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 was published! You can check from: Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset
Following commands are for reproducing our results.
bash donwload_train.sh # download data
bash setup.sh # setup data to ready training
bash reproduce.sh # train models and predict for reproducing
- 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