The 1st Place Solution of the Google Landmark 2019 Retrieval Challenge and the 3rd Place Solution of the Recognition Challenge.
NOTE: This solution code is not refactored and work in progress at this time. Stay tuned!
Our solution has been published! You can check from: Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset
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
# ABCI command
python vX.py launch-qsub tuning -d 0,1,2,3 --n-gpu 2 --n-blocks 2 -s 1 --instance-type rt_F
python vX.py launch-qsub predict -m vX/ep --ms --scale L2 --batch-size 24 --splits train,test --n-blocks 64
Following commands are for reproducing our results.
cd ./experiments/
bash donwload_train.sh # download data
bash setup.sh # setup data to ready training
bash reproduce.sh # train models and predict for reproducing
python submit_retrieval.py # for retrieval challenge
python submit_recognition.py # for retrieval challenge
- 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