This is the official implementation of the paper "Reliable Student: Addressing Noise in Semi-Supervised 3D Object Detection".
Evaluation results on the KITTI dataset.
| Labeled Percent | 1% | 2% | ||||
|---|---|---|---|---|---|---|
| Method | Car | Pedestrian | Cyclist | Car | Pedestrian | Cyclist |
| PV-RCNN | 74.1 | 31.7 | 28.8 | 76.8 | 40.4 | 42.3 |
| 3DIoUMatch (Baseline) | 76.4 | 35.7 | 36.0 | 78.9 | 47.0 | 53.3 |
| ReliableStudent | 77.0 | 41.9 | 36.4 | 79.5 | 53.0 | 59.0 |
# Create a conda environment
conda create -n reliable_student python=3.8
conda activate reliable_student
# Install PyTorch 1.7.1 with CUDA 11.0
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=11.0 -c pytorch
# Install spconv 1.2.1
git clone -b v1.2.1 https://github.com/traveller59/spconv.git --recursive
cd spconv
python setup.py bdist_wheel
python -m pip install ./dist/spconv-1.2.1-cp38-cp38-linux_x86_64.whl
cd ..
# Clone the repo, install requirements and compile
git clone https://github.com/fnozarian/ReliableStudent.git
cd ReliableStudent
python -m pip install -r requirements.txt
python setup.py developPlease refer to Data Preparation to generate the data splits or use the baseline data we provide.
# Single GPU (e.g., A100-80GB)
python -u pretrain.py \
--cfg_file ./cfgs/kitti_models/pv_rcnn.yaml \
--batch_size 8 \
--split train_0.01_1 \
--extra_tag train_0.01_1 \
--ckpt_save_interval 4 \
--repeat 10 \
--dbinfos kitti_dbinfos_train_0.01_1_37.pkl
# Multi-GPU with Slurm (e.g., A100-40GB x2)
srun -p A100-40GB --job-name=pretrain_0.01_1 --ntasks=2 \
--gpus-per-task=1 --cpus-per-task=3 \
--mem=60GB --kill-on-bad-exit=1 python -u pretrain.py \
--cfg_file ./cfgs/kitti_models/pv_rcnn.yaml --batch_size 8 \
--launcher slurm --split train_0.01_1 --extra_tag train_0.01_1 \
--ckpt_save_interval 4 --repeat 10 \
--dbinfos kitti_dbinfos_train_0.01_1_37.pkl# Single GPU (e.g., A100-80GB)
python -u train.py \
--cfg_file ./cfgs/kitti_models/pv_rcnn_ssl_60.yaml \
--batch_size 8 \
--launcher slurm \
--split train_0.01_1 \
--extra_tag train_0.01_1 \
--ckpt_save_interval 2 \
--pretrained_model "../output/cfgs/kitti_models/pv_rcnn/split_0.01_1/ckpt/checkpoint_epoch_80.pth" \
--repeat 5 \
--dbinfos kitti_dbinfos_train_0.01_1_37.pkl
# Multi-GPU with Slurm (e.g., A100-40GB x2)
srun -p A100-40GB --job-name=train_0.01_1 --ntasks=2 \
--gpus-per-task=1 --cpus-per-task=3 \
--mem=60GB --kill-on-bad-exit=1 python -u train.py \
--cfg_file ./cfgs/kitti_models/pv_rcnn_ssl_60.yaml \
--batch_size 8 --launcher slurm --split train_0.01_1 \
--extra_tag train_0.01_1 --ckpt_save_interval 2 \
--pretrained_model "../output/cfgs/kitti_models/pv_rcnn/split_0.01_1/ckpt/checkpoint_epoch_80.pth" \
--repeat 5 --dbinfos kitti_dbinfos_train_0.01_1_37.pklOur code is based on 3DIoUMatch-PVRCNN and OpenPCDet v0.5. Thanks OpenPCDet Development Team for their awesome codebase.
This work has been funded by the German Ministry for Education and Research (BMB+F) in the project MOMENTUM.
If you find this work useful, please consider citing:
@InProceedings{Nozarian_2023_CVPR,
author = {Nozarian, Farzad and Agarwal, Shashank and Rezaeianaran, Farzaneh and Shahzad, Danish and Poibrenski, Atanas and M\"uller, Christian and Slusallek, Philipp},
title = {Reliable Student: Addressing Noise in Semi-Supervised 3D Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2023},
pages = {4980-4989}
}
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