REPRODUCTION.md

Train

All the training details (hyper-parameters, trained layers, backbones, etc.) strictly follow the original MatConvNet version of NetVLAD and SARE.

In order to accomodate computation efficiency, we use small channel dimensions (64*3) and feature sizes of 128/2 X 128/2. We encourage you to change the channel dimensions (L_DIM, M_DIM, H_DIM) in train.sh to increase the performance.

bash train.sh

The default scripts adopt 4 Tesla V100 GPUs (require ~32G per GPU) for training, where each GPU loads one tuple (anchor, positive(s), negatives).

• In case you want to fasten training, enlarge GPUS for more GPUs, or enlarge the --tuple-size for more tuples on one GPU;
• In case your GPU does not have enough memory (e.g. <32G), reduce --neg-num for fewer positives or negatives in one tuple.

Test

During testing, the python scripts will automatically compute the PCA weights from Pitts30k-train or directly load from local files. Generally, model_best.pth which is selected by validation in the training performs the best.

Set the channel dimensions (L_DIM, M_DIM, H_DIM) in test.sh and run the test script with <MODEL PATH>, e.g. 'logs/saved_models/pitts30k-vgg16/conv5-sare_joint-lr0.001-tuple4/model_best.pth':

bash test.sh <MODEL PATH>

The default scripts adopt 4 Tesla V100 GPUs (require ~32G per GPU) for testing.

• In case you want to fasten training, enlarge GPUS for more GPUs, or enlarge the --test-batch-size for larger batch size on one GPU;
• In case your GPU does not have enough memory (e.g. <32G), reduce --test-batch-size for smaller batch size on one GPU.