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:

Python KNN model: Predicting a probability of getting a work visa. Tableau: Non-immigrant visas over the years.

The value of international students to the United States. Probability of getting a non-immigrant visa. Project timeline: Jan 2021 - April 2021 Project

Zinaida Dvoskina 2 Nov 21, 2021
Markov Attention Models

Introduction This repo contains code for reproducing the results in the paper Graphical Models with Attention for Context-Specific Independence and an

Vicarious 0 Dec 09, 2021
implement of SwiftNet:Real-time Video Object Segmentation

SwiftNet The official PyTorch implementation of SwiftNet:Real-time Video Object Segmentation, which has been accepted by CVPR2021. Requirements Python

haochen wang 64 Dec 14, 2022
Code for the Paper "Diffusion Models for Handwriting Generation"

Code for the Paper "Diffusion Models for Handwriting Generation"

62 Dec 21, 2022
External Attention Network

Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks paper : https://arxiv.org/abs/2105.02358 EAMLP will come soon Jitto

MenghaoGuo 357 Dec 11, 2022
JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation

JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation This the repository for this paper. Find extensions of this w

Zhuoyuan Mao 14 Oct 26, 2022
Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'.

COTREC Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'. Requirements: Python 3.7, Pytorch 1.6.0 Best Hype

Xin Xia 42 Dec 09, 2022
RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving

RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving (AAAI2021). RTS3D is efficiency and accuracy s

71 Nov 29, 2022
Compact Bidirectional Transformer for Image Captioning

Compact Bidirectional Transformer for Image Captioning Requirements Python 3.8 Pytorch 1.6 lmdb h5py tensorboardX Prepare Data Please use git clone --

YE Zhou 19 Dec 12, 2022
S-attack library. Official implementation of two papers "Are socially-aware trajectory prediction models really socially-aware?" and "Vehicle trajectory prediction works, but not everywhere".

S-attack library: A library for evaluating trajectory prediction models This library contains two research projects to assess the trajectory predictio

VITA lab at EPFL 71 Jan 04, 2023
Official implementation of the paper DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows

DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows Official implementation of the paper DeFlow: Learning Complex Im

Valentin Wolf 86 Nov 16, 2022
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

TorchCAM: class activation explorer Simple way to leverage the class-specific activation of convolutional layers in PyTorch. Quick Tour Setting your C

F-G Fernandez 1.2k Dec 29, 2022
No Code AI/ML platform

NoCodeAIML No Code AI/ML platform - Community Edition Video credits: Uday Kiran Typical No Code AI/ML Platform will have features like drag and drop,

Bhagvan Kommadi 5 Jan 28, 2022
This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.

BiPointNet: Binary Neural Network for Point Clouds Created by Haotong Qin, Zhongang Cai, Mingyuan Zhang, Yifu Ding, Haiyu Zhao, Shuai Yi, Xianglong Li

Haotong Qin 59 Dec 17, 2022
AITom is an open-source platform for AI driven cellular electron cryo-tomography analysis.

AITom Introduction AITom is an open-source platform for AI driven cellular electron cryo-tomography analysis. AITom is originated from the tomominer l

93 Jan 02, 2023
Evaluation framework for testing segmentation networks in PyTorch

Evaluation framework for testing segmentation networks in PyTorch. What segmentation network to choose for next Kaggle competition? This benchmark knows the answer!

Eugene Khvedchenya 37 Apr 27, 2022
Normalization Matters in Weakly Supervised Object Localization (ICCV 2021)

Normalization Matters in Weakly Supervised Object Localization (ICCV 2021) 99% of the code in this repository originates from this link. ICCV 2021 pap

Jeesoo Kim 10 Feb 01, 2022
YOLOv5 detection interface - PyQt5 implementation

所有代码已上传,直接clone后,运行yolo_win.py即可开启界面。 2021/9/29:加入置信度选择 界面是在ultralytics的yolov5基础上建立的,界面使用pyqt5实现,内容较简单,娱乐而已。 功能: 模型选择 本地文件选择(视频图片均可) 开关摄像头

487 Dec 27, 2022
Sematic-Segmantation - Semantic Segmentation on MIT ADE20K dataset in PyTorch

Semantic Segmentation on MIT ADE20K dataset in PyTorch This is a PyTorch impleme

Berat Eren Terzioğlu 4 Mar 22, 2022
Multiple custom object count and detection using YOLOv3-Tiny method

Electronic-Component-YOLOv3 Introduce This project created to detect, count, and recognize multiple custom object using YOLOv3-Tiny method. The target

Derwin Mahardika 2 Nov 14, 2022