Downloads - GOT-10k

Download our light-weighted, compile-free tracking toolkits or full dataset here.


The benchmark offers light-weighted and compile-free toolkits written in pure Python and MATLAB. You will find tutorials and examples in the corresponding repositories.


To download GOT-10k files, please click one of the following links and provide your email address in the new page. We will send you an email with an link to your download.

Data File Structure

The downloaded and extracted full dataset should follow the file structure:

  |-- GOT-10k/
     |-- train/
     |  |-- GOT-10k_Train_000001/
     |  |   ......
     |  |-- GOT-10k_Train_009335/
     |  |-- list.txt
     |-- val/
     |  |-- GOT-10k_Val_000001/
     |  |   ......
     |  |-- GOT-10k_Val_000180/
     |  |-- list.txt
     |-- test/
     |  |-- GOT-10k_Test_000001/
     |  |   ......
     |  |-- GOT-10k_Test_000180/
     |  |-- list.txt

Annotation Description

Each sequence folder contains 4 annotation files and 1 meta file. A brief description of these files follows (let N denotes sequence length):

Values 0~8 in file cover.label correspond to ranges of object visible ratios: 0%, (0%, 15%], (15%~30%], (30%, 45%], (45%, 60%], (60%, 75%], (75%, 90%], (90%, 100%) and 100% respectively.


The GOT-10k dataset is licensed under CC BY-NC-SA 4.0. You are free to use the dataset for research purpose. If you want to use it for commercial purpose, please contact us.


Download baseline tracking results and performance reports of 25 public entries on GOT-10k from the following links:


Please cite this paper if you use the code or datasets:


GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild.
L. Huang*, X. Zhao*, and K. Huang. ( *Equal contribution)
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
[PDF] [BibTex]