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README.md
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license: mit
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---
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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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- [How to Get Started with the Model](#how-to-get-started-with-the-model)
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- [Uses](#uses)
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- [Direct Use](#direct-use)
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- [Downstream Use](#downstream-use)
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- [Misuse and Out-of-scope Use](#misuse-and-out-of-scope-use)
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- [Limitations and Biases](#limitations-and-biases)
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- [Training](#training)
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- [Training Data](#training-data)
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- [Training Procedure](#training-procedure)
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- [Evaluation Results](#evaluation-results)
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- [Environmental Impact](#environmental-impact)
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- [Citation Information](#citation-information)
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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 apache-2.0 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>
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