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Nuscenes Train and Eval guide

General Tips

  • Nuscenes dataset evaluation contains many hard examples, you need to modify nms parameters (decrease score threshold, increase max size). You can use v1.0-mini to tune them.

  • Nuscenes dataset contain sweeps. You need to use 10 sweeps if you want to get good detection scores. Key-frame only can't get good result, so I drop support for that.

  • Nuscenes dataset contains 28130 train samples and 6019 validation samples. Use Nuscenes mini train set (my custom split, ~3500 samples) when develop if you don't have 4+ GPUs. See NuscenesDatasetD8 for more details.

  • Some data augmentation will harm detection performance such as global rotation if their value is too large.

  • Use KITTI pretrain model if possible. You can use a pointpillars xyres_16 car model in google drive as pretrained model.

  • Multi-class NMS can increase performance of large objects. The parameters of mcnms should be grid-searched, don't enable mcmns during develop.

  • Database sample can greatly increase performance of bus. Currently no effect on other class.

Config Guide

Anchor Generator

  1. use get_all_box_mean in nuscenes_dataset.py to get mean values of all boxes for each class.

  2. change size and z-center in anchor_ranges in anchor_generator_range.

  3. choose thresholds: use helper_tune_target_assigner to get instance count and assigned anchors count. Then tune them.

  4. add region_similarity_calculator. If your anchors are too sparse, you need to use distance_similarity instead of nearest_iou_similarity for small classes such as pedestrian.

  5. If you want to train with velocity, add custom_values to anchor generator. you can add two zeros. After that, anchors' shape will become [N, 9].

Preprocess

  1. disable all ground-truth noise.

  2. global_rotation_uniform_noise may decrease performance.

  3. disable database_sampler by delete all content in database_sampler.

Train

Use set_train_step in utils.config_tool.train if you don't want to calculate them manually.

Develop Guide

  • uncomment vis functions in prep_pointcloud to see assigned anchors and point cloud after data augmentation to ensure no bug in preprocess.

  • use code such as code in script_server.py instead of use commands in terminal.

Config reference

  • all.fhd.config

  1. Block filtering is enabled by default to decrease number of voxels.

  2. Use upsample_strides: [0.5] to decrease number of anchors and increase training speed.

  • all.pp.mhead.config

This config use custom VoxelNet class.

  1. Use VoxelNetNuscenesMultiHead and write a multi-head network to handle small objects.

  2. Output of multi head network MUST match order of class settings in config file.

  3. If you use custom VoxelNet class, you MUST set feature_map_size for every class in class settings.

  • all.pp.largea.config

This config use only large anchors.

  1. you can use no_anchor to disable anchor generation of a class.

  2. you must set assign_per_class to false when use no_anchor.

Reference Performance (Single GPU)

  • all.pp.lowa.config: 30 epoch, 1/2 dataset (NuscenesDatasetD2), train speed: 12 sample/s, ~50000 anchors
car Nusc dist AP@0.5, 1.0, 2.0, 4.0
58.85, 76.12, 80.65, 82.49
bicycle Nusc dist AP@0.5, 1.0, 2.0, 4.0
0.00, 0.00, 0.00, 0.00
bus Nusc dist AP@0.5, 1.0, 2.0, 4.0
2.55, 15.42, 27.19, 32.03
construction_vehicle Nusc dist AP@0.5, 1.0, 2.0, 4.0
0.00, 0.00, 0.02, 0.31
motorcycle Nusc dist AP@0.5, 1.0, 2.0, 4.0
8.61, 14.30, 15.00, 15.53
pedestrian Nusc dist AP@0.5, 1.0, 2.0, 4.0
39.14, 49.29, 53.50, 57.03
traffic_cone Nusc dist AP@0.5, 1.0, 2.0, 4.0
12.58, 18.92, 22.79, 27.99
trailer Nusc dist AP@0.5, 1.0, 2.0, 4.0
0.00, 1.10, 7.42, 20.91
truck Nusc dist AP@0.5, 1.0, 2.0, 4.0
5.44, 15.78, 22.77, 27.05
barrier Nusc dist AP@0.5, 1.0, 2.0, 4.0
7.54, 34.54, 44.52, 49.80
  • all.pp.mida.config: 30 epoch, 1/2 dataset (NuscenesDatasetD2), train speed: 10 sample/s, ~200000 anchors

car Nusc dist AP@0.5, 1.0, 2.0, 4.0 61.91, 76.75, 80.94, 82.53 bicycle Nusc dist AP@0.5, 1.0, 2.0, 4.0 0.00, 0.00, 0.00, 0.00 bus Nusc dist AP@0.5, 1.0, 2.0, 4.0 8.72, 25.76, 39.13, 42.55 construction_vehicle Nusc dist AP@0.5, 1.0, 2.0, 4.0 0.00, 0.00, 0.27, 1.18 motorcycle Nusc dist AP@0.5, 1.0, 2.0, 4.0 12.62, 17.82, 18.31, 18.82 pedestrian Nusc dist AP@0.5, 1.0, 2.0, 4.0 55.50, 58.74, 61.27, 63.93 traffic_cone Nusc dist AP@0.5, 1.0, 2.0, 4.0 17.47, 20.30, 23.56, 28.63 trailer Nusc dist AP@0.5, 1.0, 2.0, 4.0 0.00, 4.76, 18.82, 28.26 truck Nusc dist AP@0.5, 1.0, 2.0, 4.0 8.39, 20.62, 27.37, 31.12 barrier Nusc dist AP@0.5, 1.0, 2.0, 4.0 10.61, 31.31, 40.60, 46.33

  • all.pp.config: 50 epoch, 1/8 dataset (NuscenesDatasetD8), train speed: 4 sample/s, ~1200000 anchors
car Nusc dist AP@0.5, 1.0, 2.0, 4.0
62.90, 73.07, 76.77, 78.79
bicycle Nusc dist AP@0.5, 1.0, 2.0, 4.0
0.00, 0.00, 0.00, 0.00
bus Nusc dist AP@0.5, 1.0, 2.0, 4.0
9.53, 26.17, 38.01, 40.60
construction_vehicle Nusc dist AP@0.5, 1.0, 2.0, 4.0
0.00, 0.00, 0.44, 1.43
motorcycle Nusc dist AP@0.5, 1.0, 2.0, 4.0
9.25, 12.90, 13.69, 14.11
pedestrian Nusc dist AP@0.5, 1.0, 2.0, 4.0
61.44, 62.61, 64.09, 66.35
traffic_cone Nusc dist AP@0.5, 1.0, 2.0, 4.0
11.63, 13.14, 15.81, 21.22
trailer Nusc dist AP@0.5, 1.0, 2.0, 4.0
0.80, 9.90, 17.61, 23.26
truck Nusc dist AP@0.5, 1.0, 2.0, 4.0
9.81, 21.40, 27.55, 30.34
  • all.fhd.config: train speed: 5.5 sample/s. comming soon