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[Docs] Update all the metafiles acoording to README (#2006)
* update centerpoint, dgcnn, fcos3d and free_anchor metafile * check all metafile in configs * update centerpoint, pointpillars and second yml * rename PartA2 to parta2 in the name of config * update metafile in nuimages * update readme in nuimages
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‎.dev_scripts/test_benchmark.sh

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$CHECKPOINT_DIR/configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py/latest.pth --eval map \
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2>&1|tee $CHECKPOINT_DIR/configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py/FULL_LOG.txt &
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echo 'configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py' &
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mkdir -p $CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py
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GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py \
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$CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/latest.pth --eval map \
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2>&1|tee $CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/FULL_LOG.txt &
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echo 'configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py' &
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mkdir -p $CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py
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GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py \
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$CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/latest.pth --eval map \
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2>&1|tee $CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/FULL_LOG.txt &
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echo 'configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py' &
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mkdir -p $CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py

‎.dev_scripts/train_benchmark.sh

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$CHECKPOINT_DIR/configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
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2>&1|tee $CHECKPOINT_DIR/configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py/FULL_LOG.txt &
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echo 'configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py' &
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mkdir -p $CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py
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GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py \
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$CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
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2>&1|tee $CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/FULL_LOG.txt &
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echo 'configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py' &
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mkdir -p $CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py
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GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py \
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$CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
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2>&1|tee $CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/FULL_LOG.txt &
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echo 'configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py' &
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mkdir -p $CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py

‎configs/centerpoint/README.md

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‎configs/centerpoint/metafile.yml

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Version: v0.6.0
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Models:
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- Name: centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus
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- Name: centerpoint_voxel01_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d
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In Collection: CenterPoint
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Config: configs/centerpoint/centerpoint_voxel01_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d.py
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Metadata:
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Training Memory (GB): 4.9
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metadata:
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Training Memory (GB): 5.2
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Results:
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- Task: 3D Object Detection
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Dataset: nuScenes
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Metrics:
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mAP: 56.19
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NDS: 64.43
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Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20201001_135205-5db91e00.pth
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mAP: 56.11
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NDS: 64.61
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Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220810_030004-9061688e.pth
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- Name: centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus
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- Name: centerpoint_voxel01_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d
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In Collection: CenterPoint
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Config: configs/centerpoint/centerpoint_voxel01_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py
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Metadata:
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Training Memory (GB): 5.2
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Training Memory (GB): 5.5
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Results:
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- Task: 3D Object Detection
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Dataset: nuScenes
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Metrics:
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mAP: 56.34
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NDS: 64.81
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Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20201004_075317-26d8176c.pth
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mAP: 56.10
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NDS: 64.69
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Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20220810_052355-a6928835.pth
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- Name: centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus
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- Name: centerpoint_voxel0075_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d
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In Collection: CenterPoint
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Config: configs/centerpoint/centerpoint_voxel0075_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d.py
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Metadata:
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Training Memory (GB): 7.8
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Training Memory (GB): 8.2
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Results:
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- Task: 3D Object Detection
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Dataset: nuScenes
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Metrics:
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mAP: 57.34
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NDS: 65.23
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Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20200925_230905-358fbe3b.pth
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mAP: 56.54
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NDS: 65.17
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Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220810_011659-04cb3a3b.pth
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- Name: centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus
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- Name: centerpoint_voxel0075_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d
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In Collection: CenterPoint
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Config: configs/centerpoint/centerpoint_voxel0075_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py
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Metadata:
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Training Memory (GB): 8.5
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Training Memory (GB): 8.7
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Results:
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- Task: 3D Object Detection
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Dataset: nuScenes
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mAP: 57.27
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NDS: 65.58
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Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20200930_201619-67c8496f.pth
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mAP: 56.92
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NDS: 65.27
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Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20220810_025930-657f67e0.pth
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- Name: centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus
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- Name: centerpoint_pillar02_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d
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In Collection: CenterPoint
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Config: configs/centerpoint/centerpoint_pillar02_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d.py
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Metadata:
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Training Memory (GB): 4.4
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Training Memory (GB): 4.6
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Results:
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- Task: 3D Object Detection
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Dataset: nuScenes
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Metrics:
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mAP: 49.07
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NDS: 59.66
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Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20201004_170716-a134a233.pth
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mAP: 48.70
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NDS: 59.62
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Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20220811_031844-191a3822.pth
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- Name: centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus
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- Name: centerpoint_pillar02_second_secfpn_head-dcn_8xb4-cyclic-20e_nus-3d
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In Collection: CenterPoint
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Config: configs/centerpoint/centerpoint_pillar02_second_secfpn_head-dcn_8xb4-cyclic-20e_nus-3d.py
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Training Memory (GB): 4.6
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Training Memory (GB): 4.9
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- Task: 3D Object Detection
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Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus_20200930_103722-3bb135f2.pth
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mAP: 48.38
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NDS: 59.79
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Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus_20220811_045458-808e69ad.pth

‎configs/fcos3d/README.md

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| Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP | NDS | Download |
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| :-------------------------------------------------------------------------------------: | :-----: | :------: | :------------: | :--: | :--: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| \[ResNet101 w/ DCN\](./fcos3d_r101-caffe- fpn-head-gn-dcn_8xb2-1x_nus-mono3d.py) | 1x | 8.69 | | 29.8 | 37.7 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210715_235813-4bed5239.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210715_235813.log.json) |
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| \[ResNet101 w/ DCN\](./fcos3d_r101-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d.py) | 1x | 8.69 | | 29.8 | 37.7 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210715_235813-4bed5239.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210715_235813.log.json) |
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| [above w/ finetune](./fcos3d_r101-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d_finetune.py) | 1x | 8.69 | | 32.1 | 39.5 | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210717_095645-8d806dc2.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210717_095645.log.json) |
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| above w/ tta | 1x | 8.69 | | 33.1 | 40.3 | |
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‎configs/fcos3d/metafile.yml

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Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210425_181341-8d5a21fe.pth
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Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_20210715_235813-4bed5239.pth
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mAP: 32.1
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NDS: 39.3
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Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210427_091419-35aaaad0.pth
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Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210717_095645-8d806dc2.pth

‎configs/free_anchor/metafile.yml

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- Name: hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d
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- Name: pointpillars_hv_fpn_sbn-all_8xb4-2x_nus-3d
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In Collection: FreeAnchor
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Config: pointpillars/pointpillars_hv_fpn_sbn-all_8xb4-2x_nus-3d.py
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Metadata:
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Training Memory (GB): 17.1
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Results:
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- Task: 3D Object Detection
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Dataset: nuScenes
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Metrics:
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mAP: 40.0
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NDS: 53.3
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Weights: 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
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- Name: pointpillars_hv_fpn_head-free-anchor_sbn-all_8xb4-2x_nus-3d
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Config: free_anchor/pointpillars_hv_fpn_head-free-anchor_sbn-all_8xb4-2x_nus-3d.py
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Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210816_163441-ae0897e7.pth
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- Name: pointpillars_hv_regnet-400mf_fpn_sbn-all_8xb4-2x_nus-3d
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In Collection: FreeAnchor
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Config: configs/regnet/pointpillars_hv_regnet-400mf_fpn_sbn-all_8xb4-2x_nus-3d.py
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Metadata:
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Training Memory (GB): 17.3
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Results:
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- Task: 3D Object Detection
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Dataset: nuScenes
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Metrics:
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mAP: 44.8
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NDS: 56.4
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Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210816_163441-ae0897e7.pth
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- Name: hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d
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- Name: pointpillars_hv_regnet-400mf_fpn_head-free-anchor_sbn-all_8xb4-2x_nus-3d
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Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210828_025608-bfbd506e.pth
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- Name: hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d
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- Name: pointpillars_hv_regnet-1.6gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d
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- Name: pointpillars_hv_regnet-3.2gf_fpn_head-free-anchor_sbn-all_8xb4-2x_nus-3d
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Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20210827_181237-e385c35a.pth
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‎configs/monoflex/metafile.yml

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‎configs/nuimages/README.md

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# NuImages Results
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# Mask R-CNN
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<!-- [DATASET] -->
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> [Mask R-CNN](https://arxiv.org/abs/1703.06870)
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<!-- [ALGORITHM] -->
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## Abstract
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We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without bells and whistles, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition.
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<div align=center>
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<img src="https://user-images.githubusercontent.com/40661020/143967081-c2552bed-9af2-46c4-ae44-5b3b74e5679f.png"/>
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</div>
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## Introduction
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