About test features

[Peanut736]

Hi, thanks for your code first. In ./test/roxford5k folder, you use Filip Radenovic's code to extract feature and the features saved the gl18-tl-resnet101-gem-w.pkl file?

[Peanut736]

The file just had the gallery and query features? The feature's size is dim*num?

[Peanut736]

Can you offer the ./r1m gl18-tl-resnet101-gem-w.pkl and rSfM120k-tl-resnet101-gem-w.pkl file?
@MCC-WH

[gu6225ha-s]

I created a fork of the cnnimageretrieval-pytorch repository where I added some functionality to generate test data for CSA.

More specifically, you can run the following commands to create the feature vector files for roxford5k, rparis6k and rsfm120k:

python -m cirtorch.examples.test --network-path gl18-tl-resnet101-gem-w --datasets roxford5k --csa-output-dir data/test/ --multiscale '[1, 2**(1/2), 1/2**(1/2)]'
python -m cirtorch.examples.test --network-path gl18-tl-resnet101-gem-w --datasets rparis6k --csa-output-dir data/test/ --multiscale '[1, 2**(1/2), 1/2**(1/2)]'
python -m cirtorch.examples.test --network-path gl18-tl-resnet101-gem-w --datasets rsfm120k --csa-output-dir data/train/ --multiscale '[1, 2**(1/2), 1/2**(1/2)]'

These files give the following mAP values which are very close to those reported in the README:

>> Test Dataset: roxford5k *** fist-stage >>
>> gl18-tl-resnet101-gem-w: mAP Eeay: 84.42, Medium: 67.31, Hard: 44.26
>> gl18-tl-resnet101-gem-w: mP@k[1, 5, 10] Easy: [97.06 91.76 87.04], Medium: [95.71 90.29 84.57], Hard: [87.14 70.29 59.57]
>> Test Dataset: rparis6k *** fist-stage >>
>> gl18-tl-resnet101-gem-w: mAP Eeay: 92.83, Medium: 80.5, Hard: 61.36
>> gl18-tl-resnet101-gem-w: mP@k[1, 5, 10] Easy: [98.57 96.   95.29], Medium: [100.    98.    96.86], Hard: [97.14 93.43 90.43]

[MCC-WH]

I apologize for not replying to the message before. You are right, we also use the same model with you to extract image features. gl18-tl-resnet101-gem-w.pkl and rSfM120k-tl-resnet101-gem-w.pkl files contain features extracted by different models, which are in size of N x d. You should prepare these data prior to training and testing. The minor performance differences might be caused by different computing platforms and pytorch versions.