4st place solution for the PBVS 2022 Multi-modal Aerial View Object Classification Challenge - Track 1 (SAR) at PBVS2022

Overview

A Two-Stage Shake-Shake Network for Long-tailed Recognition of SAR Aerial View Objects

4st place solution for the PBVS 2022 Multi-modal Aerial View Object Classification Challenge - Track 1 (SAR)

Challenge Site

Overview

Synthetic Aperture Radar (SAR) has received more attention due to its complementary superiority on capturing significant information in the remote sensing area. However, for an Aerial View Object Classification (AVOC) task, SAR images still suffer from the long-tailed distribution of the aerial view objects. This disparity dampens the performance of classification methods, especially for the datasensitive deep learning models. In this paper, we propose a two-stage shake-shake network to tackle the long-tailed learning problem. Specifically, it decouples the learning procedure into the representation learning stage and the classification learning stage. Moreover, we apply the test time augmentation (TTA) and a post-processing approach (CAN) to improve the accuracy. In the PBVS 2022 Multi-modal Aerial View Object Classification Challenge Track 1, our method achieves 21.82% and 27.97% accuracy in the development phase and testing phase respectively, which achieves the top-tier among all the participants.

image-20220417170228668

Requirements

  • Ubuntu (It's only tested on Ubuntu, so it may not work on Windows.)

  • Python >= 3.7

  • PyTorch >= 1.4.0

  • torchvision

    pip install -r requirements.txt

Usage

The first stage training

python train.py --config ./configs/sar10/shake_shake.yaml
  • You need to change the value of “dataset_dir”, “dataset_dir_val”, under the “dataset” field and “output_dir” under the “train” field in the file “./configs/sar10/shake_shake.yaml”。

The second stage training

python train.py --config ./configs/sar10/shake_shake_fc.yaml
  • You need to change the value of “dataset_dir”, “dataset_dir_val” under the “dataset” field and “output_dir”, “checkpoint” under the “train” field in the file “./configs/sar10/shake_shake_fc.yaml”。

Test

python predict_TTA.py 
  • You need to change the value of “dataset_dir”, “checkpoint”, under the “test” field in the file “./configs/sar10/shake_shake.yaml”, then you can find the results in file “.result/results.csv”。
  • You can download the trained model here.

Acknowledge

The codes borrow heavily from hysts/pytorch_image_classification.

Owner
LinpengPan
LinpengPan
Aerial Imagery dataset for fire detection: classification and segmentation (Unmanned Aerial Vehicle (UAV))

Aerial Imagery dataset for fire detection: classification and segmentation using Unmanned Aerial Vehicle (UAV) Title FLAME (Fire Luminosity Airborne-b

null 71 Jul 18, 2022
Xview3 solution - XView3 challenge, 2nd place solution

Xview3, 2nd place solution https://iuu.xview.us/ test split aggregate score publ

Selim Seferbekov 22 Jul 19, 2022
PyTorch code for the paper "Complementarity is the King: Multi-modal and Multi-grained Hierarchical Semantic Enhancement Network for Cross-modal Retrieval".

Complementarity is the King: Multi-modal and Multi-grained Hierarchical Semantic Enhancement Network for Cross-modal Retrieval (M2HSE) PyTorch code fo

Xinlei-Pei 9 Apr 23, 2022
🏆 The 1st Place Submission to AICity Challenge 2021 Natural Language-Based Vehicle Retrieval Track (Alibaba-UTS submission)

AI City 2021: Connecting Language and Vision for Natural Language-Based Vehicle Retrieval ?? The 1st Place Submission to AICity Challenge 2021 Natural

null 69 Jul 7, 2022
Code for 1st place solution in Sleep AI Challenge SNU Hospital

Sleep AI Challenge SNU Hospital 2021 Code for 1st place solution for Sleep AI Challenge (Note that the code is not fully organized) Refer to the notio

Saewon Yang 13 Jan 3, 2022
The 1st place solution of track2 (Vehicle Re-Identification) in the NVIDIA AI City Challenge at CVPR 2021 Workshop.

AICITY2021_Track2_DMT The 1st place solution of track2 (Vehicle Re-Identification) in the NVIDIA AI City Challenge at CVPR 2021 Workshop. Introduction

Hao Luo 86 Jul 26, 2022
Waymo motion prediction challenge 2021: 3rd place solution

Waymo motion prediction challenge 2021: 3rd place solution ?? Technical report ??️ Presentation ?? Announcement ??Motion Prediction Channel Website ??

null 137 Aug 4, 2022
4th place solution to datafactory challenge by Intermarché.

Solution to Datafactory challenge by Intermarché. 4th place solution to datafactory challenge by Intermarché. The objective of the challenge is to pre

Raphael Sourty 11 Mar 19, 2022
Kaggle | 9th place (part of) solution for the Bristol-Myers Squibb – Molecular Translation challenge

Part of the 9th place solution for the Bristol-Myers Squibb – Molecular Translation challenge translating images containing chemical structures into I

Erdene-Ochir Tuguldur 20 Nov 2, 2021
Kaggle | 9th place single model solution for TGS Salt Identification Challenge

UNet for segmenting salt deposits from seismic images with PyTorch. General We, tugstugi and xuyuan, have participated in the Kaggle competition TGS S

Erdene-Ochir Tuguldur 274 May 13, 2022
1st place solution to the Satellite Image Change Detection Challenge hosted by SenseTime

1st place solution to the Satellite Image Change Detection Challenge hosted by SenseTime

Lihe Yang 182 Aug 12, 2022
10th place solution for Google Smartphone Decimeter Challenge at kaggle.

Under refactoring 10th place solution for Google Smartphone Decimeter Challenge at kaggle. Google Smartphone Decimeter Challenge Global Navigation Sat

null 10 Jul 29, 2022
4th place solution for the SIGIR 2021 challenge.

SIGIR-2021 (Tinkoff.AI) How to start Download train and test data: https://sigir-ecom.github.io/data-task.html Place it under sigir-2021/data/. Run py

Tinkoff.AI 4 Jul 1, 2022
Meli Data Challenge 2021 - First Place Solution

My solution for the Meli Data Challenge 2021

Matias Moreyra 23 Mar 9, 2022
The sixth place winning solution (6/220) in 2021 Gaofen Challenge.

SwinTransformer + OBBDet The sixth place winning solution (6/220) in the track of Fine-grained Object Recognition in High-Resolution Optical Images, 2

ming71 41 Jun 29, 2022
Codebase for the solution that won first place and was awarded the most human-like agent in the 2021 NeurIPS Competition MineRL BASALT Challenge.

KAIROS MineRL BASALT Codebase for the solution that won first place and was awarded the most human-like agent in the 2021 NeurIPS Competition MineRL B

Vinicius G. Goecks 33 Aug 1, 2022
Kohei's 5th place solution for xview3 challenge

xview3-kohei-solution Usage This repository assumes that the given data set is stored in the following locations: $ ls data/input/xview3/*.csv data/in

Kohei Ozaki 2 Jan 17, 2022
1st place solution in CCF BDCI 2021 ULSEG challenge

1st place solution in CCF BDCI 2021 ULSEG challenge This is the source code of the 1st place solution for ultrasound image angioma segmentation task (

Chenxu Peng 30 May 17, 2022
Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal, multi-exposure and multi-focus image fusion.

U2Fusion Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal (VIS-IR, medical), multi

Han Xu 102 Aug 6, 2022