Implementation of "Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency"

Last update: Nov 17, 2022

Related tags

Overview

Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency (ICCV2021)

Paper Link: https://arxiv.org/abs/2107.11355

This implementation builds on top of OpenPCDet. Please kindly refer to its Github repo for introduction and recent updates.

Installation

a. Set up the environment:

Python 3.6+ (3.7 suggested)
Pytorch 1.1+ (1.3 suggested)

b. Clone this repository.

c. Install requirements

pip install -r requirements.txt

d. Install the pcdet library:

python setup.py develop

e. Data preparation

Please refer to GETTING_STARTED.md for data preparation and basic usage of pcdet .

Training and Testing

a. Train a base model on the source domain (KITTI) as the pretrained model for domain adaptation

cd tools/

sh scripts/dist_train.sh ${NUM_GPUS} --cfg_file cfgs/kitti_models/pointrcnn.yaml

# or using slurm
sh scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} ${NUM_GPUS} --cfg_file cfgs/kitti_models/pointrcnn.yaml

b. Train the domain adaptation model

sh scripts/dist_train_mean_teacher.sh ${NUM_GPUS} \
--cfg_file cfgs/kitti_models/pointrcnn_mean_teacher_waymo.yaml \
--pretrained_model ${PATH_TO_PRETRAINED_MODEL_CHECKPOINT}

# or using slurm
sh scripts/slurm_train_mean_teacher.sh ${PARTITION} ${JOB_NAME} ${NUM_GPUS} \
--cfg_file cfgs/kitti_models/pointrcnn_mean_teacher_waymo.yaml \
--pretrained_model ${PATH_TO_PRETRAINED_MODEL_CHECKPOINT}

c. Test on the target domain (Waymo)

python test.py --cfg_file cfgs/waymo_models/pointrcnn_kitti2waymo.yaml \
--ckpt ${PATH_TO_MODEL_CHECKPOINT}

Implementation of "Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency"

Related tags

Overview

Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency (ICCV2021)

Installation

Training and Testing

Owner

This repository contains the needed resources to build the HIRID-ICU-Benchmark dataset

PyTorch implementation of DeepDream algorithm

Sequence modeling benchmarks and temporal convolutional networks

Serving PyTorch 1.0 Models as a Web Server in C++

Data-depth-inference - Data depth inference with python

Simple PyTorch implementations of Badnets on MNIST and CIFAR10.

Official implementation of deep-multi-trajectory-based single object tracking (IEEE T-CSVT 2021).

TensorFlow implementation of "Variational Inference with Normalizing Flows"

Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research

Highway networks implemented in PyTorch.

Implementation for "Seamless Manga Inpainting with Semantics Awareness" (SIGGRAPH 2021 issue)

EsViT: Efficient self-supervised Vision Transformers

DeepSpamReview: Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures. Summer Internship project at CoreView Systems.

This is a repository for a semantic segmentation inference API using the OpenVINO toolkit

NumPy로 구현한 딥러닝 라이브러리입니다. (자동 미분 지원)

An intelligent, flexible grammar of machine learning.

Code of paper "Compositionally Generalizable 3D Structure Prediction"

Team nan solution repository for FPT data-centric competition. Data augmentation, Albumentation, Mosaic, Visualization, KNN application

Start-to-finish tutorial for interactive music co-creation in PyTorch and Tensorflow.js

Official Pytorch Implementation of Length-Adaptive Transformer (ACL 2021)