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View Code? Open in Web Editor NEW[ICCV 2021] Official PyTorch implementation for Deep Relational Metric Learning.
[ICCV 2021] Official PyTorch implementation for Deep Relational Metric Learning.
Hi! Thanks for your interesting works.
I am a beginner in the field of Deep Metal Learning.
From the code, Recall@1 is calculated. I would like to ask how to see images fail to be recalled ( fail examples in Recall@1). That is, when the DRML model searches for an image, I want to see images that have not been successfully searched. Which file should I write this code from?
Once again, I'm a very beginner. The scale of this code feels large, and it is difficult to grasp the structure. If you answer, it will be of great help. Thank you.
while running
CUDA_VISIBLE_DEVICES=1 python examples/demo.py --data_path ./ --save_path ./output --device 0 --batch_size 180 --test_batch_size 180 --setting proxy_anchor_drml --embeddings_dim 512 --features_dim 1024 --branch_num 4 --proxyanchor_margin 0.2 --proxyanchor_alpha 64 --num_classes 100 --wd 0.0001 --gamma 0.5 --step 10 --lr_trunk 0.00005 --lr_embedder 0.001 --lr_collector 0.01 --weight_recon_loss 1 --weight_repre_loss 10 --dataset cub200 --delete_old --warm_up 1 --warm_up_list embedder ensemble repre
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