Awesome

Mapillary Street-level Sequences

:newspaper: News

2020-07-14 - Released patch v1.1 fixing some corrupt images - you will receive a link to download it if you already requested the data.

Description

Mapillary Street-level Sequences (MSLS) is a large-scale long-term place recognition dataset that contains 1.6M street-level images.

⬇️ Download: https://www.mapillary.com/dataset/places (sample here)
📄 Paper: https://research.mapillary.com/publication/cvpr20c
️🧑‍⚖️ Code of Conduct
🗳️ Contributing / Pull Requests

🔥 Using MSLS

We've included an implementation of a PyTorch Dataset in datasets/msls.py. It can be used for evaluation (returning database and query images) or for training (returning triplets). Check out the demo to understand its usage.

📊 Standalone evaluation script

A standalone evaluation script is available for all tasks. It reads the predictions from a text file (example) and prints the metrics.

Here we show results of models consisting of a Resnet50 backbone followed by Generalized Mean Layer. The models are trained with either the standard triplet loss or the uncertainty-aware Bayesian triplet loss. All models are trained with standard hard negative mining on image resolution 224x224.

Results on test set (Miami, Athens, Buenos Aires, Stockholm, Bengaluru, Kampala):

Loss	R@1	R@5	R@10	R@20	M@1	M@5	M@10	M@20
Triplet Loss	0.372	0.522	0.582	0.636	0.372	0.261	0.234	0.228
Bayesian Triplet Loss	0.366	0.513	0.574	0.629	0.366	0.253	0.229	0.222

Results on validation set (San Francisco, Copenhagen)

Loss	R@1	R@5	R@10	R@20	M@1	M@5	M@10	M@20
Triplet Loss	0.623	0.780	0.830	0.859	0.623	0.432	0.380	0.372
Bayesian Triplet Loss	0.618	0.746	0.805	0.839	0.618	0.419	0.369	0.360

📦 Package structure

images_vol_X.zip: images, split into 6 parts for easier download.
metadata.zip: a single zip archive containing the metadata.
patch_vX.Y.zip: unzip any patches on top of the dataset to upgrade.

All the archives can be extracted in the same directory resulting in the following tree:

train_val
- city
  - query / database
    - images/key.jpg
    - seq_info.csv
    - subtask_index.csv
    - raw.csv
    - postprocessed.csv
test
- city
  - query / database
    - images/key.jpg
    - seq_info.csv
    - subtask_index.csv

The meta files include the following information:

raw.csv: raw data recorded during capture
- key
- lon
- lat
- ca
- captured_at
- pano
seq_info.csv: Sequence information
- key
- sequence_id
- frame_number
postprocessed.csv: Data derived from the raw images and metadata
- key
- utm (easting and northing)
- night
- control_panel
- view_direction (Forward, Backward, Sideways)
- unique_cluster
subtask_index.csv: Precomputed image indices for each subtask in order to evaluate models on (all, summer2winter, winter2summer, day2night, night2day, old2new, new2old)

License

This repository is MIT licensed.