R3Det based on mmdet 2.19.0

Overview

R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object

License arXiv

Installation

# install mmdetection first if you haven't installed it yet. (Refer to mmdetection for details.)
pip install mmdet==2.19.0

# install r3det (Compiling rotated ops is a little time-consuming.)
pip install -r requirements.txt
pip install -v -e .
  • It is best to use opencv-python greater than 4.5.1 because its angle representation has been changed in 4.5.1. The following experiments are all run with 4.5.3.

Quick Start

Please change path in configs to your data path.

# train
CUDA_VISIBLE_DEVICES=0 PORT=29500 \
./tools/dist_train.sh configs/rretinanet/rretinanet_obb_r50_fpn_1x_dota_v3.py 1

# submission
CUDA_VISIBLE_DEVICES=0 PORT=29500 \
./tools/dist_test.sh configs/rretinanet/rretinanet_obb_r50_fpn_1x_dota_v3.py \
        work_dirs/rretinanet_obb_r50_fpn_1x_dota_v3/epoch_12.pth 1 --format-only\
        --eval-options submission_dir=work_dirs/rretinanet_obb_r50_fpn_1x_dota_v3/Task1_results

For DOTA dataset, please crop the original images into 1024×1024 patches with an overlap of 200 by run

python tools/split/img_split.py --base_json \
       tools/split/split_configs/split_configs/dota1_0/ss_trainval.json

python tools/split/img_split.py --base_json \
       tools/split/split_configs/dota1_0/ss_test.json

Please change path in ss_trainval.json, ss_test.json to your path. (Forked from BboxToolkit, which is faster then DOTA_Devkit.)

Angle Representations

Three angle representations are built-in, which can freely switch in the config.

  • v1 (from R3Det): [-PI/2, 0)
  • v2 (from S2ANet): [-Pi/4, 3PI/4)
  • v3 (from OBBDetection): [-PI/2, PI/2)

The differences of the three angle representations are reflected in poly2obb, obb2poly, obb2xyxy, obb2hbb, hbb2obb, etc. [More], And according to the above three papers, the coders of them are different.

  • DeltaXYWHAOBBoxCoder
    • v1:None
    • v2:Constrained angle + Projection of dx and dy + Normalized with PI
    • v3:Constrained angle and length&width + Projection of dx and dy
  • DeltaXYWHAHBBoxCoder
    • v1:None
    • v2:Constrained angle + Normalized with PI
    • v3:Constrained angle and length&width + Normalized with 2PI

We believe that different coders are the key reason for the different baselines in different papers. The good news is that all the above coders can be freely switched in R3Det. In addition, R3Det also provide 4 NMS ops and 3 IoU_Calculators for rotation detection as follows:

  • nms.type
    • v1:v1
    • v2:v2
    • v3:v3
    • mmcv: mmcv
  • iou_calculator
    • v1:RBboxOverlaps2D_v1
    • v2:RBboxOverlaps2D_v2
    • v3:RBboxOverlaps2D_v3

Performance

DOTA1.0 (Task1)
Model Backbone Lr schd MS RR Angle box AP Official Download
RRetinaNet HBB R50-FPN 1x - - v1 65.19 65.73 Baidu:0518/Google
RRetinaNet OBB R50-FPN 1x - - v3 68.20 69.40 Baidu:0518/Google
RRetinaNet OBB R50-FPN 1x - - v2 68.64 68.40 Baidu:0518/Google
R3Det R50-FPN 1x - - v1 70.41 70.66 Baidu:0518/Google
R3Det* R50-FPN 1x - - v1 70.86 - Baidu:0518/Google
  • MS means multiple scale image split.
  • RR means random rotation.

Citation

@inproceedings{yang2021r3det,
    title={R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object},
    author={Yang, Xue and Yan, Junchi and Feng, Ziming and He, Tao},
    booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
    volume={35},
    number={4},
    pages={3163--3171},
    year={2021}
}

Owner
SJTU-Thinklab-Det
SJTU-Thinklab-Det
[ICCV 2021] Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation

ADDS-DepthNet This is the official implementation of the paper Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation I

LIU_LINA 52 Nov 24, 2022
PyTorch implementation for the paper Pseudo Numerical Methods for Diffusion Models on Manifolds

Pseudo Numerical Methods for Diffusion Models on Manifolds (PNDM) This repo is the official PyTorch implementation for the paper Pseudo Numerical Meth

Luping Liu (刘路平) 196 Jan 05, 2023
Learning from Synthetic Humans, CVPR 2017

Learning from Synthetic Humans (SURREAL) Gül Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J. Black, Ivan Laptev and Cordelia Schmid,

Gul Varol 538 Dec 18, 2022
Generic image compressor for machine learning. Pytorch code for our paper "Lossy compression for lossless prediction".

Lossy Compression for Lossless Prediction Using: Training: This repostiory contains our implementation of the paper: Lossy Compression for Lossless Pr

Yann Dubois 84 Jan 02, 2023
Unoffical reMarkable AddOn for Firefox.

reMarkable for Firefox (Download) This repo converts the offical reMarkable Chrome Extension into a Firefox AddOn published here under the name "Unoff

Jelle Schutter 45 Nov 28, 2022
Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset (CVPR'19)

Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset (CVPR'19) Tianyu Wang*, Xin Yang*, Ke Xu, Shaozhe Chen, Qiang Zhang, Ry

Steve Wong 177 Dec 01, 2022
Emotional conditioned music generation using transformer-based model.

This is the official repository of EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation. The paper has b

hung anna 96 Nov 09, 2022
《A-CNN: Annularly Convolutional Neural Networks on Point Clouds》(2019)

A-CNN: Annularly Convolutional Neural Networks on Point Clouds Created by Artem Komarichev, Zichun Zhong, Jing Hua from Department of Computer Science

Artёm Komarichev 44 Feb 24, 2022
InsTrim: Lightweight Instrumentation for Coverage-guided Fuzzing

InsTrim The paper: InsTrim: Lightweight Instrumentation for Coverage-guided Fuzzing Build Prerequisite llvm-8.0-dev clang-8.0 cmake = 3.2 Make git cl

75 Dec 23, 2022
competitions-v2

Codabench (formerly Codalab Competitions v2) Installation $ cp .env_sample .env $ docker-compose up -d $ docker-compose exec django ./manage.py migrat

CodaLab 21 Dec 02, 2022
Opinionated code formatter, just like Python's black code formatter but for Beancount

beancount-black Opinionated code formatter, just like Python's black code formatter but for Beancount Try it out online here Features MIT licensed - b

Launch Platform 16 Oct 11, 2022
Self-supervised learning (SSL) is a method of machine learning

Self-supervised learning (SSL) is a method of machine learning. It learns from unlabeled sample data. It can be regarded as an intermediate form between supervised and unsupervised learning.

Ashish Patel 4 May 26, 2022
Parris, the automated infrastructure setup tool for machine learning algorithms.

README Parris, the automated infrastructure setup tool for machine learning algorithms. What Is This Tool? Parris is a tool for automating the trainin

Joseph Greene 319 Aug 02, 2022
Python code to generate art with Generative Adversarial Network

GAN_Canvas_Maker Generating Art using Generative Adversarial Network (GAN) Python code to generate art with Generative Adversarial Network: https://to

Jonny Banana 10 Aug 22, 2022
Train robotic agents to learn pick and place with deep learning for vision-based manipulation in PyBullet.

Ravens is a collection of simulated tasks in PyBullet for learning vision-based robotic manipulation, with emphasis on pick and place. It features a Gym-like API with 10 tabletop rearrangement tasks,

Google Research 367 Jan 09, 2023
CMT: Convolutional Neural Networks Meet Vision Transformers

CMT: Convolutional Neural Networks Meet Vision Transformers [arxiv] 1. Introduction This repo is the CMT model which impelement with pytorch, no refer

FlyEgle 83 Dec 30, 2022
Randomized Correspondence Algorithm for Structural Image Editing

===================================== README: Inpainting based PatchMatch ===================================== @Author: Younesse ANDAM @Conta

Younesse 116 Dec 24, 2022
Registration Loss Learning for Deep Probabilistic Point Set Registration

RLLReg This repository contains a Pytorch implementation of the point set registration method RLLReg. Details about the method can be found in the 3DV

Felix Järemo Lawin 35 Nov 02, 2022
Tensorflow implementation of soft-attention mechanism for video caption generation.

SA-tensorflow Tensorflow implementation of soft-attention mechanism for video caption generation. An example of soft-attention mechanism. The attentio

Paul Chen 153 Nov 14, 2022
《LXMERT: Learning Cross-Modality Encoder Representations from Transformers》(EMNLP 2020)

The Most Important Thing. Our code is developed based on: LXMERT: Learning Cross-Modality Encoder Representations from Transformers

53 Dec 16, 2022