Weakly Supervised 3D Object Detection from Point Cloud with Only Image Level Annotation

Related tags

Deep LearningSCCKTIM
Overview

SCCKTIM

Weakly Supervised 3D Object Detection from Point Cloud with Only Image-Level Annotation
Our code will be available soon.

The class knowledge transfer module and pseudo_label generalization module provide docker images.

Class Knowledge Transfer Module

Installation according to WS3DOD.
Generating the superpixel by running the following:

conda activate ws3dod
cd core/source/context_module
python generate_superpixel_image

Our data file structure is as follows:

--data
  --kitti
    --training
      --calib
      --image_2
      --label_2
      --planes
      --sphere
      --superpixel_2
      --velodyne
    --train.txt
    --trainval.txt
  --kitti_pseudo
    --training
      --label_2

Files in kitti_pseudo are generated by PG in the previous iteration.
Please read core/launcher.py and paper for details of running the code.

Conceptual Knowledge Transfer Module

Following README.md in CKT

Pseudo-label Generalization

Installation according to OpenpcDet.

conda activate openpcdet

Our data file structure is as follows:

--data
  --kitti
    --ImageSets
      --trainval.txt
      --val.txt
      --test.txt
    --ImageSets_real
      --train.txt
      --trainval.txt
      --val.txt
      --test.txt
    --testing
      --calib
      --image_2
      --velodyne
    --training
      --calib
      --image_2
      --label_2
      --velodyne
      --planes
      --pseudo_label
  --waymo

Files in pseudo_label are generated by CKT previous step.
label_2 is empty before training the deep network. Using the following command to generate pseudo-labels:

cd tools
python generate_pseudo_label

Using the following command for training deep network.

python -m torch.distributed.launch --nproc_per_node=4 train.py --launcher pytorch --cfg_file cfgs/kitti_models/pv_rcnn.yaml│
 --sync_bn --fix_random_seed --extra_tag normal_nonrot_pcn_reg_pvrcnn_iter1_pcn_reg

License

We note that some code in this repository is adapted from the following repositories:

Official PyTorch code for CVPR 2020 paper "Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision"

Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision https://arxiv.org/abs/2003.00393 Abstract Active learning (AL) aims to min

Denis 29 Nov 21, 2022
Python Algorithm Interview Book Review

파이썬 알고리즘 인터뷰 책 리뷰 리뷰 IT 대기업에 들어가고 싶은 목표가 있다. 내가 꿈꿔온 회사에서 일하는 사람들의 모습을 보면 멋있다고 생각이 들고 나의 목표에 대한 열망이 강해지는 것 같다. 미래의 핵심 사업 중 하나인 SW 부분을 이끌고 발전시키는 우리나라의 I

SharkBSJ 1 Dec 14, 2021
CAMoE + Dual SoftMax Loss (DSL): Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss

CAMoE + Dual SoftMax Loss (DSL): Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss This is official implement of "

程星 87 Dec 24, 2022
Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment"

DSN-IQA Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment" Requirements Python =3.8.0 Pytorch =1.7.1 Usage wit

7 Oct 13, 2022
Official implementation of Neural Bellman-Ford Networks (NeurIPS 2021)

NBFNet: Neural Bellman-Ford Networks This is the official codebase of the paper Neural Bellman-Ford Networks: A General Graph Neural Network Framework

MilaGraph 136 Dec 21, 2022
PyTorch implementation for COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction (CVPR 2021)

Completer: Incomplete Multi-view Clustering via Contrastive Prediction This repo contains the code and data of the following paper accepted by CVPR 20

XLearning Group 72 Dec 07, 2022
🔮 Execution time predictions for deep neural network training iterations across different GPUs.

Habitat: A Runtime-Based Computational Performance Predictor for Deep Neural Network Training Habitat is a tool that predicts a deep neural network's

Geoffrey Yu 44 Dec 27, 2022
CVAT is free, online, interactive video and image annotation tool for computer vision

Computer Vision Annotation Tool (CVAT) CVAT is free, online, interactive video and image annotation tool for computer vision. It is being used by our

OpenVINO Toolkit 8.6k Jan 04, 2023
wmctrl ported to Python Ctypes

work in progress wmctrl is a command that can be used to interact with an X Window manager that is compatible with the EWMH/NetWM specification. wmctr

Iyad Ahmed 22 Dec 31, 2022
Bagua is a flexible and performant distributed training algorithm development framework.

Bagua is a flexible and performant distributed training algorithm development framework.

786 Dec 17, 2022
A compendium of useful, interesting, inspirational usage of pandas functions, each example will be an ipynb file

Pandas_by_examples A compendium of useful/interesting/inspirational usage of pandas functions, each example will be an ipynb file What is this reposit

Guangyuan(Frank) Li 32 Nov 20, 2022
Connecting Java/ImgLib2 + Python/NumPy

imglyb imglyb aims at connecting two worlds that have been seperated for too long: Python with numpy Java with ImgLib2 imglyb uses jpype to access num

ImgLib2 29 Dec 21, 2022
[ICML 2021] Towards Understanding and Mitigating Social Biases in Language Models

Towards Understanding and Mitigating Social Biases in Language Models This repo contains code and data for evaluating and mitigating bias from generat

Paul Liang 42 Jan 03, 2023
Animal Sound Classification (Cats Vrs Dogs Audio Sentiment Classification)

this is a simple artificial neural network model using deep learning and torch-audio to classify cats and dog sounds.

crispengari 3 Dec 05, 2022
Recreate CenternetV2 based on MMDET.

Introduction This project is trying to Recreate CenternetV2 based on MMDET, which is proposed in paper Probabilistic two-stage detection. This project

25 Dec 09, 2022
ArcaneGAN by Alex Spirin

ArcaneGAN by Alex Spirin

Alex 617 Dec 28, 2022
Covid19-Forecasting - An interactive website that tracks, models and predicts COVID-19 Cases

Covid-Tracker This is an interactive website that tracks, models and predicts CO

Adam Lahmadi 1 Feb 01, 2022
Code for Learning to Segment The Tail (LST)

Learning to Segment the Tail [arXiv] In this repository, we release code for Learning to Segment The Tail (LST). The code is directly modified from th

47 Nov 07, 2022
Self-labelling via simultaneous clustering and representation learning. (ICLR 2020)

Self-labelling via simultaneous clustering and representation learning 🆗 🆗 🎉 NEW models (20th August 2020): Added standard SeLa pretrained torchvis

Yuki M. Asano 469 Jan 02, 2023
[NeurIPS 2021] COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining

COCO-LM This repository contains the scripts for fine-tuning COCO-LM pretrained models on GLUE and SQuAD 2.0 benchmarks. Paper: COCO-LM: Correcting an

Microsoft 106 Dec 12, 2022