Google Landmark Recogntion and Retrieval 2021 Solutions

Overview

Google Landmark Recogntion and Retrieval 2021 Solutions

In this repository you can find solution and code for Google Landmark Recognition 2021 and Google Landmark Retrieval 2021 competitions (both in top-100).

Brief Summary

My solution is based on the latest modeling from the previous competition and strong post-processing based on re-ranking and using side models like detectors. I used single RTX 3080, EfficientNet B0 and only competition data for training.

Model and loss function

I used the same model and loss as the winner team of the previous competition as a base. Since I had only single RTX 3080, I hadn't enough time to experiment with that and change it. The only things I managed to test is Subcenter ArcMarginProduct as the last block of model and ArcFaceLossAdaptiveMargin loss function, which has been used by the 2nd place team in the previous year. Both those things gave me a signifact score boost (around 4% on CV and 5% on LB).

Setting up the training and validation

Optimizing and scheduling

Optimizer - Ranger (lr=0.003)
Scheduler - CosineAnnealingLR (T_max=12) + 1 epoch Warm-Up

Training stages

I found the best perfomance in training for 15 epochs and 5 stages:

  1. (1-3) - Resize to image size, Horizontal Flip
  2. (4-6) - Resize to bigger image size, Random Crop to image size, Horizontal Flip
  3. (7-9) - Resize to bigger image size, Random Crop to image size, Horizontal Flip, Coarse Dropout with one big square (CutMix)
  4. (10-12) - Resize to bigger image size, Random Crop to image size, Horizontal Flip, FMix, CutMix, MixUp
  5. (13-15) - Resize to bigger image size, Random Crop to image size, Horizontal Flip

I used default Normalization on all the epochs.

Validation scheme

Since I hadn't enough hardware, this became my first competition where I wasn't able to use a K-fold validation, but at least I saw stable CV and CV/LB correlation at the previous competitions, so I used simple stratified train-test split in 0.8, 0.2 ratio. I also oversampled all the samples up to 5 for each class.

Inference and Post-Processing:

  1. Change class to non-landmark if it was predicted more than 20 times .
  2. Using pretrained YoloV5 for detecting non-landmark images. All classes are used, boxes with confidence < 0.5 are dropped. If total area of boxes is greater than total_image_area / 2.7, the sample is marked as non-landmark. I tried to use YoloV5 for cleaning the train dataset as well, but it only decreased a score.
  3. Tuned post-processing from this paper, based on the cosine similarity between train and test images to non-landmark ones.
  4. Higher image size for extracting embeddings on inference.
  5. Also using public train dataset as an external data for extracting embeddings.

Didn't work for me

  • Knowledge Distillation
  • Resnet architectures (on average they were worse than effnets)
  • Adding an external non-landmark class to training from 2019 test dataset
  • Train binary non-landmark classifier

Transfer Learning on the full dataset and Label Smoothing should be useful here, but I didn't have time to test it.

Owner
Vadim Timakin
17 y.o Machine Learning Engineer | Kaggle Competitions Expert | ML/DL/CV | PyTorch
Vadim Timakin
ppo_pytorch_cpp - an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch

PPO Pytorch C++ This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment t

Martin Huber 59 Dec 09, 2022
Official repository for the ICLR 2021 paper Evaluating the Disentanglement of Deep Generative Models with Manifold Topology

Official repository for the ICLR 2021 paper Evaluating the Disentanglement of Deep Generative Models with Manifold Topology Sharon Zhou, Eric Zelikman

Stanford Machine Learning Group 34 Nov 16, 2022
A curated list of awesome projects and resources related fastai

A curated list of awesome projects and resources related fastai

Tanishq Abraham 138 Dec 22, 2022
code for Multi-scale Matching Networks for Semantic Correspondence, ICCV

MMNet This repo is the official implementation of ICCV 2021 paper "Multi-scale Matching Networks for Semantic Correspondence.". Pre-requisite conda cr

joey zhao 25 Dec 12, 2022
A tf.keras implementation of Facebook AI's MadGrad optimization algorithm

MADGRAD Optimization Algorithm For Tensorflow This package implements the MadGrad Algorithm proposed in Adaptivity without Compromise: A Momentumized,

20 Aug 18, 2022
Embeddinghub is a database built for machine learning embeddings.

Embeddinghub is a database built for machine learning embeddings.

Featureform 1.2k Jan 01, 2023
Object Detection with YOLOv3

Object Detection with YOLOv3 Bu projede YOLOv3-608 modeli kullanılmıştır. Requirements Python 3.8 OpenCV Numpy Documentation Yolo ile ilgili detaylı b

Ayşe Konuş 0 Mar 27, 2022
Code accompanying "Dynamic Neural Relational Inference" from CVPR 2020

Code accompanying "Dynamic Neural Relational Inference" This codebase accompanies the paper "Dynamic Neural Relational Inference" from CVPR 2020. This

Colin Graber 48 Dec 23, 2022
chen2020iros: Learning an Overlap-based Observation Model for 3D LiDAR Localization.

Overlap-based 3D LiDAR Monte Carlo Localization This repo contains the code for our IROS2020 paper: Learning an Overlap-based Observation Model for 3D

Photogrammetry & Robotics Bonn 219 Dec 15, 2022
Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics.

Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics. By Andres Milioto @ University of Bonn. (for the new P

Photogrammetry & Robotics Bonn 314 Dec 30, 2022
Compositional Sketch Search

Compositional Sketch Search Official repository for ICIP 2021 Paper: Compositional Sketch Search Requirements Install and activate conda environment c

Alexander Black 8 Sep 06, 2021
Simple node deletion tool for onnx.

snd4onnx Simple node deletion tool for onnx. I only test very miscellaneous and limited patterns as a hobby. There are probably a large number of bugs

Katsuya Hyodo 6 May 15, 2022
Exploring Image Deblurring via Blur Kernel Space (CVPR'21)

Exploring Image Deblurring via Encoded Blur Kernel Space About the project We introduce a method to encode the blur operators of an arbitrary dataset

VinAI Research 118 Dec 19, 2022
Madanalysis5 - A package for event file analysis and recasting of LHC results

Welcome to MadAnalysis 5 Outline What is MadAnalysis 5? Requirements Downloading

MadAnalysis 15 Jan 01, 2023
Fantasy Points Prediction and Dream Team Formation

Fantasy-Points-Prediction-and-Dream-Team-Formation Collected Data from open source resources that have over 100 Parameters for predicting cricket play

Akarsh Singh 2 Sep 13, 2022
Neural Contours: Learning to Draw Lines from 3D Shapes (CVPR2020)

Neural Contours: Learning to Draw Lines from 3D Shapes This repository contains the PyTorch implementation for CVPR 2020 Paper "Neural Contours: Learn

93 Dec 16, 2022
《Lerning n Intrinsic Grment Spce for Interctive Authoring of Grment Animtion》

Learning an Intrinsic Garment Space for Interactive Authoring of Garment Animation Overview This is the demo code for training a motion invariant enco

YuanBo 213 Dec 14, 2022
Sharpness-Aware Minimization for Efficiently Improving Generalization

Sharpness-Aware-Minimization-TensorFlow This repository provides a minimal implementation of sharpness-aware minimization (SAM) (Sharpness-Aware Minim

Sayak Paul 54 Dec 08, 2022
Dungeons and Dragons randomized content generator

Component based Dungeons and Dragons generator Supports Entity/Monster Generation NPC Generation Weapon Generation Encounter Generation Environment Ge

Zac 3 Dec 04, 2021
An unofficial styleguide and best practices summary for PyTorch

A PyTorch Tools, best practices & Styleguide This is not an official style guide for PyTorch. This document summarizes best practices from more than a

IgorSusmelj 1.5k Jan 05, 2023