Hi, thank you for sharing your dataset. Living in China, it's almost impossible to download your dataset from Google Drive. It's also stop if we try to use a VPN. Can you provide a direct link to download your dataset?

Thank you

Hi @layumi , thanks for releasing the codes.

When I ran the train.py file (using the resnet model), after initializing with the pretraining model parameters and training for 119 epochs, I ran the test.py file and only got the following results: Recall@1:1.29 Recall@5:4.54 Recall@10:7.43 Recall@top1:7.92 AP:2.53

And when I ran the train.py file using the vgg mode, I got: Recall@1:1.75 Recall@5:6.22 Recall@10:10.36 Recall@top1:11.16 AP:3.39

The hyper-parameters of the above results are set by default. To get the results in the paper, do I need to modify the hyper-parameters in the code?

I use pytorch 1.1.0 and V100

Hello, I've been looking at your code recently. In test.py file, after extracting the features of the image, save result to pytorch__result. mat file, and then run evaluate_ gpu. py file for evaluation. I want to know how to visualize the search results and get the matching results like Figure 5 in the paper.

Hello, First of all, thank you for sharing your great work.

I'm currently doing researches with cross-view geo-localization and I want to collect image data like the University1652 dataset, so I was wondering if you could share some sample codes, or a simple tutorial about how to collect images using Google Earth Engine.

Thank you and best regards.

Hi. I trained using the tutorial readme with this command. python train.py --gpu_ids 0,2 --name ft_ResNet50 --train_all --batchsize 32 --data_dir /home/xx/datasets/University-Release/train And this is the generated yaml

DA: false
batchsize: 32
color_jitter: false
data_dir: /home/paul/datasets/University-Release/train
droprate: 0.5
erasing_p: 0
extra_Google: false
fp16: false
gpu_ids: 0,2
h: 384
lr: 0.01
moving_avg: 1.0
name: ft_ResNet50
nclasses: 701
pad: 10
pool: avg
resume: false
share: false
stride: 2
train_all: true
use_NAS: false
use_dense: false
views: 2
w: 384
warm_epoch: 0

So, for testing, i do this python test.py --gpu_ids 0 --name ft_ResNet50 --test_dir /home/xx/datasets/University-Release/test --batchsize 32 --which_epoch 119 I found out that, the views=2 and the view_index=3 in the extract_feature function. Using this code

def which_view(name):
    if 'satellite' in name:
        return 1
    elif 'street' in name:
        return 2
    elif 'drone' in name:
        return 3
    else:
        print('unknown view')
    return -1

The task is 3 -> 1 means Drone -> Satellite with views=2. But the code in the testing, doesn't consider this scenario

 for scale in ms:
    if scale != 1:
       # bicubic is only  available in pytorch>= 1.1
        input_img = nn.functional.interpolate(input_img, scale_factor=scale, mode='bilinear', align_corners=False)
        if opt.views ==2:
           if view_index == 1:
              outputs, _ = model(input_img, None) 
           elif view_index ==2:
               _, outputs = model(None, input_img) 
        elif opt.views ==3:
           if view_index == 1:
              outputs, _, _ = model(input_img, None, None)
           elif view_index ==2:
                _, outputs, _ = model(None, input_img, None)
            elif view_index ==3:
                    _, _, outputs = model(None, None, input_img)
                ff += outputs # Give error, since outputs is not defined

For views == 2, there is no views_index == 3

Hi, I guess on a Unix/Linux system, such error might not occur. But a file naming similar to the Market-1501 dataset could have been better for Windows based systems. Here an error due to the path length in Windows systems.

Hi, Dr. Zheng. After reading your paper, I want to use t-SNE code, could you release this t-SNE code? I find lots of t-SNE codes on github, but I can not find useful codes of using resnet network or pretrained models. Thanks a lot !!!!

Hello @layumi , thank you for your work

I was trying to reproduce the result in paper "Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective" using your pytorch code, but I'm having some trouble in running the program.

The program needs "market_88_test.pkl" as input data for re-ranking process, but I don't understand how to generate it properly.

Could you give some advices on how to use this code?

Thank you and best regards.