Comments (3)
can you give me the url, thank you!
I have found it, the Pittsburgh dataset, the url is here: https://data.ciirc.cvut.cz/public/projects/2015netVLAD/Pittsburgh250k/
Besides, pitts30k_test is included in the file netvlad_v100_datasets.tar.gz.
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Hello @Eidell,
You can request the dataset from https://github.com/Relja/netvlad by emailing Relja.
For evaluation on the Pitts30k-test and many other benchmarks, please refer to the following tutorial in our gsv-cities repo: https://github.com/amaralibey/gsv-cities/blob/main/notebooks/evaluate_model.ipynb
Best regards,
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你能给我网址吗,谢谢!
我找到了它,匹兹堡数据集,网址在这里: https://data.ciirc.cvut.cz/public/projects/2015netVLAD/Pittsburgh250k/ 此外,pitts30k_test包含在文件netvlad_v100_datasets.tar.gz中。
Can you tell me how to download these contents about pitts30k
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Related Issues (20)
- why is the skip connection executed inside the FeatureMixerLayer here? HOT 2
- Really a good work with simple but effective approach!
- Training loss and generalization during test HOT 4
- Just for testing HOT 2
- A singleGPU will run the results, but multiple GPUs will make an error!! HOT 3
- can you provide more detailed comparative data? HOT 2
- optimizer step HOT 2
- question about backbone ‘Swin’ HOT 5
- Multi-similarity mining on Pittsburgh30k training set HOT 6
- how to change query image shape? HOT 1
- The error samples are due to issues with the ground truth annotations rather than errors in the model predictions. HOT 6
- Dataset HOT 4
- About the specific number of images of the Mapillary Challenge dataset
- would you release the resnet18 pretrained model?
- How to evaluate this model?
- Some questions
- About pitts30k_val.mat. I can not find it in PittsburgDataset HOT 1
- License File
- Releasing the model on torch.hub?
- On releasing the trained weights of ResNet50 with 2048 dimensionality.
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