readme: ---
  license: mit
  tags:
  - object-detection
  - object-tracking
  - video
  - video-object-segmentation
  inference: false
  ---
  
  # unicorn_track_tiny_mask 
  
  ## Table of Contents
  - [unicorn_track_tiny_mask](#-model_id--defaultmymodelname-true)
    - [Table of Contents](#table-of-contents)
    - [Model Details](#model-details)
    - [Uses](#uses)
        - [Direct Use](#direct-use)
    - [Evaluation Results](#evaluation-results)
  
  
  <model_details>
  
  ## Model Details
  
  Unicorn accomplishes the great unification of the network architecture and the learning paradigm for four tracking tasks. Unicorn puts forwards new state-of-the-art performance on many challenging tracking benchmarks using the same model parameters. This model has an input size of 800x1280.
  
  - License: This model is licensed under the MIT license
  - Resources for more information:
    - [Research Paper](https://arxiv.org/abs/2111.12085)
    - [GitHub Repo](https://github.com/MasterBin-IIAU/Unicorn)
  
  </model_details>
  
  <uses>
  
  ## Uses
  
  #### Direct Use
  
  This model can be used for:
  
  * Single Object Tracking (SOT)
  * Multiple Object Tracking (MOT)
  * Video Object Segmentation (VOS)
  * Multi-Object Tracking and Segmentation (MOTS)
  
  <Eval_Results>
  
  ## Evaluation Results
  
  LaSOT AUC (%): 67.7
  BDD100K mMOTA (%): 39.9
  DAVIS17 J&F (%): 68.0
  BDD100K MOTS mMOTSA (%): 29.7
  
  
  </Eval_Results>
  
  <Cite>
  
  ## Citation Information
  
  ```bibtex
  @inproceedings{unicorn,
    title={Towards Grand Unification of Object Tracking},
    author={Yan, Bin and Jiang, Yi and Sun, Peize and Wang, Dong and Yuan, Zehuan and Luo, Ping and Lu, Huchuan},
    booktitle={ECCV},
    year={2022}
  }
  ```
  </Cite>