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

Facilitates implementing deep neural-network backbones, data augmentations

Trajectory Prediction with Graph-based Dual-scale Context Fusion

links and status of cool gradio demos

Augmentation for Single-Image-Super-Resolution

A Framework for Encrypted Machine Learning in TensorFlow

Implementation for the EMNLP 2021 paper "Interactive Machine Comprehension with Dynamic Knowledge Graphs".

Breast Cancer Classification Model is applied on a different dataset

Code repository for "Free View Synthesis", ECCV 2020.

ECLARE: Extreme Classification with Label Graph Correlations

FPSAutomaticAiming——基于YOLOV5的FPS类游戏自动瞄准AI

Implementation of ResMLP, an all MLP solution to image classification, in Pytorch

Python package provinding tools for artistic interactive applications using AI

A library for researching neural networks compression and acceleration methods.

Pytorch implementations of the paper Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients

Rayvens makes it possible for data scientists to access hundreds of data services within Ray with little effort.

Shape-Adaptive Selection and Measurement for Oriented Object Detection

《Truly shift-invariant convolutional neural networks》(2021)

Code release for NeurIPS 2020 paper "Co-Tuning for Transfer Learning"

This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

Convnet transfer - Code for paper How transferable are features in deep neural networks?