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}
}
Sionna: An Open-Source Library for Next-Generation Physical Layer Research

Sionna: An Open-Source Library for Next-Generation Physical Layer Research Sionna™ is an open-source Python library for link-level simulations of digi

NVIDIA Research Projects 313 Dec 22, 2022
Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks"

LUNAR Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks" Adam Goodge, Bryan Hooi, Ng See Kiong and

Adam Goodge 25 Dec 28, 2022
Image data augmentation scheduler for albumentations transforms

albu_scheduler Scheduler for albumentations transforms based on PyTorch schedulers interface Usage TransformMultiStepScheduler import albumentations a

19 Aug 04, 2021
Prometheus exporter for Cisco Unified Computing System (UCS) Manager

prometheus-ucs-exporter Overview Use metrics from the UCS API to export relevant metrics to Prometheus This repository is a fork of Drew Stinnett's or

Marshall Wace 6 Nov 07, 2022
Official Pytorch Implementation of Unsupervised Image Denoising with Frequency Domain Knowledge

Unsupervised Image Denoising with Frequency Domain Knowledge (BMVC 2021 Oral) : Official Project Page This repository provides the official PyTorch im

Donggon Jang 12 Sep 26, 2022
A minimal TPU compatible Jax implementation of NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

NeRF Minimal Jax implementation of NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. Result of Tiny-NeRF RGB Depth

Soumik Rakshit 11 Jul 24, 2022
Language Models for the legal domain in Spanish done @ BSC-TEMU within the "Plan de las Tecnologías del Lenguaje" (Plan-TL).

Spanish legal domain Language Model ⚖️ This repository contains the page for two main resources for the Spanish legal domain: A RoBERTa model: https:/

Plan de Tecnologías del Lenguaje - Gobierno de España 12 Nov 14, 2022
Efficient Multi Collection Style Transfer Using GAN

Proposed a new model that can make style transfer from single style image, and allow to transfer into multiple different styles in a single model.

Zhaozheng Shen 2 Jan 15, 2022
Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT

CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT CheXbert is an accurate, automated dee

Stanford Machine Learning Group 51 Dec 08, 2022
[NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods Large Scale Learning on Non-Homophilous Graphs: New Benchmark

60 Jan 03, 2023
Code for the paper Open Sesame: Getting Inside BERT's Linguistic Knowledge.

Open Sesame This repository contains the code for the paper Open Sesame: Getting Inside BERT's Linguistic Knowledge. Credits We built the project on t

9 Jul 24, 2022
Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth

Instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth This codebase implements the loss function described in: Insta

209 Dec 07, 2022
Permeability Prediction Via Multi Scale 3D CNN

Permeability-Prediction-Via-Multi-Scale-3D-CNN Data: The raw CT rock cores are obtained from the Imperial Colloge portal. The CT rock cores are sub-sa

Mohamed Elmorsy 2 Jul 06, 2022
classify fashion-mnist dataset with pytorch

Fashion-Mnist Classifier with PyTorch Inference 1- clone this repository: git clone https://github.com/Jhamed7/Fashion-Mnist-Classifier.git 2- Instal

1 Jan 14, 2022
[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.

[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.

VITA 112 Nov 07, 2022
An Implementation of SiameseRPN with Feature Pyramid Networks

SiameseRPN with FPN This project is mainly based on HelloRicky123/Siamese-RPN. What I've done is just add a Feature Pyramid Network method to the orig

3 Apr 16, 2022
ChatBot-Pytorch - A GPT-2 ChatBot implemented using Pytorch and Huggingface-transformers

ChatBot-Pytorch A GPT-2 ChatBot implemented using Pytorch and Huggingface-transf

ParZival 42 Dec 09, 2022
7th place solution of Human Protein Atlas - Single Cell Classification on Kaggle

kaggle-hpa-2021-7th-place-solution Code for 7th place solution of Human Protein Atlas - Single Cell Classification on Kaggle. A description of the met

8 Jul 09, 2021
Tello Drone Trajectory Tracking

With this library you can track the trajectory of your tello drone or swarm of drones in real time.

Kamran Asgarov 2 Oct 12, 2022
Artificial Intelligence playing minesweeper 🤖

AI playing Minesweeper ✨ Minesweeper is a single-player puzzle video game. The objective of the game is to clear a rectangular board containing hidden

Vaibhaw 8 Oct 17, 2022