The 2nd place solution of 2021 google landmark retrieval on kaggle.

Overview

Google_Landmark_Retrieval_2021_2nd_Place_Solution

The 2nd place solution of 2021 google landmark retrieval on kaggle.

Environment

We use cuda 11.1/python 3.7/torch 1.9.1/torchvision 0.8.1 for training and testing.

Download imagenet pretrained model ResNeXt101ibn and SEResNet101ibn from IBN-Net. ResNest101 and ResNeSt269 can be found in ResNest.

Prepare data

  1. Download GLDv2 full version from the official site.

  2. Run python tools/generate_gld_list.py. This will generate clean, c2x, trainfull and all data for different stage of training.

  3. Validation annotation comes from all 1129 images in GLDv2. We expand the competition index set to index_expand. Each query could find all its GTs in the expanded index set and the validation could be more accurate.

Train

We use 8 GPU (32GB/16GB) for training. The evaluation metric in landmark retrieval is different from person re-identification. Due to the validation scale, we skip the validation stage during training and just use the model from last epoch for evaluation.

Fast Train Script

To make quick experiments, we provide scripts for R50_256 trained for clean subset. This setting trains very fast and is helpful for debug.

python -m torch.distributed.run --standalone --nnodes=1 --nproc_per_node=8 --master_port 55555 --max_restarts 0 train.py --config_file configs/GLDv2/R50_256.yml

Whole Train Pipeline

The whole training pipeline for SER101ibn backbone is listed below. Other backbones and input size can be modified accordingly.

python -m torch.distributed.run --standalone --nnodes=1 --nproc_per_node=8 --master_port 55555 --max_restarts 0 train.py --config_file configs/GLDv2/SER101ibn_384.yml
python -m torch.distributed.run --standalone --nnodes=1 --nproc_per_node=8 --master_port 55555 --max_restarts 0 train.py --config_file configs/GLDv2/SER101ibn_384_finetune.yml
python -m torch.distributed.run --standalone --nnodes=1 --nproc_per_node=8 --master_port 55555 --max_restarts 0 train.py --config_file configs/GLDv2/SER101ibn_512_finetune.yml
python -m torch.distributed.run --standalone --nnodes=1 --nproc_per_node=8 --master_port 55555 --max_restarts 0 train.py --config_file configs/GLDv2/SER101ibn_512_all.yml

Inference(notebooks)

  • With four models trained, cd to submission/code/ and modify settings in landmark_retrieval.py properly.

  • Then run eval_retrieval.sh to get submission file and evaluate on validation set offline.

General Settings

REID_EXTRACT_FLAG: Skip feature extraction when using offline code.
FEAT_DIR: Save cached features.
IMAGE_DIR: competition image dir. We make a soft link for competition data at submission/input/landmark-retrieval-2021/
RAW_IMAGE_DIR: origin GLDv2 dir
MODEL_DIR: the latest models for submission
META_DIR: saves meta files for rerank purpose
LOCAL_MATCHING and KR_FLAG disabled for our submission.

Fast Inference Script

Use R50_256 model trained from clean subset correspongding to the fast train script. Set CATEGORY_RERANK and REF_SET_EXTRACT to False. You will get about mAP=32.84% for the validation set.

Whole Inference Pipeline

  • Copy cache_all_list.pkl, cache_index_train_list.pkl and cache_full_list.pkl from cache to submission/input/meta-data-final

  • Set REF_SET_EXTRACT to True to extract features for all images of GLDv2. This will save about 4.9 million 512 dim features for each model in submission/input/meta-data-final.

  • Set REF_SET_EXTRACT to False and CATEGORY_RERANK to before_merge. This will load the precomputed features and run the proposed Landmark-Country aware rerank.

  • The notebooks on kaggle is exactly the same file as in base_landmark.py and landmark_retrieval.py. We also upload the same notebooks as in kaggle in kaggle.ipynb.

Kaggle and ICCV workshops

  • The challenge is held on kaggle and the leaderboard can be found here. We rank 2nd(2/263) in this challenge.

  • Kaggle Discussion post link here

  • ICCV workshop slides coming soon.

Thanks

The code is motivated by AICITY2021_Track2_DMT, 2020_1st_recognition_solution, 2020_2nd_recognition_solution, 2020_1st_retrieval_solution.

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{zhang2021landmark,
 title={2nd Place Solution to Google Landmark Retrieval 2021},
 author={Zhang, Yuqi and Xu, Xianzhe and Chen, Weihua and Wang, Yaohua and Zhang, Fangyi},
 year={2021}
}
The repo for the paper "I3CL: Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection".

I3CL: Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection Updates | Introduction | Results | Usage | Citation |

33 Jan 05, 2023
TraSw for FairMOT - A Single-Target Attack example (Attack ID: 19; Screener ID: 24):

TraSw for FairMOT A Single-Target Attack example (Attack ID: 19; Screener ID: 24): Fig.1 Original Fig.2 Attacked By perturbing only two frames in this

Derry Lin 21 Dec 21, 2022
A GOOD REPRESENTATION DETECTS NOISY LABELS

A GOOD REPRESENTATION DETECTS NOISY LABELS This code is a PyTorch implementation of the paper: Prerequisites Python 3.6.9 PyTorch 1.7.1 Torchvision 0.

<a href=[email protected]"> 64 Jan 04, 2023
3DMV jointly combines RGB color and geometric information to perform 3D semantic segmentation of RGB-D scans.

3DMV 3DMV jointly combines RGB color and geometric information to perform 3D semantic segmentation of RGB-D scans. This work is based on our ECCV'18 p

Владислав Молодцов 0 Feb 06, 2022
Does Oversizing Improve Prosumer Profitability in a Flexibility Market? - A Sensitivity Analysis using PV-battery System

Does Oversizing Improve Prosumer Profitability in a Flexibility Market? - A Sensitivity Analysis using PV-battery System The possibilities to involve

Babu Kumaran Nalini 0 Nov 19, 2021
Anagram Generator in Python

Anagrams Generator This is a program for computing multiword anagrams. It makes no effort to come up with sentences that make sense; it only finds ana

Day Fundora 5 Nov 17, 2022
Code for the paper "Controllable Video Captioning with an Exemplar Sentence"

SMCG Code for the paper "Controllable Video Captioning with an Exemplar Sentence" Introduction We investigate a novel and challenging task, namely con

10 Dec 04, 2022
Breaking the Dilemma of Medical Image-to-image Translation

Breaking the Dilemma of Medical Image-to-image Translation Supervised Pix2Pix and unsupervised Cycle-consistency are two modes that dominate the field

Kid Liet 86 Dec 21, 2022
The Turing Change Point Detection Benchmark: An Extensive Benchmark Evaluation of Change Point Detection Algorithms on real-world data

Turing Change Point Detection Benchmark Welcome to the repository for the Turing Change Point Detection Benchmark, a benchmark evaluation of change po

The Alan Turing Institute 85 Dec 28, 2022
Autonomous Perception: 3D Object Detection with Complex-YOLO

Autonomous Perception: 3D Object Detection with Complex-YOLO LiDAR object detect

Thomas Dunlap 2 Feb 18, 2022
Pytorch implementation of One-Shot Affordance Detection

One-shot Affordance Detection PyTorch implementation of our one-shot affordance detection models. This repository contains PyTorch evaluation code, tr

46 Dec 12, 2022
DL & CV-based indicator toolset for the vehicle drivers via live dash-cam footage.

Vehicle Indicator Toolset Deep Learning and Computer Vision based indicator toolset for vehicle drivers using live dash-cam footages. Tracking of vehi

Alex Xu 12 Dec 28, 2021
Repo for WWW 2022 paper: Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval

BiDR Repo for WWW 2022 paper: Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval. Requirements torch==

Microsoft 11 Oct 20, 2022
Check out the StyleGAN repo and place it in the same directory hierarchy as the present repo

Variational Model Inversion Attacks Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard Zemel, Alireza Makhzani Most commands are in run_scripts. W

Jackson Wang 15 Dec 26, 2022
GNN4Traffic - This is the repository for the collection of Graph Neural Network for Traffic Forecasting

GNN4Traffic - This is the repository for the collection of Graph Neural Network for Traffic Forecasting

564 Jan 02, 2023
Automatic deep learning for image classification.

AutoDL AutoDL automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few line

wenqi 2 Oct 12, 2022
Joint project of the duo Hacker Ninjas

Project Smoothie Společný projekt dua Hacker Ninjas. První pokus o hříčku po třech týdnech učení se programování. Jakub Kolář e:\

Jakub Kolář 2 Jan 07, 2022
Parameter-ensemble-differential-evolution - Shows how to do parameter ensembling using differential evolution.

Ensembling parameters with differential evolution This repository shows how to ensemble parameters of two trained neural networks using differential e

Sayak Paul 9 May 04, 2022
Pairwise learning neural link prediction for ogb link prediction

Pairwise Learning for Neural Link Prediction for OGB (PLNLP-OGB) This repository provides evaluation codes of PLNLP for OGB link property prediction t

Zhitao WANG 31 Oct 10, 2022
Split Variational AutoEncoder

Split-VAE Split Variational AutoEncoder Introduction This repository contains and implemementation of a Split Variational AutoEncoder (SVAE). In a SVA

Andrea Asperti 2 Sep 02, 2022