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