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---
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> |