Pytorch implementation of NetVlad including training on Pittsburgh.
Implementation of NetVlad in PyTorch, including code for training the model on the Pittsburgh dataset.
Below are the result as compared to the results in third row in the right column of Table 1:
| |[email protected]|[email protected]|[email protected]| |---|---|---|---| | NetVlad paper | 84.1 | 94.6 | 95.5 | | pytorch-NetVlad(alexnet) | 68.6 | 84.6 | 89.3 | | pytorch-NetVlad(vgg16) | 85.2 | 94.8 | 97.0 |
Running main.py with train mode and default settings should give similar scores to the ones shown above. Additionally, the model state for the above run is available here: https://drive.google.com/open?id=17luTjZFCX639guSVy00OUtzfTQo4AMF2
Using this checkpoint and the following command you can obtain the results shown above:
python main.py --mode=test --split=val --resume=vgg16_netvlad_checkpoint/
pittsburgh.pycontains a hardcoded path to a directory, where the code expects directories
010with the various Pittsburth database images, a directory
010with the query images, and a directory
datasetswith the dataset specifications (.mat files).
main.pycontains the majority of the code, and has three different modes (
cluster) which we'll discuss in mode detail below.
In order to initialise the NetVlad layer it is necessary to first run
main.pywith the correct settings and
--mode=cluster. After which a model can be trained using (the following default flags):
python main.py --mode=train --arch=vgg16 --pooling=netvlad --num_clusters=64
The commandline args, the tensorboard data, and the model state will all be saved to
opt.runsPath, which subsequently can be used for testing, or to resuming training.
For more information on all commandline arguments run:
python main.py --help
To test a previously trained model on the Pittsburgh 30k testset (replace directory with correct dir for your case):
python main.py --mode=test --resume=runsPath/Nov19_12-00-00_vgg16_netvlad --split=test
The commandline arguments for training were saved, so we shouldnt need to specify them for testing. Additionally, to obtain the 'off the shelf' performance we can also omit the resume directory:
python main.py --mode=test
In order to initialise the NetVlad layer we need to first sample from the data and obtain
opt.num_clusterscentroids. This step is necessary for each configuration of the network and for each dataset. To cluster simply run
python main.py --mode=cluster --arch=vgg16 --pooling=netvlad --num_clusters=64
with the correct values for any additional commandline arguments.