Landmark2019-1st-and-3rd-Place-Solution

The 1st Place Solution of the Google Landmark 2019 Retrieval Challenge and the 3rd Place Solution of the Recognition Challenge.

Our solution has been published! You can check from: Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset

Environments

You can reproduce our environments using Dockerfile provided here https://github.com/lyakaap/Landmark2019-1st-and-3rd-Place-Solution/blob/master/docker/Dockerfile

Data

• Google Landmark Dataset v1 (GLD-v1): https://www.kaggle.com/google/google-landmarks-dataset
• Google Landmark Dataset v2 (GLD-v2): https://github.com/cvdfoundation/google-landmark
• Clean version of the v2 (Newly Released!): https://www.kaggle.com/confirm/cleaned-subsets-of-google-landmarks-v2/kernels

Dataset statistics:

Dataset (train split)	# Samples	# Labels
GLD-v1	1,225,029	14,951
GLD-v2	4,132,914	203,094
GLD-v2 (clean)	1,580,470	81,313

Prepare cleaned subset

(You can skip this procedure to generate a cleaned subset. Pre-computed files are available on kaggle dataset.)

Run scripts/prepare_cleaned_subset.sh for cleaning the GLD-v2 dataset. The cleaning code requires DELF library (install instructions).

Reproduce

Prepare FishNet pretrained checkpoints first.

1. Download the checkpoint of FishNet-150 from https://www.dropbox.com/s/ajy9p6f97y45f1r/fishnet150_ckpt_welltrained.tar?dl=0
2. Place the checkpoint src/FishNet/checkpoints/
3. Execute src/FishNet/fix_checkpoint.py

Following commands are for reproducing our results.

cd ./experiments/
bash download_train.sh # download data
python ../src/prepare_dataset.py  # setup data to ready for training
bash reproduce.sh  # train models and predict for reproducing

python submit_retrieval.py  # for retrieval challenge
python submit_recognition.py  # for recognition challenge

Experiments

Model training and inference are done in experiments/ directory.

# train models by various parameter settings with 4 gpus (each training is done with 2 gpus).
python vX.py tuning -d 0,1,2,3 --n-gpu 2

# predict
python vX.py predict -m vX/epX.pth -d 0
# predict with multiple gpus
python vX.py multigpu-predict -m vX/epX.pth --scale L2 --ms -b 32 -d 0,1

Results

Place	Team	Private	Public
1st	smlyaka (ours)	37.23	35.69
2nd	imagesearch	34.75	32.25
3rd	Layer 6 AI	32.18	29.85

Reference

• https://github.com/filipradenovic/cnnimageretrieval-pytorch/tree/master/cirtorch
• https://github.com/ronghuaiyang/arcface-pytorch/blob/master/models/metrics.py
• https://github.com/4uiiurz1/pytorch-adacos/blob/master/metrics.py
• https://github.com/kevin-ssy/FishNet
• https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/data/samplers/grouped_batch_sampler.py