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**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.
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This
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**LaSOT** is featured in:
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## Download
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You can download the whole dataset
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```
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```
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Alternatively, download the videos of a specific category manually from this [page](https://huggingface.co/datasets/l-lt/LaSOT/tree/main).
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LaSOT is also distributed through
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*
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*
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###
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Unzip all zip files and the paths should be organized as following:
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```
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**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.
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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)).
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**LaSOT** is featured in:
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## Download
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You can download the whole dataset via the ```huggingface_hub``` library ([guide](https://huggingface.co/docs/huggingface_hub/guides/download)):
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```python
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from huggingface_hub import snapshot_download
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snapshot_download(repo_id='l-lt/LaSOT', repo_type='dataset', local_dir='/path/to/download')
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```
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Alternatively, download the videos of a specific category manually from this [page](https://huggingface.co/datasets/l-lt/LaSOT/tree/main).
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LaSOT is also distributed through several cloud storage services (currently only OneDrive):
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* As a single zip file: [OneDrive](https://1drv.ms/u/s!Akt_zO4y_u6DgoQsxl9ixr5Y393qWA?e=7yTwjc)
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* 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)
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### Setup
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Unzip all zip files and the paths should be organized as following:
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```
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