Made changes to val.py that should fix the indices issue that was causing low reported recalls for mapillary validation on the cph and sf datasets. Without PCA, the recalls with NetVLAD (with the mapillary trained model) are now: 0.495, 0.65, 0.718, 0.77, 0.83, 0.868.

Thank you for this nice code base and paper. I am trying to reproduce the result on Mapillary following the training from scratch instruction. However, after training for multiple weeks, the result is still extremely low:

Is the result before PCAW supposed to be like this? Or maybe there is something wrong? I just follow the instruction: python train.py
--config_path patchnetvlad/configs/train.ini
--cache_path=/path/to/your/desired/cache/folder
--save_path=/path/to/your/desired/checkpoint/save/folder
--dataset_root_dir=/path/to/your/mapillary/dataset And there is no error reported. Thank you so much for your time. Looking forward to hearing from you.

Hi, will you release your version of the Nordland dataset? It would be great to have the chance to download the dataset directly, as it would avoid possible inconsistencies. Also, there shouldn't be any legal issue given that it is licensed under Creative Commons :-)

hi :) I installed all packages and tried to run "match_two.py" file. for loading Auto-download pretrained models, it asked me so I type "YES" but i got a error message. [certificate verify failed]

I am using Window PC, please let me know how to solve this. thanks

@Tobias-Fischer @oravus Hi,first,I really appreciate for your work. I'm trying to reproduce results of NetVLAD on RobotCar Seasons v2 in the Table 1 and Supplementary Table 1,but encountered some problems.I have extracted the global descriptor using Netvlad with netvlad_extract.ini.May I ask if it is convenient to supplement the subsequent code to facilitate the reoccurrence work？Perhaps it would be better to have detailed instructions. I am always looking forward to your kind response. Best regards.

Hi, I really appreciate for the work! It is helpful for me to try it with your instructions. But I have a question about training. I try to retrain the network (with original Vgg16 backbone or change it to ResNet18, cropped it properly and maintain most of the parameters as the pretrained weights). I found that the loss went down rapidly at the first epoch and almost reach to 0.00. Would you describe the change of your loss when you train this network? Thank you very much!

Best regards

Dear,

Thank you for the great job!

I got a question about how to use the output of patchnetvlad to train the network while vlad_local and vlad_global are generated at the same time. is it OK to use the vlad_global feature to estimate the triplet loss?

Best, Qiang

Hi, thanks for your work. I have a question about “posDistThr” of Nordland dataset. This repository sets the utm coordinate of i-th frame to (i,i) in the Nordland.npz file, which allows us to have a consistent function for evaluating [email protected] for all datasets. It's great! The tolerance for Nordland dataset is 10 frames in the Patch-NetVLAD paper. However, the radius of the first frame (0,0) to the 11-th frame (10,10) is 10sqrt(2) rather than 10. The “posDistThr” is set to 10 in Nordland.npz file in this repository. I have wrote a script to calculate [email protected] on Nordland (tolerance is 2 frames). The result is consistent with setting the “posDistThr” to 2sqrt(2) rather than 2.

Hi, thank you for sharing this great work. In the official paper, DELG achieves the top performance on some datasets. May I know how to reproduce those results of DELG? Do you use the pre-trained model or fine-tune it on Pitts30k/MSLS?

Hi, I tried to use your pretrained pittsburgh_WPCA4096 model to test on Aachen Day-Night dataset, but got totally incorrect result. here is my complete process.

1. generate Aachen_db_path.txt and Aachen_query_path.txt

2. run the feature_extract.py for db and query images use following two commands.

python feature_extract.py \
  --config_path patchnetvlad/configs/speed.ini \
  --dataset_file_path=Aachen_db_path.txt \
  --output_features_dir patchnetvlad/output_features/aachen_index

python feature_extract.py \
  --config_path patchnetvlad/configs/speed.ini \
  --dataset_file_path=Aachen_query_path.txt \
  --output_features_dir patchnetvlad/output_features/aachen_query

3. run the 'feture_match.py' for feature matching without providing ground_truth_path

python feature_match.py \
  --config_path patchnetvlad/configs/speed.ini \
  --query_file_path=Aachen_query_path.txt \
  --index_file_path=Aachen_db_path.txt \
  --query_input_features_dir patchnetvlad/output_features/aachen_query \
  --index_input_features_dir patchnetvlad/output_features/aachen_index \
  --result_save_folder patchnetvlad/results/aachen_subset

3. then process PatchNetVLAD_predictions.txt, for each query image it output top 100 reference images. I just used the pose of top1 reference image as the estimated pose of each query image and post the result to benchmark, but got totally wrong result. here is the result:

I want to know what's wrong with my steps and why it outputs such unacceptable results. Is the model version that I used is wrong? Or have you test your models on Aachen dataset, if you did, could you please provide the result ?

Hi, I really appreciate for your work. I'm trying to reproduce your result on RobotCar seasons v2 dataset and encountered some problems. Which dataset split is used in the Tables 1 and 2 of the main paper, test or train? Besides, are these results integrate ALL conditions including day and night?

I noticed that the Suppl. Table 1. reports results for each condition obtained from the training split. But when I tried to summary these results in Suppl. Table 1. using statistics on the train query set, I failed to obtain the results in the Tables 1 of the main paper. So I'm a bit confused about the dataset settings.

I am always looking forward to your kind response.

Best regards,

Hi, I tried to reproduce your result on Robotcar Seasons V2 test set by submitting to the challenge submission server. I used the released performance-focused model which is pre-trained on MSLS dataset, but I got this incorrect result: And I tried the model pre-trained on Pitts30k, the results are not correct either. Besides, the results on other datasets is normal. Is the model version that I used is wrong? Could you possibly release the model state that achieves the results on Robotcat dataset shown in the paper? Or would you provide the results on test set split by conditions like the Supplementary Table 1? Thank you so much.

Best regards,

Hi, I really appreciate for the work you've done. I wondered if the training code will be released, which can be used to train our custom dataset, thanks.

Best regards.