for the test set, users can only evaluate test set performance by submitting to its online benchmark [website](http://kaldir.vc.in.tum.de/scannet_benchmark/). However, users are only allowed to submit once every two weeks. Therefore, we currently report val set mIoU. Test set performance may be added in the future.
- To generate submission file for ScanNet online benchmark, you need to modify the ScanNet dataset's [config](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/scannet_seg-3d-20class.py#L126). Change `ann_file=data_root + 'scannet_infos_val.pkl'` to `ann_file=data_root + 'scannet_infos_test.pkl'`, and then simply run: - To generate submission file for ScanNet online benchmark, you need to modify the ScanNet dataset's [config](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/scannet_seg-3d-20class.py#L126). Change `ann_file=data_root + 'scannet_infos_val.pkl'` to `ann_file=data_root + 'scannet_infos_test.pkl'`, and then simply run:
```shell ```shell
...@@ -44,9 +46,9 @@ We implement PointNet++ and provide the result and checkpoints on ScanNet and S3 ...@@ -44,9 +46,9 @@ We implement PointNet++ and provide the result and checkpoints on ScanNet and S3
### S3DIS ### S3DIS
| Method | Split | Lr schd | Mem (GB) | Inf time (fps) | mIoU (Val set) | Download | | Method | Split | Lr schd | Mem (GB) | Inf time (fps) | mIoU (Val set) | Download |
| :-------------------------------------------------------------------------: | :----: | :--------: | :------: | :------------: | :------------: | :----------------------: | | :-------------------------------------------------------------------------: | :----: | :--------: | :------: | :------------: | :------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [PointNet++ (SSG)](./pointnet2_ssg_16x2_cosine_50e_s3dis_seg-3d-13class.py) | Area_5 | cosine 50e | 3.6 | | 56.93 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_16x2_cosine_50e_s3dis_seg-3d-13class/pointnet2_ssg_16x2_cosine_50e_s3dis_seg-3d-13class_20210514_144205-995d0119.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_16x2_cosine_50e_s3dis_seg-3d-13class/pointnet2_ssg_16x2_cosine_50e_s3dis_seg-3d-13class_20210514_144205.log.json) | | [PointNet++ (SSG)](./pointnet2_ssg_16x2_cosine_50e_s3dis_seg-3d-13class.py) | Area_5 | cosine 50e | 3.6 | | 56.93 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_16x2_cosine_50e_s3dis_seg-3d-13class/pointnet2_ssg_16x2_cosine_50e_s3dis_seg-3d-13class_20210514_144205-995d0119.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_16x2_cosine_50e_s3dis_seg-3d-13class/pointnet2_ssg_16x2_cosine_50e_s3dis_seg-3d-13class_20210514_144205.log.json) |
| [PointNet++ (MSG)](./pointnet2_msg_16x2_cosine_80e_s3dis_seg-3d-13class.py) | Area_5 | cosine 80e | 3.6 | | 58.04 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_msg_16x2_cosine_80e_s3dis_seg-3d-13class/pointnet2_msg_16x2_cosine_80e_s3dis_seg-3d-13class_20210514_144307-b2059817.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_msg_16x2_cosine_80e_s3dis_seg-3d-13class/pointnet2_msg_16x2_cosine_80e_s3dis_seg-3d-13class_20210514_144307.log.json) | | [PointNet++ (MSG)](./pointnet2_msg_16x2_cosine_80e_s3dis_seg-3d-13class.py) | Area_5 | cosine 80e | 3.6 | | 58.04 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_msg_16x2_cosine_80e_s3dis_seg-3d-13class/pointnet2_msg_16x2_cosine_80e_s3dis_seg-3d-13class_20210514_144307-b2059817.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_msg_16x2_cosine_80e_s3dis_seg-3d-13class/pointnet2_msg_16x2_cosine_80e_s3dis_seg-3d-13class_20210514_144307.log.json) |
**Notes:** **Notes:**
......
configs/pointpillars/README.md
View file @ cf922153
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configs/regnet/README.md
View file @ cf922153
...@@ -21,6 +21,7 @@ The pre-trained modles are converted from [model zoo of pycls](https://github.co ...@@ -21,6 +21,7 @@ The pre-trained modles are converted from [model zoo of pycls](https://github.co
## Usage ## Usage
To use a regnet model, there are two steps to do: To use a regnet model, there are two steps to do:
1. Convert the model to ResNet-style supported by MMDetection 1. Convert the model to ResNet-style supported by MMDetection
2. Modify backbone and neck in config accordingly 2. Modify backbone and neck in config accordingly
...@@ -34,8 +35,8 @@ ResNet-style checkpoints used in MMDetection. ...@@ -34,8 +35,8 @@ ResNet-style checkpoints used in MMDetection.
```bash ```bash
python -u tools/model_converters/regnet2mmdet.py ${PRETRAIN_PATH} ${STORE_PATH} python -u tools/model_converters/regnet2mmdet.py ${PRETRAIN_PATH} ${STORE_PATH}
``` ```
This script convert model from `PRETRAIN_PATH` and store the converted model in `STORE_PATH`.
This script convert model from `PRETRAIN_PATH` and store the converted model in `STORE_PATH`.
### Modify config ### Modify config
...@@ -50,22 +51,22 @@ For other pre-trained models or self-implemented regnet models, the users are re ...@@ -50,22 +51,22 @@ For other pre-trained models or self-implemented regnet models, the users are re
### nuScenes ### nuScenes
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP |NDS| Download | | Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP | NDS | Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | | :------------------------------------------------------------------------------------: | :-----: | :------: | :------------: | :---: | :--: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
|[SECFPN](../pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py)|2x|16.4||35.17|49.7|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725-0817d270.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725.log.json)| | [SECFPN](../pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py) | 2x | 16.4 | | 35.17 | 49.7 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725-0817d270.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725.log.json) |
|[RegNetX-400MF-SECFPN](./hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d.py)| 2x |16.4||41.2|55.2|[model](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) | [log](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.log.json)| | [RegNetX-400MF-SECFPN](./hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d.py) | 2x | 16.4 | | 41.2 | 55.2 | [model](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) \| [log](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.log.json) |
|[FPN](../pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|17.1||40.0|53.3|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405-2fa62f3d.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405.log.json)| | [FPN](../pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py) | 2x | 17.1 | | 40.0 | 53.3 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405-2fa62f3d.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405.log.json) |
|[RegNetX-400MF-FPN](./hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|17.3||44.8|56.4|[model](https://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) | [log](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.log.json)| | [RegNetX-400MF-FPN](./hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d.py) | 2x | 17.3 | | 44.8 | 56.4 | [model](https://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) \| [log](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.log.json) |
|[RegNetX-1.6gF-FPN](./hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|24.0||48.2|59.3|[model](https://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) | [log](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.log.json)| | [RegNetX-1.6gF-FPN](./hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py) | 2x | 24.0 | | 48.2 | 59.3 | [model](https://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) \| [log](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.log.json) |
### Lyft ### Lyft
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | Private Score | Public Score | Download | | Backbone | Lr schd | Mem (GB) | Inf time (fps) | Private Score | Public Score | Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | | :-------------------------------------------------------------------------------------: | :-----: | :------: | :------------: | :-----------: | :----------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
|[SECFPN](../pointpillars/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d.py)|2x|12.2||13.9|14.1|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d_20210517_204807-2518e3de.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d_20210517_204807.log.json)| | [SECFPN](../pointpillars/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d.py) | 2x | 12.2 | | 13.9 | 14.1 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d_20210517_204807-2518e3de.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d_20210517_204807.log.json) |
|[RegNetX-400MF-SECFPN](./hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_lyft-3d.py)| 2x |15.9||14.9|15.1|[model](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) | [log](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.log.json)| | [RegNetX-400MF-SECFPN](./hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_lyft-3d.py) | 2x | 15.9 | | 14.9 | 15.1 | [model](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) \| [log](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.log.json) |
|[FPN](../pointpillars/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d.py)|2x|9.2||14.9|15.1|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d_20210517_202818-fc6904c3.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d_20210517_202818.log.json)| | [FPN](../pointpillars/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d.py) | 2x | 9.2 | | 14.9 | 15.1 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d_20210517_202818-fc6904c3.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d_20210517_202818.log.json) |
|[RegNetX-400MF-FPN](./hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_lyft-3d.py)|2x|13.0||16.0|16.1|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d_20210521_115618-823dcf18.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d_20210521_115618.log.json)| | [RegNetX-400MF-FPN](./hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_lyft-3d.py) | 2x | 13.0 | | 16.0 | 16.1 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d_20210521_115618-823dcf18.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d_20210521_115618.log.json) |
## Citation ## Citation
......
configs/second/README.md
View file @ cf922153
...@@ -20,21 +20,21 @@ We implement SECOND and provide the results and checkpoints on KITTI dataset. ...@@ -20,21 +20,21 @@ We implement SECOND and provide the results and checkpoints on KITTI dataset.
### KITTI ### KITTI
| Backbone |Class| Lr schd | Mem (GB) | Inf time (fps) | mAP |Download | | Backbone | Class | Lr schd | Mem (GB) | Inf time (fps) | mAP | Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | | :-----------------------------------------------------------------: | :-----: | :--------: | :------: | :------------: | :---: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [SECFPN](./hv_second_secfpn_6x8_80e_kitti-3d-car.py)| Car |cyclic 80e|5.4||79.07|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-car/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238-393f000c.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-car/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238.log.json)| | [SECFPN](./hv_second_secfpn_6x8_80e_kitti-3d-car.py) | Car | cyclic 80e | 5.4 | | 79.07 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-car/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238-393f000c.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-car/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238.log.json) |
| [SECFPN (FP16)](./hv_second_secfpn_fp16_6x8_80e_kitti-3d-car.py)| Car |cyclic 80e|2.9||78.72|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301-1f5ad833.pth)| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301.log.json)| | [SECFPN (FP16)](./hv_second_secfpn_fp16_6x8_80e_kitti-3d-car.py) | Car | cyclic 80e | 2.9 | | 78.72 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301-1f5ad833.pth)\| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301.log.json) |
| [SECFPN](./hv_second_secfpn_6x8_80e_kitti-3d-3class.py)| 3 Class |cyclic 80e|5.4||65.74|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-3class/hv_second_secfpn_6x8_80e_kitti-3d-3class_20210831_022017-ae782e87.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-3class/hv_second_secfpn_6x8_80e_kitti-3d-3class_20210831_022017log.json)| | [SECFPN](./hv_second_secfpn_6x8_80e_kitti-3d-3class.py) | 3 Class | cyclic 80e | 5.4 | | 65.74 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-3class/hv_second_secfpn_6x8_80e_kitti-3d-3class_20210831_022017-ae782e87.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-3class/hv_second_secfpn_6x8_80e_kitti-3d-3class_20210831_022017log.json) |
| [SECFPN (FP16)](./hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py)| 3 Class |cyclic 80e|2.9||67.4|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059-05f67bdf.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059.log.json)| | [SECFPN (FP16)](./hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py) | 3 Class | cyclic 80e | 2.9 | | 67.4 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059-05f67bdf.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059.log.json) |
### Waymo ### Waymo
| Backbone | Load Interval | Class | Lr schd | Mem (GB) | Inf time (fps) | mAP@L1 | mAPH@L1 | mAP@L2 | **mAPH@L2** | Download | | Backbone | Load Interval | Class | Lr schd | Mem (GB) | Inf time (fps) | mAP@L1 | mAPH@L1 | mAP@L2 | **mAPH@L2** | Download |
| :-------: | :-----------: |:-----:| :------:| :------: | :------------: | :----: | :-----: | :-----: | :-----: | :------: | | :-----------------------------------------------------------: | :-----------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :----: | :---------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [SECFPN](./hv_second_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py)|5|3 Class|2x|8.12||65.3|61.7|58.9|55.7|[log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_sbn_4x8_2x_waymoD5-3d-3class/hv_second_secfpn_sbn_4x8_2x_waymoD5-3d-3class_20201115_112448.log.json)| | [SECFPN](./hv_second_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py) | 5 | 3 Class | 2x | 8.12 | | 65.3 | 61.7 | 58.9 | 55.7 | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_sbn_4x8_2x_waymoD5-3d-3class/hv_second_secfpn_sbn_4x8_2x_waymoD5-3d-3class_20201115_112448.log.json) |
| above @ Car|||2x|8.12||67.1|66.6|58.7|58.2| | | above @ Car | | | 2x | 8.12 | | 67.1 | 66.6 | 58.7 | 58.2 | |
| above @ Pedestrian|||2x|8.12||68.1|59.1|59.5|51.5| | | above @ Pedestrian | | | 2x | 8.12 | | 68.1 | 59.1 | 59.5 | 51.5 | |
| above @ Cyclist|||2x|8.12||60.7|59.5|58.4|57.3| | | above @ Cyclist | | | 2x | 8.12 | | 60.7 | 59.5 | 58.4 | 57.3 | |
Note: Note:
......
configs/smoke/README.md
View file @ cf922153
...@@ -21,17 +21,17 @@ We implement SMOKE and provide the results and checkpoints on KITTI dataset. ...@@ -21,17 +21,17 @@ We implement SMOKE and provide the results and checkpoints on KITTI dataset.
### KITTI ### KITTI
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP | Download | | Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP | Download |
| :---------: | :-----: | :------: | :------------: | :----: | :------: | | :------------------------------------------------------------------: | :-----: | :------: | :------------: | :---: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
|[DLA34](./smoke_dla34_pytorch_dlaneck_gn-all_8x4_6x_kitti-mono3d.py)|6x|9.64||13.85|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/smoke/smoke_dla34_pytorch_dlaneck_gn-all_8x4_6x_kitti-mono3d_20210929_015553-d46d9bb0.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/smoke/smoke_dla34_pytorch_dlaneck_gn-all_8x4_6x_kitti-mono3d_20210929_015553.log.json) | [DLA34](./smoke_dla34_pytorch_dlaneck_gn-all_8x4_6x_kitti-mono3d.py) | 6x | 9.64 | | 13.85 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/smoke/smoke_dla34_pytorch_dlaneck_gn-all_8x4_6x_kitti-mono3d_20210929_015553-d46d9bb0.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/smoke/smoke_dla34_pytorch_dlaneck_gn-all_8x4_6x_kitti-mono3d_20210929_015553.log.json) |
Note: mAP represents Car moderate 3D strict AP11 results. Note: mAP represents Car moderate 3D strict AP11 results.
Detailed performance on KITTI 3D detection (3D/BEV) is as follows, evaluated by AP11 metric: Detailed performance on KITTI 3D detection (3D/BEV) is as follows, evaluated by AP11 metric:
| | Easy | Moderate | Hard | | | Easy | Moderate | Hard |
|-------------|:-------------:|:--------------:|:------------:| | ---------- | :-----------: | :-----------: | :-----------: |
| Car | 16.92 / 22.97 | 13.85 / 18.32 | 11.90 / 15.88| | Car | 16.92 / 22.97 | 13.85 / 18.32 | 11.90 / 15.88 |
| Pedestrian | 11.13 / 12.61| 11.10 / 11.32 | 10.67 / 11.14| | Pedestrian | 11.13 / 12.61 | 11.10 / 11.32 | 10.67 / 11.14 |
| Cyclist | 0.99 / 1.47 | 0.54 / 0.65 | 0.55 / 0.67 | | Cyclist | 0.99 / 1.47 | 0.54 / 0.65 | 0.55 / 0.67 |
## Citation ## Citation
......
configs/ssn/README.md
View file @ cf922153
...@@ -21,19 +21,19 @@ We implement PointPillars with Shape-aware grouping heads used in the SSN and pr ...@@ -21,19 +21,19 @@ We implement PointPillars with Shape-aware grouping heads used in the SSN and pr
### NuScenes ### NuScenes
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP | NDS | Download | | Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP | NDS | Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | | :--------------------------------------------------------------------------------------------: | :-----: | :------: | :------------: | :---: | :---: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
|[SECFPN](../pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py)|2x|16.4||35.17|49.76|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725-0817d270.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725.log.json)| | [SECFPN](../pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py) | 2x | 16.4 | | 35.17 | 49.76 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725-0817d270.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725.log.json) |
|[SSN](./hv_ssn_secfpn_sbn-all_2x16_2x_nus-3d.py)|2x|3.6||40.91|54.44|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/ssn/hv_ssn_secfpn_sbn-all_2x16_2x_nus-3d/hv_ssn_secfpn_sbn-all_2x16_2x_nus-3d_20210830_101351-51915986.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/ssn/hv_ssn_secfpn_sbn-all_2x16_2x_nus-3d/hv_ssn_secfpn_sbn-all_2x16_2x_nus-3d_20210830_101351.log.json)| | [SSN](./hv_ssn_secfpn_sbn-all_2x16_2x_nus-3d.py) | 2x | 3.6 | | 40.91 | 54.44 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/ssn/hv_ssn_secfpn_sbn-all_2x16_2x_nus-3d/hv_ssn_secfpn_sbn-all_2x16_2x_nus-3d_20210830_101351-51915986.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/ssn/hv_ssn_secfpn_sbn-all_2x16_2x_nus-3d/hv_ssn_secfpn_sbn-all_2x16_2x_nus-3d_20210830_101351.log.json) |
[RegNetX-400MF-SECFPN](../regnet/hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d.py)|2x|16.4||41.15|55.20|[model](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) | [log](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.log.json)| | [RegNetX-400MF-SECFPN](../regnet/hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d.py) | 2x | 16.4 | | 41.15 | 55.20 | [model](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) \| [log](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.log.json) |
|[RegNetX-400MF-SSN](./hv_ssn_regnet-400mf_secfpn_sbn-all_2x16_2x_nus-3d.py)|2x|5.1||46.65|58.24|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/ssn/hv_ssn_regnet-400mf_secfpn_sbn-all_2x16_2x_nus-3d/hv_ssn_regnet-400mf_secfpn_sbn-all_2x16_2x_nus-3d_20210829_210615-361e5e04.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/ssn/hv_ssn_regnet-400mf_secfpn_sbn-all_2x16_2x_nus-3d/hv_ssn_regnet-400mf_secfpn_sbn-all_2x16_2x_nus-3d_20210829_210615.log.json)| | [RegNetX-400MF-SSN](./hv_ssn_regnet-400mf_secfpn_sbn-all_2x16_2x_nus-3d.py) | 2x | 5.1 | | 46.65 | 58.24 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/ssn/hv_ssn_regnet-400mf_secfpn_sbn-all_2x16_2x_nus-3d/hv_ssn_regnet-400mf_secfpn_sbn-all_2x16_2x_nus-3d_20210829_210615-361e5e04.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/ssn/hv_ssn_regnet-400mf_secfpn_sbn-all_2x16_2x_nus-3d/hv_ssn_regnet-400mf_secfpn_sbn-all_2x16_2x_nus-3d_20210829_210615.log.json) |
### Lyft ### Lyft
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | Private Score | Public Score | Download | | Backbone | Lr schd | Mem (GB) | Inf time (fps) | Private Score | Public Score | Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | | :--------------------------------------------------------------------------: | :-----: | :------: | :------------: | :-----------: | :----------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
|[SECFPN](../pointpillars/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d.py)|2x|12.2||13.9|14.1|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d_20210517_204807-2518e3de.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d_20210517_204807.log.json)| | [SECFPN](../pointpillars/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d.py) | 2x | 12.2 | | 13.9 | 14.1 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d_20210517_204807-2518e3de.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d_20210517_204807.log.json) |
|[SSN](./hv_ssn_secfpn_sbn-all_2x16_2x_lyft-3d.py)|2x|8.5||17.5|17.5|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/ssn/hv_ssn_secfpn_sbn-all_2x16_2x_lyft-3d/hv_ssn_secfpn_sbn-all_2x16_2x_lyft-3d_20210822_134731-46841b41.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/ssn/hv_ssn_secfpn_sbn-all_2x16_2x_lyft-3d/hv_ssn_secfpn_sbn-all_2x16_2x_lyft-3d_20210822_134731.log.json)| | [SSN](./hv_ssn_secfpn_sbn-all_2x16_2x_lyft-3d.py) | 2x | 8.5 | | 17.5 | 17.5 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/ssn/hv_ssn_secfpn_sbn-all_2x16_2x_lyft-3d/hv_ssn_secfpn_sbn-all_2x16_2x_lyft-3d_20210822_134731-46841b41.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/ssn/hv_ssn_secfpn_sbn-all_2x16_2x_lyft-3d/hv_ssn_secfpn_sbn-all_2x16_2x_lyft-3d_20210822_134731.log.json) |
|[RegNetX-400MF-SSN](./hv_ssn_regnet-400mf_secfpn_sbn-all_1x16_2x_lyft-3d.py)|2x|7.4||17.9|18|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/ssn/hv_ssn_regnet-400mf_secfpn_sbn-all_1x16_2x_lyft-3d/hv_ssn_regnet-400mf_secfpn_sbn-all_1x16_2x_lyft-3d_20210829_122825-d93475a1.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/ssn/hv_ssn_regnet-400mf_secfpn_sbn-all_1x16_2x_lyft-3d/hv_ssn_regnet-400mf_secfpn_sbn-all_1x16_2x_lyft-3d_20210829_122825.log.json)| | [RegNetX-400MF-SSN](./hv_ssn_regnet-400mf_secfpn_sbn-all_1x16_2x_lyft-3d.py) | 2x | 7.4 | | 17.9 | 18 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/ssn/hv_ssn_regnet-400mf_secfpn_sbn-all_1x16_2x_lyft-3d/hv_ssn_regnet-400mf_secfpn_sbn-all_1x16_2x_lyft-3d_20210829_122825-d93475a1.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/ssn/hv_ssn_regnet-400mf_secfpn_sbn-all_1x16_2x_lyft-3d/hv_ssn_regnet-400mf_secfpn_sbn-all_1x16_2x_lyft-3d_20210829_122825.log.json) |
Note: Note:
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configs/votenet/README.md
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...@@ -20,17 +20,17 @@ We implement VoteNet and provide the result and checkpoints on ScanNet and SUNRG ...@@ -20,17 +20,17 @@ We implement VoteNet and provide the result and checkpoints on ScanNet and SUNRG
### ScanNet ### ScanNet
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 |AP@0.5| Download | | Backbone | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 | AP@0.5 | Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | | :-----------------------------------------------: | :-----: | :------: | :------------: | :-----: | :----: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [PointNet++](./votenet_8x8_scannet-3d-18class.py) | 3x |4.1||62.34|40.82|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/votenet/votenet_8x8_scannet-3d-18class/votenet_8x8_scannet-3d-18class_20210823_234503-cf8134fa.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/votenet/votenet_8x8_scannet-3d-18class/votenet_8x8_scannet-3d-18class_20210823_234503.log.json)| | [PointNet++](./votenet_8x8_scannet-3d-18class.py) | 3x | 4.1 | | 62.34 | 40.82 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/votenet/votenet_8x8_scannet-3d-18class/votenet_8x8_scannet-3d-18class_20210823_234503-cf8134fa.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/votenet/votenet_8x8_scannet-3d-18class/votenet_8x8_scannet-3d-18class_20210823_234503.log.json) |
### SUNRGBD ### SUNRGBD
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 |AP@0.5| Download | | Backbone | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 | AP@0.5 | Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | | :------------------------------------------------: | :-----: | :------: | :------------: | :-----: | :----: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [PointNet++](./votenet_16x8_sunrgbd-3d-10class.py) | 3x |8.1||59.78|35.77|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/votenet/votenet_16x8_sunrgbd-3d-10class/votenet_16x8_sunrgbd-3d-10class_20210820_162823-bf11f014.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/votenet/votenet_16x8_sunrgbd-3d-10class/votenet_16x8_sunrgbd-3d-10class_20210820_162823.log.json)| | [PointNet++](./votenet_16x8_sunrgbd-3d-10class.py) | 3x | 8.1 | | 59.78 | 35.77 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/votenet/votenet_16x8_sunrgbd-3d-10class/votenet_16x8_sunrgbd-3d-10class_20210820_162823-bf11f014.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/votenet/votenet_16x8_sunrgbd-3d-10class/votenet_16x8_sunrgbd-3d-10class_20210820_162823.log.json) |
**Notice**: If your current mmdetection3d version >= 0.6.0, and you are using the checkpoints downloaded from the above links or using checkpoints trained with mmdetection3d version < 0.6.0, the checkpoints have to be first converted via [tools/model_converters/convert_votenet_checkpoints.py](../../tools/model_converters/convert_votenet_checkpoints.py): **Notice**: If your current mmdetection3d version >= 0.6.0, and you are using the checkpoints downloaded from the above links or using checkpoints trained with mmdetection3d version \< 0.6.0, the checkpoints have to be first converted via [tools/model_converters/convert_votenet_checkpoints.py](../../tools/model_converters/convert_votenet_checkpoints.py):
``` ```
python ./tools/model_converters/convert_votenet_checkpoints.py ${ORIGINAL_CHECKPOINT_PATH} --out=${NEW_CHECKPOINT_PATH} python ./tools/model_converters/convert_votenet_checkpoints.py ${ORIGINAL_CHECKPOINT_PATH} --out=${NEW_CHECKPOINT_PATH}
...@@ -50,9 +50,9 @@ Adding IoU loss (simply = 1-IoU) boosts VoteNet's performance. To use IoU loss, ...@@ -50,9 +50,9 @@ Adding IoU loss (simply = 1-IoU) boosts VoteNet's performance. To use IoU loss,
iou_loss=dict(type='AxisAlignedIoULoss', reduction='sum', loss_weight=10.0 / 3.0) iou_loss=dict(type='AxisAlignedIoULoss', reduction='sum', loss_weight=10.0 / 3.0)
``` ```
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 |AP@0.5| Download | | Backbone | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 | AP@0.5 | Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | | :-------------------------------------------------------: | :-----: | :------: | :------------: | :-----: | :----: | :------: |
| [PointNet++](./votenet_iouloss_8x8_scannet-3d-18class.py) | 3x |4.1||63.81|44.21|/| | [PointNet++](./votenet_iouloss_8x8_scannet-3d-18class.py) | 3x | 4.1 | | 63.81 | 44.21 | / |
For now, we only support calculating IoU loss for axis-aligned bounding boxes since the CUDA op of general 3D IoU calculation does not implement the backward method. Therefore, IoU loss can only be used for ScanNet dataset for now. For now, we only support calculating IoU loss for axis-aligned bounding boxes since the CUDA op of general 3D IoU calculation does not implement the backward method. Therefore, IoU loss can only be used for ScanNet dataset for now.
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data/s3dis/README.md
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...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
We follow the procedure in [pointnet](https://github.com/charlesq34/pointnet). We follow the procedure in [pointnet](https://github.com/charlesq34/pointnet).
1. Download S3DIS data by filling this [Google form](https://docs.google.com/forms/d/e/1FAIpQLScDimvNMCGhy_rmBA2gHfDu3naktRm6A8BPwAWWDv-Uhm6Shw/viewform?c=0&w=1). Download the ```Stanford3dDataset_v1.2_Aligned_Version.zip``` file and unzip it. Link or move the folder to this level of directory. 1. Download S3DIS data by filling this [Google form](https://docs.google.com/forms/d/e/1FAIpQLScDimvNMCGhy_rmBA2gHfDu3naktRm6A8BPwAWWDv-Uhm6Shw/viewform?c=0&w=1). Download the `Stanford3dDataset_v1.2_Aligned_Version.zip` file and unzip it. Link or move the folder to this level of directory.
2. In this directory, extract point clouds and annotations by running `python collect_indoor3d_data.py`. 2. In this directory, extract point clouds and annotations by running `python collect_indoor3d_data.py`.
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docs/en/1_exist_data_model.md
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...@@ -32,6 +32,7 @@ python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [- ...@@ -32,6 +32,7 @@ python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [-
For now, CPU testing is only supported for SMOKE. For now, CPU testing is only supported for SMOKE.
Optional arguments: Optional arguments:
- `RESULT_FILE`: Filename of the output results in pickle format. If not specified, the results will not be saved to a file. - `RESULT_FILE`: Filename of the output results in pickle format. If not specified, the results will not be saved to a file.
- `EVAL_METRICS`: Items to be evaluated on the results. Allowed values depend on the dataset. Typically we default to use official metrics for evaluation on different datasets, so it can be simply set to `mAP` as a placeholder for detection tasks, which applies to nuScenes, Lyft, ScanNet and SUNRGBD. For KITTI, if we only want to evaluate the 2D detection performance, we can simply set the metric to `img_bbox` (unstable, stay tuned). For Waymo, we provide both KITTI-style evaluation (unstable) and Waymo-style official protocol, corresponding to metric `kitti` and `waymo` respectively. We recommend to use the default official metric for stable performance and fair comparison with other methods. Similarly, the metric can be set to `mIoU` for segmentation tasks, which applies to S3DIS and ScanNet. - `EVAL_METRICS`: Items to be evaluated on the results. Allowed values depend on the dataset. Typically we default to use official metrics for evaluation on different datasets, so it can be simply set to `mAP` as a placeholder for detection tasks, which applies to nuScenes, Lyft, ScanNet and SUNRGBD. For KITTI, if we only want to evaluate the 2D detection performance, we can simply set the metric to `img_bbox` (unstable, stay tuned). For Waymo, we provide both KITTI-style evaluation (unstable) and Waymo-style official protocol, corresponding to metric `kitti` and `waymo` respectively. We recommend to use the default official metric for stable performance and fair comparison with other methods. Similarly, the metric can be set to `mIoU` for segmentation tasks, which applies to S3DIS and ScanNet.
- `--show`: If specified, detection results will be plotted in the silient mode. It is only applicable to single GPU testing and used for debugging and visualization. This should be used with `--show-dir`. - `--show`: If specified, detection results will be plotted in the silient mode. It is only applicable to single GPU testing and used for debugging and visualization. This should be used with `--show-dir`.
...@@ -182,6 +183,7 @@ Optional arguments are: ...@@ -182,6 +183,7 @@ Optional arguments are:
- `--options 'Key=value'`: Override some settings in the used config. - `--options 'Key=value'`: Override some settings in the used config.
Difference between `resume-from` and `load-from`: Difference between `resume-from` and `load-from`:
- `resume-from` loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. It is usually used for resuming the training process that is interrupted accidentally. - `resume-from` loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. It is usually used for resuming the training process that is interrupted accidentally.
- `load-from` only loads the model weights and the training epoch starts from 0. It is usually used for finetuning. - `load-from` only loads the model weights and the training epoch starts from 0. It is usually used for finetuning.
...@@ -217,7 +219,6 @@ NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR ./tools/dist_tra ...@@ -217,7 +219,6 @@ NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR ./tools/dist_tra
Usually it is slow if you do not have high speed networking like InfiniBand. Usually it is slow if you do not have high speed networking like InfiniBand.
### Launch multiple jobs on a single machine ### Launch multiple jobs on a single machine
If you launch multiple jobs on a single machine, e.g., 2 jobs of 4-GPU training on a machine with 8 GPUs, If you launch multiple jobs on a single machine, e.g., 2 jobs of 4-GPU training on a machine with 8 GPUs,
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docs/en/benchmarks.md
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# Benchmarks # Benchmarks
Here we benchmark the training and testing speed of models in MMDetection3D, Here we benchmark the training and testing speed of models in MMDetection3D,
...@@ -6,34 +5,35 @@ with some other open source 3D detection codebases. ...@@ -6,34 +5,35 @@ with some other open source 3D detection codebases.
## Settings ## Settings
* Hardwares: 8 NVIDIA Tesla V100 (32G) GPUs, Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz - Hardwares: 8 NVIDIA Tesla V100 (32G) GPUs, Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
* Software: Python 3.7, CUDA 10.1, cuDNN 7.6.5, PyTorch 1.3, numba 0.48.0. - Software: Python 3.7, CUDA 10.1, cuDNN 7.6.5, PyTorch 1.3, numba 0.48.0.
* Model: Since all the other codebases implements different models, we compare the corresponding models including SECOND, PointPillars, Part-A2, and VoteNet with them separately. - Model: Since all the other codebases implements different models, we compare the corresponding models including SECOND, PointPillars, Part-A2, and VoteNet with them separately.
* Metrics: We use the average throughput in iterations of the entire training run and skip the first 50 iterations of each epoch to skip GPU warmup time. - Metrics: We use the average throughput in iterations of the entire training run and skip the first 50 iterations of each epoch to skip GPU warmup time.
## Main Results ## Main Results
We compare the training speed (samples/s) with other codebases if they implement the similar models. The results are as below, the greater the numbers in the table, the faster of the training process. The models that are not supported by other codebases are marked by `×`. We compare the training speed (samples/s) with other codebases if they implement the similar models. The results are as below, the greater the numbers in the table, the faster of the training process. The models that are not supported by other codebases are marked by `×`.
| Methods | MMDetection3D | OpenPCDet |votenet| Det3D | | Methods | MMDetection3D | OpenPCDet | votenet | Det3D |
|:-------:|:-------------:|:---------:|:-----:|:-----:| | :-----------------: | :-----------: | :-------: | :-----: | :---: |
| VoteNet | 358 | × | 77 | × | | VoteNet | 358 | × | 77 | × |
| PointPillars-car| 141 | × | × | 140 | | PointPillars-car | 141 | × | × | 140 |
| PointPillars-3class| 107 |44 | × | × | | PointPillars-3class | 107 | 44 | × | × |
| SECOND| 40 |30 | × | × | | SECOND | 40 | 30 | × | × |
| Part-A2| 17 |14 | × | × | | Part-A2 | 17 | 14 | × | × |
## Details of Comparison ## Details of Comparison
### Modification for Calculating Speed ### Modification for Calculating Speed
* __MMDetection3D__: We try to use as similar settings as those of other codebases as possible using [benchmark configs](https://github.com/open-mmlab/MMDetection3D/blob/master/configs/benchmark). - __MMDetection3D__: We try to use as similar settings as those of other codebases as possible using [benchmark configs](https://github.com/open-mmlab/MMDetection3D/blob/master/configs/benchmark).
* __Det3D__: For comparison with Det3D, we use the commit [519251e](https://github.com/poodarchu/Det3D/tree/519251e72a5c1fdd58972eabeac67808676b9bb7). - __Det3D__: For comparison with Det3D, we use the commit [519251e](https://github.com/poodarchu/Det3D/tree/519251e72a5c1fdd58972eabeac67808676b9bb7).
* __OpenPCDet__: For comparison with OpenPCDet, we use the commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2). - __OpenPCDet__: For comparison with OpenPCDet, we use the commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2).
For training speed, we add code to record the running time in the file `./tools/train_utils/train_utils.py`. We calculate the speed of each epoch, and report the average speed of all the epochs. For training speed, we add code to record the running time in the file `./tools/train_utils/train_utils.py`. We calculate the speed of each epoch, and report the average speed of all the epochs.
<details> <details>
<summary> <summary>
(diff to make it use the same method for benchmarking speed - click to expand) (diff to make it use the same method for benchmarking speed - click to expand)
...@@ -117,19 +117,18 @@ We compare the training speed (samples/s) with other codebases if they implement ...@@ -117,19 +117,18 @@ We compare the training speed (samples/s) with other codebases if they implement
### VoteNet ### VoteNet
* __MMDetection3D__: With release v0.1.0, run - __MMDetection3D__: With release v0.1.0, run
```bash ```bash
./tools/dist_train.sh configs/votenet/votenet_16x8_sunrgbd-3d-10class.py 8 --no-validate ./tools/dist_train.sh configs/votenet/votenet_16x8_sunrgbd-3d-10class.py 8 --no-validate
``` ```
* __votenet__: At commit [2f6d6d3](https://github.com/facebookresearch/votenet/tree/2f6d6d36ff98d96901182e935afe48ccee82d566), run - __votenet__: At commit [2f6d6d3](https://github.com/facebookresearch/votenet/tree/2f6d6d36ff98d96901182e935afe48ccee82d566), run
```bash ```bash
python train.py --dataset sunrgbd --batch_size 16 python train.py --dataset sunrgbd --batch_size 16
``` ```
Then benchmark the test speed by running Then benchmark the test speed by running
```bash ```bash
...@@ -199,13 +198,13 @@ We compare the training speed (samples/s) with other codebases if they implement ...@@ -199,13 +198,13 @@ We compare the training speed (samples/s) with other codebases if they implement
### PointPillars-car ### PointPillars-car
* __MMDetection3D__: With release v0.1.0, run - __MMDetection3D__: With release v0.1.0, run
```bash ```bash
./tools/dist_train.sh configs/benchmark/hv_pointpillars_secfpn_3x8_100e_det3d_kitti-3d-car.py 8 --no-validate ./tools/dist_train.sh configs/benchmark/hv_pointpillars_secfpn_3x8_100e_det3d_kitti-3d-car.py 8 --no-validate
``` ```
* __Det3D__: At commit [519251e](https://github.com/poodarchu/Det3D/tree/519251e72a5c1fdd58972eabeac67808676b9bb7), use `kitti_point_pillars_mghead_syncbn.py` and run - __Det3D__: At commit [519251e](https://github.com/poodarchu/Det3D/tree/519251e72a5c1fdd58972eabeac67808676b9bb7), use `kitti_point_pillars_mghead_syncbn.py` and run
```bash ```bash
./tools/scripts/train.sh --launcher=slurm --gpus=8 ./tools/scripts/train.sh --launcher=slurm --gpus=8
...@@ -241,13 +240,13 @@ We compare the training speed (samples/s) with other codebases if they implement ...@@ -241,13 +240,13 @@ We compare the training speed (samples/s) with other codebases if they implement
### PointPillars-3class ### PointPillars-3class
* __MMDetection3D__: With release v0.1.0, run - __MMDetection3D__: With release v0.1.0, run
```bash ```bash
./tools/dist_train.sh configs/benchmark/hv_pointpillars_secfpn_4x8_80e_pcdet_kitti-3d-3class.py 8 --no-validate ./tools/dist_train.sh configs/benchmark/hv_pointpillars_secfpn_4x8_80e_pcdet_kitti-3d-3class.py 8 --no-validate
``` ```
* __OpenPCDet__: At commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2), run - __OpenPCDet__: At commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2), run
```bash ```bash
cd tools cd tools
...@@ -258,13 +257,13 @@ We compare the training speed (samples/s) with other codebases if they implement ...@@ -258,13 +257,13 @@ We compare the training speed (samples/s) with other codebases if they implement
For SECOND, we mean the [SECONDv1.5](https://github.com/traveller59/second.pytorch/blob/master/second/configs/all.fhd.config) that was first implemented in [second.Pytorch](https://github.com/traveller59/second.pytorch). Det3D's implementation of SECOND uses its self-implemented Multi-Group Head, so its speed is not compatible with other codebases. For SECOND, we mean the [SECONDv1.5](https://github.com/traveller59/second.pytorch/blob/master/second/configs/all.fhd.config) that was first implemented in [second.Pytorch](https://github.com/traveller59/second.pytorch). Det3D's implementation of SECOND uses its self-implemented Multi-Group Head, so its speed is not compatible with other codebases.
* __MMDetection3D__: With release v0.1.0, run - __MMDetection3D__: With release v0.1.0, run
```bash ```bash
./tools/dist_train.sh configs/benchmark/hv_second_secfpn_4x8_80e_pcdet_kitti-3d-3class.py 8 --no-validate ./tools/dist_train.sh configs/benchmark/hv_second_secfpn_4x8_80e_pcdet_kitti-3d-3class.py 8 --no-validate
``` ```
* __OpenPCDet__: At commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2), run - __OpenPCDet__: At commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2), run
```bash ```bash
cd tools cd tools
...@@ -273,13 +272,13 @@ For SECOND, we mean the [SECONDv1.5](https://github.com/traveller59/second.pytor ...@@ -273,13 +272,13 @@ For SECOND, we mean the [SECONDv1.5](https://github.com/traveller59/second.pytor
### Part-A2 ### Part-A2
* __MMDetection3D__: With release v0.1.0, run - __MMDetection3D__: With release v0.1.0, run
```bash ```bash
./tools/dist_train.sh configs/benchmark/hv_PartA2_secfpn_4x8_cyclic_80e_pcdet_kitti-3d-3class.py 8 --no-validate ./tools/dist_train.sh configs/benchmark/hv_PartA2_secfpn_4x8_cyclic_80e_pcdet_kitti-3d-3class.py 8 --no-validate
``` ```
* __OpenPCDet__: At commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2), train the model by running - __OpenPCDet__: At commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2), train the model by running
```bash ```bash
cd tools cd tools
......
docs/en/changelog.md
View file @ cf922153
...@@ -51,7 +51,7 @@ A total of 11 developers contributed to this release. ...@@ -51,7 +51,7 @@ A total of 11 developers contributed to this release.
- We update some of the model checkpoints after the refactor of coordinate systems. Please stay tuned for the release of the remaining model checkpoints. - We update some of the model checkpoints after the refactor of coordinate systems. Please stay tuned for the release of the remaining model checkpoints.
| | Fully Updated | Partially Updated | In Progress | No Influcence | | | Fully Updated | Partially Updated | In Progress | No Influcence |
|--------------------|:-------------:|:--------:| :-----------: | :-----------: | | ------------- | :-----------: | :---------------: | :---------: | :-----------: |
| SECOND | | ✓ | | | | SECOND | | ✓ | | |
| PointPillars | | ✓ | | | | PointPillars | | ✓ | | |
| FreeAnchor | ✓ | | | | | FreeAnchor | ✓ | | | |
...@@ -60,19 +60,18 @@ A total of 11 developers contributed to this release. ...@@ -60,19 +60,18 @@ A total of 11 developers contributed to this release.
| 3DSSD | | ✓ | | | | 3DSSD | | ✓ | | |
| Part-A2 | ✓ | | | | | Part-A2 | ✓ | | | |
| MVXNet | ✓ | | | | | MVXNet | ✓ | | | |
| CenterPoint | | |✓ | | | CenterPoint | | | ✓ | |
| SSN | ✓ | | | | | SSN | ✓ | | | |
| ImVoteNet | ✓ | | | | | ImVoteNet | ✓ | | | |
| FCOS3D | | | |✓ | | FCOS3D | | | | ✓ |
| PointNet++ | | | |✓ | | PointNet++ | | | | ✓ |
| Group-Free-3D | | | |✓ | | Group-Free-3D | | | | ✓ |
| ImVoxelNet | ✓ | | | | | ImVoxelNet | ✓ | | | |
| PAConv | | | |✓ | | PAConv | | | | ✓ |
| DGCNN | | | |✓ | | DGCNN | | | | ✓ |
| SMOKE | | | |✓ | | SMOKE | | | | ✓ |
| PGD | | | |✓ | | PGD | | | | ✓ |
| MonoFlex | | | |✓ | | MonoFlex | | | | ✓ |
#### Highlights #### Highlights
...@@ -414,7 +413,6 @@ A total of 12 developers contributed to this release. ...@@ -414,7 +413,6 @@ A total of 12 developers contributed to this release.
@yinchimaoliang, @gopi231091, @filaPro, @ZwwWayne, @ZCMax, @hjin2902, @wHao-Wu, @Wuziyi616, @xiliu8006, @THU17cyz, @DCNSW, @Tai-Wang @yinchimaoliang, @gopi231091, @filaPro, @ZwwWayne, @ZCMax, @hjin2902, @wHao-Wu, @Wuziyi616, @xiliu8006, @THU17cyz, @DCNSW, @Tai-Wang
### v0.15.0 (1/7/2021) ### v0.15.0 (1/7/2021)
#### Compatibility #### Compatibility
...@@ -449,7 +447,6 @@ In order to fix the problem that the priority of EvalHook is too low, all hook p ...@@ -449,7 +447,6 @@ In order to fix the problem that the priority of EvalHook is too low, all hook p
- Add documentation for vision-only 3D detection (#669) - Add documentation for vision-only 3D detection (#669)
- Refine docs for Quick Run and Useful Tools (#686) - Refine docs for Quick Run and Useful Tools (#686)
#### Bug Fixes #### Bug Fixes
- Fix the bug of [BackgroundPointsFilter](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/pipelines/transforms_3d.py) using the bottom center of ground truth (#609) - Fix the bug of [BackgroundPointsFilter](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/pipelines/transforms_3d.py) using the bottom center of ground truth (#609)
...@@ -458,10 +455,10 @@ In order to fix the problem that the priority of EvalHook is too low, all hook p ...@@ -458,10 +455,10 @@ In order to fix the problem that the priority of EvalHook is too low, all hook p
- Fix test commands in docs and make some refinements (#635) - Fix test commands in docs and make some refinements (#635)
- Fix wrong config paths in unit tests (#641) - Fix wrong config paths in unit tests (#641)
### v0.14.0 (1/6/2021) ### v0.14.0 (1/6/2021)
#### Highlights #### Highlights
- Support the point cloud segmentation method [PointNet++](https://arxiv.org/abs/1706.02413) - Support the point cloud segmentation method [PointNet++](https://arxiv.org/abs/1706.02413)
#### New Features #### New Features
...@@ -482,16 +479,17 @@ In order to fix the problem that the priority of EvalHook is too low, all hook p ...@@ -482,16 +479,17 @@ In order to fix the problem that the priority of EvalHook is too low, all hook p
- Remove a useless parameter `label_weight` from segmentation datasets including `Custom3DSegDataset`, `ScanNetSegDataset` and `S3DISSegDataset` (#607) - Remove a useless parameter `label_weight` from segmentation datasets including `Custom3DSegDataset`, `ScanNetSegDataset` and `S3DISSegDataset` (#607)
#### Bug Fixes #### Bug Fixes
- Fix a corrupted lidar data file in Lyft dataset in [data_preparation](https://github.com/open-mmlab/mmdetection3d/tree/master/docs/data_preparation.md) (#546) - Fix a corrupted lidar data file in Lyft dataset in [data_preparation](https://github.com/open-mmlab/mmdetection3d/tree/master/docs/data_preparation.md) (#546)
- Fix evaluation bugs in nuScenes and Lyft dataset (#549) - Fix evaluation bugs in nuScenes and Lyft dataset (#549)
- Fix converting points between coordinates with specific transformation matrix in the [coord_3d_mode.py](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/bbox/structures/coord_3d_mode.py) (#556) - Fix converting points between coordinates with specific transformation matrix in the [coord_3d_mode.py](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/bbox/structures/coord_3d_mode.py) (#556)
- Support PointPillars models on Lyft dataset (#578) - Support PointPillars models on Lyft dataset (#578)
- Fix the bug of demo with pre-trained VoteNet model on ScanNet (#600) - Fix the bug of demo with pre-trained VoteNet model on ScanNet (#600)
### v0.13.0 (1/5/2021) ### v0.13.0 (1/5/2021)
#### Highlights #### Highlights
- Support a monocular 3D detection method [FCOS3D](https://arxiv.org/abs/2104.10956) - Support a monocular 3D detection method [FCOS3D](https://arxiv.org/abs/2104.10956)
- Support ScanNet and S3DIS semantic segmentation dataset - Support ScanNet and S3DIS semantic segmentation dataset
- Enhancement of visualization tools for dataset browsing and demos, including support of visualization for multi-modality data and point cloud segmentation. - Enhancement of visualization tools for dataset browsing and demos, including support of visualization for multi-modality data and point cloud segmentation.
...@@ -746,7 +744,7 @@ In order to fix the problem that the priority of EvalHook is too low, all hook p ...@@ -746,7 +744,7 @@ In order to fix the problem that the priority of EvalHook is too low, all hook p
- Support Batch Inference (#95, #103, #116): MMDetection3D v0.6.0 migrates to support batch inference based on MMDetection >= v2.4.0. This change influences all the test APIs in MMDetection3D and downstream codebases. - Support Batch Inference (#95, #103, #116): MMDetection3D v0.6.0 migrates to support batch inference based on MMDetection >= v2.4.0. This change influences all the test APIs in MMDetection3D and downstream codebases.
- Start to use collect environment function from MMCV (#113): MMDetection3D v0.6.0 migrates to use `collect_env` function in MMCV. - Start to use collect environment function from MMCV (#113): MMDetection3D v0.6.0 migrates to use `collect_env` function in MMCV.
`get_compiler_version` and `get_compiling_cuda_version` compiled in `mmdet3d.ops.utils` are removed. Please import these two functions from `mmcv.ops`. `get_compiler_version` and `get_compiling_cuda_version` compiled in `mmdet3d.ops.utils` are removed. Please import these two functions from `mmcv.ops`.
#### New Features #### New Features
......
docs/en/compatibility.md
View file @ cf922153
...@@ -10,46 +10,45 @@ In this version we did a major code refactoring that boosted the performance of ...@@ -10,46 +10,45 @@ In this version we did a major code refactoring that boosted the performance of
Meanwhile, we also fixed the imprecise timestamps saving issue in waymo dataset conversion. This change introduces following backward compatibility breaks: Meanwhile, we also fixed the imprecise timestamps saving issue in waymo dataset conversion. This change introduces following backward compatibility breaks:
- The point cloud .bin files of waymo dataset need to be regenerated. - The point cloud .bin files of waymo dataset need to be regenerated.
In the .bin files each point occupies 6 `float32` and the meaning of the last `float32` now changed from **imprecise timestamps** to **range frame offset**. In the .bin files each point occupies 6 `float32` and the meaning of the last `float32` now changed from **imprecise timestamps** to **range frame offset**.
The **range frame offset** for each point is calculated as`ri * h * w + row * w + col` if the point is from the **TOP** lidar or `-1` otherwise. The **range frame offset** for each point is calculated as`ri * h * w + row * w + col` if the point is from the **TOP** lidar or `-1` otherwise.
The `h`, `w` denote the height and width of the TOP lidar's range frame. The `h`, `w` denote the height and width of the TOP lidar's range frame.
The `ri`, `row`, `col` denote the return index, the row and the column of the range frame where each point locates. The `ri`, `row`, `col` denote the return index, the row and the column of the range frame where each point locates.
Following tables show the difference across the change: Following tables show the difference across the change:
Before Before
| Element offset (float32) | 0 | 1 | 2 | 3 | 4 | 5 | | Element offset (float32) | 0 | 1 | 2 | 3 | 4 | 5 |
|--------------------------|:---:|:---:|:---:|:---------:|:----------:|:-----------------------:| | ------------------------ | :-: | :-: | :-: | :-------: | :--------: | :---------------------: |
| Bytes offset | 0 | 4 | 8 | 12 | 16 | 20 | | Bytes offset | 0 | 4 | 8 | 12 | 16 | 20 |
| Meaning | x | y | z | intensity | elongation | **imprecise timestamp** | | Meaning | x | y | z | intensity | elongation | **imprecise timestamp** |
After After
| Element offset (float32) | 0 | 1 | 2 | 3 | 4 | 5 | | Element offset (float32) | 0 | 1 | 2 | 3 | 4 | 5 |
|--------------------------|:---:|:---:|:---:|:---------:|:----------:|:----------------------:| | ------------------------ | :-: | :-: | :-: | :-------: | :--------: | :--------------------: |
| Bytes offset | 0 | 4 | 8 | 12 | 16 | 20 | | Bytes offset | 0 | 4 | 8 | 12 | 16 | 20 |
| Meaning | x | y | z | intensity | elongation | **range frame offset** | | Meaning | x | y | z | intensity | elongation | **range frame offset** |
- The objects' point cloud .bin files in the GT-database of waymo dataset need to be regenerated because we also dumped the range frame offset for each point into it. - The objects' point cloud .bin files in the GT-database of waymo dataset need to be regenerated because we also dumped the range frame offset for each point into it.
Following tables show the difference across the change: Following tables show the difference across the change:
Before Before
| Element offset (float32) | 0 | 1 | 2 | 3 | 4 | | Element offset (float32) | 0 | 1 | 2 | 3 | 4 |
|--------------------------|:---:|:---:|:---:|:---------:|:----------:| | ------------------------ | :-: | :-: | :-: | :-------: | :--------: |
| Bytes offset | 0 | 4 | 8 | 12 | 16 | | Bytes offset | 0 | 4 | 8 | 12 | 16 |
| Meaning | x | y | z | intensity | elongation | | Meaning | x | y | z | intensity | elongation |
After After
| Element offset (float32) | 0 | 1 | 2 | 3 | 4 | 5 | | Element offset (float32) | 0 | 1 | 2 | 3 | 4 | 5 |
|--------------------------|:---:|:---:|:---:|:---------:|:----------:|:----------------------:| | ------------------------ | :-: | :-: | :-: | :-------: | :--------: | :--------------------: |
| Bytes offset | 0 | 4 | 8 | 12 | 16 | 20 | | Bytes offset | 0 | 4 | 8 | 12 | 16 | 20 |
| Meaning | x | y | z | intensity | elongation | **range frame offset** | | Meaning | x | y | z | intensity | elongation | **range frame offset** |
- Any configuration that uses waymo dataset with GT Augmentation should change the `db_sampler.points_loader.load_dim` from `5` to `6`. - Any configuration that uses waymo dataset with GT Augmentation should change the `db_sampler.points_loader.load_dim` from `5` to `6`.
## v1.0.0rc0 ## v1.0.0rc0
### Coordinate system refactoring ### Coordinate system refactoring
...@@ -63,6 +62,7 @@ In this version, we did a major code refactoring which improved the consistency ...@@ -63,6 +62,7 @@ In this version, we did a major code refactoring which improved the consistency
#### ***NOTICE!!*** #### ***NOTICE!!***
Since definitions of box representation have changed, the annotation data of most datasets require updating: Since definitions of box representation have changed, the annotation data of most datasets require updating:
- SUN RGB-D: Yaw angles in the annotation should be reversed. - SUN RGB-D: Yaw angles in the annotation should be reversed.
- KITTI: For LiDAR boxes in GT databases, (x_size, y_size, z_size, yaw) out of (x, y, z, x_size, y_size, z_size) should be converted from the old LiDAR coordinate system to the new one. The training/validation data annotations should be left unchanged since they are under the Camera coordinate system, which is unmodified after the refactoring. - KITTI: For LiDAR boxes in GT databases, (x_size, y_size, z_size, yaw) out of (x, y, z, x_size, y_size, z_size) should be converted from the old LiDAR coordinate system to the new one. The training/validation data annotations should be left unchanged since they are under the Camera coordinate system, which is unmodified after the refactoring.
- Waymo: Same as KITTI. - Waymo: Same as KITTI.
...@@ -88,7 +88,6 @@ Functions only involving points are generally unaffected except if they rely on ...@@ -88,7 +88,6 @@ Functions only involving points are generally unaffected except if they rely on
- Data augmentation utils in [data_augment_utils.py](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/mmdet3d/datasets/pipelines/data_augment_utils.py) now follow the rules of a right-handed system. - Data augmentation utils in [data_augment_utils.py](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/mmdet3d/datasets/pipelines/data_augment_utils.py) now follow the rules of a right-handed system.
- We do not need the yaw hacking in KITTI anymore after refining [`get_direction_target`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/mmdet3d/models/dense_heads/train_mixins.py). Interested users may refer to PR [#677](https://github.com/open-mmlab/mmdetection3d/pull/677) . - We do not need the yaw hacking in KITTI anymore after refining [`get_direction_target`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/mmdet3d/models/dense_heads/train_mixins.py). Interested users may refer to PR [#677](https://github.com/open-mmlab/mmdetection3d/pull/677) .
## 0.16.0 ## 0.16.0
### Returned values of `QueryAndGroup` operation ### Returned values of `QueryAndGroup` operation
...@@ -168,4 +167,4 @@ Please refer to the SUNRGBD [README.md](https://github.com/open-mmlab/mmdetectio ...@@ -168,4 +167,4 @@ Please refer to the SUNRGBD [README.md](https://github.com/open-mmlab/mmdetectio
### VoteNet and H3DNet model structure update ### VoteNet and H3DNet model structure update
In MMDetection 0.6.0, we updated the model structures of VoteNet and H3DNet, therefore model checkpoints generated by MMDetection < 0.6.0 should be first converted to a format compatible with the latest structures via [convert_votenet_checkpoints.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/model_converters/convert_votenet_checkpoints.py) and [convert_h3dnet_checkpoints.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/model_converters/convert_h3dnet_checkpoints.py) . For more details, please refer to the VoteNet [README.md](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/votenet/README.md/) and H3DNet [README.md](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/h3dnet/README.md/). In MMDetection 0.6.0, we updated the model structures of VoteNet and H3DNet, therefore model checkpoints generated by MMDetection \< 0.6.0 should be first converted to a format compatible with the latest structures via [convert_votenet_checkpoints.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/model_converters/convert_votenet_checkpoints.py) and [convert_h3dnet_checkpoints.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/model_converters/convert_h3dnet_checkpoints.py) . For more details, please refer to the VoteNet [README.md](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/votenet/README.md/) and H3DNet [README.md](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/h3dnet/README.md/).
docs/en/datasets/kitti_det.md
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...@@ -88,16 +88,16 @@ kitti ...@@ -88,16 +88,16 @@ kitti
- `kitti_gt_database/xxxxx.bin`: point cloud data included in each 3D bounding box of the training dataset - `kitti_gt_database/xxxxx.bin`: point cloud data included in each 3D bounding box of the training dataset
- `kitti_infos_train.pkl`: training dataset infos, each frame info contains following details: - `kitti_infos_train.pkl`: training dataset infos, each frame info contains following details:
- info['point_cloud']: {'num_features': 4, 'velodyne_path': velodyne_path}. - info\['point_cloud'\]: {'num_features': 4, 'velodyne_path': velodyne_path}.
- info['annos']: { - info\['annos'\]: {
- location: x,y,z are bottom center in referenced camera coordinate system (in meters), an Nx3 array - location: x,y,z are bottom center in referenced camera coordinate system (in meters), an Nx3 array
- dimensions: height, width, length (in meters), an Nx3 array - dimensions: height, width, length (in meters), an Nx3 array
- rotation_y: rotation ry around Y-axis in camera coordinates [-pi..pi], an N array - rotation_y: rotation ry around Y-axis in camera coordinates \[-pi..pi\], an N array
- name: ground truth name array, an N array - name: ground truth name array, an N array
- difficulty: kitti difficulty, Easy, Moderate, Hard - difficulty: kitti difficulty, Easy, Moderate, Hard
- group_ids: used for multi-part object - group_ids: used for multi-part object
} }
- (optional) info['calib']: { - (optional) info\['calib'\]: {
- P0: camera0 projection matrix after rectification, an 3x4 array - P0: camera0 projection matrix after rectification, an 3x4 array
- P1: camera1 projection matrix after rectification, an 3x4 array - P1: camera1 projection matrix after rectification, an 3x4 array
- P2: camera2 projection matrix after rectification, an 3x4 array - P2: camera2 projection matrix after rectification, an 3x4 array
...@@ -106,9 +106,9 @@ kitti ...@@ -106,9 +106,9 @@ kitti
- Tr_velo_to_cam: transformation from Velodyne coordinate to camera coordinate, an 4x4 array - Tr_velo_to_cam: transformation from Velodyne coordinate to camera coordinate, an 4x4 array
- Tr_imu_to_velo: transformation from IMU coordinate to Velodyne coordinate, an 4x4 array - Tr_imu_to_velo: transformation from IMU coordinate to Velodyne coordinate, an 4x4 array
} }
- (optional) info['image']:{'image_idx': idx, 'image_path': image_path, 'image_shape', image_shape}. - (optional) info\['image'\]:{'image_idx': idx, 'image_path': image_path, 'image_shape', image_shape}.
**Note:** the info['annos'] is in the referenced camera coordinate system. More details please refer to [this](http://www.cvlibs.net/publications/Geiger2013IJRR.pdf) **Note:** the info\['annos'\] is in the referenced camera coordinate system. More details please refer to [this](http://www.cvlibs.net/publications/Geiger2013IJRR.pdf)
The core function to get kitti_infos_xxx.pkl and kitti_infos_xxx_mono3d.coco.json are [get_kitti_image_info](https://github.com/open-mmlab/mmdetection3d/blob/7873c8f62b99314f35079f369d1dab8d63f8a3ce/tools/data_converter/kitti_data_utils.py#L140) and [get_2d_boxes](https://github.com/open-mmlab/mmdetection3d/blob/7873c8f62b99314f35079f369d1dab8d63f8a3ce/tools/data_converter/kitti_converter.py#L378). Please refer to [kitti_converter.py](https://github.com/open-mmlab/mmdetection3d/blob/7873c8f62b99314f35079f369d1dab8d63f8a3ce/tools/data_converter/kitti_converter.py) for more details. The core function to get kitti_infos_xxx.pkl and kitti_infos_xxx_mono3d.coco.json are [get_kitti_image_info](https://github.com/open-mmlab/mmdetection3d/blob/7873c8f62b99314f35079f369d1dab8d63f8a3ce/tools/data_converter/kitti_data_utils.py#L140) and [get_2d_boxes](https://github.com/open-mmlab/mmdetection3d/blob/7873c8f62b99314f35079f369d1dab8d63f8a3ce/tools/data_converter/kitti_converter.py#L378). Please refer to [kitti_converter.py](https://github.com/open-mmlab/mmdetection3d/blob/7873c8f62b99314f35079f369d1dab8d63f8a3ce/tools/data_converter/kitti_converter.py) for more details.
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docs/en/datasets/lyft_det.md
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...@@ -90,19 +90,19 @@ Next, we will elaborate on the difference compared to nuScenes in terms of the d ...@@ -90,19 +90,19 @@ Next, we will elaborate on the difference compared to nuScenes in terms of the d
- without `lyft_database/xxxxx.bin`: This folder and `.bin` files are not extracted on the Lyft dataset due to the negligible effect of ground-truth sampling in the experiments. - without `lyft_database/xxxxx.bin`: This folder and `.bin` files are not extracted on the Lyft dataset due to the negligible effect of ground-truth sampling in the experiments.
- `lyft_infos_train.pkl`: training dataset infos, each frame info has two keys: `metadata` and `infos`. - `lyft_infos_train.pkl`: training dataset infos, each frame info has two keys: `metadata` and `infos`.
`metadata` contains the basic information for the dataset itself, such as `{'version': 'v1.01-train'}`, while `infos` contains the detailed information the same as nuScenes except for the following details: `metadata` contains the basic information for the dataset itself, such as `{'version': 'v1.01-train'}`, while `infos` contains the detailed information the same as nuScenes except for the following details:
- info['sweeps']: Sweeps information. - info\['sweeps'\]: Sweeps information.
- info['sweeps'][i]['type']: The sweep data type, e.g., `'lidar'`. - info\['sweeps'\]\[i\]\['type'\]: The sweep data type, e.g., `'lidar'`.
Lyft has different LiDAR settings for some samples, but we always take only the points collected by the top LiDAR for the consistency of data distribution. Lyft has different LiDAR settings for some samples, but we always take only the points collected by the top LiDAR for the consistency of data distribution.
- info['gt_names']: There are 9 categories on the Lyft dataset, and the imbalance of annotations for different categories is even more significant than nuScenes. - info\['gt_names'\]: There are 9 categories on the Lyft dataset, and the imbalance of annotations for different categories is even more significant than nuScenes.
- without info['gt_velocity']: There is no velocity measurement on Lyft. - without info\['gt_velocity'\]: There is no velocity measurement on Lyft.
- info['num_lidar_pts']: Set to -1 by default. - info\['num_lidar_pts'\]: Set to -1 by default.
- info['num_radar_pts']: Set to 0 by default. - info\['num_radar_pts'\]: Set to 0 by default.
- without info['valid_flag']: This flag does recorded due to invalid `num_lidar_pts` and `num_radar_pts`. - without info\['valid_flag'\]: This flag does recorded due to invalid `num_lidar_pts` and `num_radar_pts`.
- `nuscenes_infos_train_mono3d.coco.json`: training dataset coco-style info. This file only contains 2D information, without the information required by 3D detection, such as camera intrinsics. - `nuscenes_infos_train_mono3d.coco.json`: training dataset coco-style info. This file only contains 2D information, without the information required by 3D detection, such as camera intrinsics.
- info['images']: A list containing all the image info. - info\['images'\]: A list containing all the image info.
- only containing `'file_name'`, `'id'`, `'width'`, `'height'`. - only containing `'file_name'`, `'id'`, `'width'`, `'height'`.
- info['annotations']: A list containing all the annotation info. - info\['annotations'\]: A list containing all the annotation info.
- only containing `'file_name'`, `'image_id'`, `'area'`, `'category_name'`, `'category_id'`, `'bbox'`, `'is_crowd'`, `'segmentation'`, `'id'`, where `'is_crowd'`, `'segmentation'` are set to `0` and `[]` by default. - only containing `'file_name'`, `'image_id'`, `'area'`, `'category_name'`, `'category_id'`, `'bbox'`, `'is_crowd'`, `'segmentation'`, `'id'`, where `'is_crowd'`, `'segmentation'` are set to `0` and `[]` by default.
There is no attribute annotation on Lyft. There is no attribute annotation on Lyft.
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