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
- Google Landmark Dataset v1: https://www.kaggle.com/google/google-landmarks-dataset
- Google Landmark Dataset 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
You can skip this procedure to generate a cleaned subset. Pre-computed files are available on kaggle dataset.
To be able to use this code, please follow these instructions to properly install the DELF library.
bash scripts/prepare_cleaned_subset.sh
Prepare FishNet pretrained checkpoints first.
- Download the checkpoint of FishNet-150 from https://www.dropbox.com/s/ajy9p6f97y45f1r/fishnet150_ckpt_welltrained.tar?dl=0
- Place the checkpoint
src/FishNet/checkpoints/ - Execute src/FishNet/fix_checkpoint.py
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
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
- 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