# mapillary_slss

**Repository Path**: klysxc616/mapillary_slss

## Basic Information

- **Project Name**: mapillary_slss
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: automated_fixup_code_of_conduct_file_exists
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-12-27
- **Last Updated**: 2021-12-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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
- 📄 Paper: https://research.mapillary.com/publication/cvpr20c


## 🔥 Using MSLS

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


#### 📊 Standalone evaluation script

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


## 📦 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)