lu-feng / selavpr Goto Github PK

Thank you for sharing your work, your paper was very interesting and the results are also very impressive!

I had a question regarding the evaluation on MSLS-val. I attempted to reproduce your results by following the repository, downloading the data, and training the model as described in the README. Initially, I trained the model solely on the MSLS dataset. I attempted to evaluate the results on MSLS by executing the following command for both my trained model and the provided trained model:

python3 eval.py --datasets_folder=/path/to/your/datasets_vg/datasets --dataset_name=msls --resume=/path/to/finetuned/msls/model/SelaVPR_msls.pth --rerank_num=100

However, these were the results that I obtained:

Further fine-tuning the model on Pitts30k and evaluating it gave the same results as you had in your README for evaluation on Pitts30k. Therefore, I'm wondering if you could help me understand why there's a difference for the MSLS-val. Am I evaluating with the wrong data, or is there something else I might be missing?

Thanks for sharing your work, your paper was very interesting and the results are also very impressive!
i have a question that how to view the keypoint matching in picture, the variable kps is defined but bot used, could you please tell me how to get the matching keypoints? thank you very much!

Thank you for sharing your work, your paper was very interesting and the results are also very impressive!When I trained the pre-trained model on the msls datasets,I met the following problem.Because the RAM was out of memory,my programa was killed by the system.How can I solve the issue?

感谢作者的工作，恭喜！

还没有跑代码，但有几个疑问：

1. readme中说rerank_num设置为20的速度为0.018s，请问一下图像分辨率和使用的机器配置是怎样？完整的SelaVPR（带reranking）方法和mixvpr在速度上对比如何？
   2.该方法使用到不同数据集时候，都需要finetune吗？还是说直接把预训练模型拿来用即可？（比如都是室外街景数据）
2. 完整的SelaVPR（带reranking）方法，在室内数据集上效果如何？

期待回答，感谢。

self.database_utms = np.array([(path.split("@")[1], path.split("@")[2]) for path in self.database_paths]).astype(np.float)
self.queries_utms = np.array([(path.split("@")[1], path.split("@")[2]) for path in self.queries_paths]).astype(np.float)
I don't understand these two lines of code