README.md

---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
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paperswithcode_id: lasot
---

# Dataset Card for LaSOT

## Dataset Description

- **Homepage:** [LaSOT homepage](http://vision.cs.stonybrook.edu/~lasot/)
- **Paper:** [LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://arxiv.org/abs/1809.07845)
- **Point of Contact:** [Heng Fan]([email protected])

### Dataset Summary

**La**rge-scale **S**ingle **O**bject **T**racking (**LaSOT**) aims to provide a dedicated platform for training data-hungry deep trackers as well as assessing long-term tracking performance.

This repository contains the conference version of LaSOT, published in CVPR-19 ([LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://arxiv.org/abs/1809.07845)).

**LaSOT** is featured in:

- **Large-scale**: 1,400 sequences with more than 3.5 millions frames
- **High-quality**: Manual annotation with careful inspection in each frame
- **Category balance**: 70 categories, each containing 20 sequences
- **Long-term tracking**: An average video length of around 2,500 frames (i.e., 83 seconds)
- **Comprehensive labeling**: Providing both visual and lingual annotation for each sequence

For the new subset (15 categories with 150 videos) in [extended journal version](https://arxiv.org/abs/2009.03465) (commonly referred to as LaSOT<sub>ext</sub>), visit this [repo](https://huggingface.co/datasets/l-lt/LaSOT-ext).


## Download

You can download the whole dataset via the ```huggingface_hub``` library ([guide](https://huggingface.co/docs/huggingface_hub/guides/download)):

```python
from huggingface_hub import snapshot_download
snapshot_download(repo_id='l-lt/LaSOT', repo_type='dataset', local_dir='/path/to/download')
```

Alternatively, download the videos of a specific category manually from this [page](https://huggingface.co/datasets/l-lt/LaSOT/tree/main).

LaSOT is also distributed through several cloud storage services (currently only OneDrive):

* As a single zip file: [OneDrive](https://1drv.ms/u/s!Akt_zO4y_u6DgoQsxl9ixr5Y393qWA?e=7yTwjc)

* As one zip file per category: [OneDrive](https://1drv.ms/f/s!Akt_zO4y_u6DgoNSoMJrfnVwveDjhA?e=PBeyuD) or [Baidu Pan](https://pan.baidu.com/s/1xFANiqkBHytE7stMOLUpLQ)

### Setup

Unzip all zip files and the paths should be organized as following:
```
├── airplane
│   ├── airplane-1
│   ...
├── basketball
...
├── training_set.txt
└── testing_set.txt
```

## Evaluation Metrics and Toolkit

See the [homepage](http://vision.cs.stonybrook.edu/~lasot/results.html) for more information.