
# 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>
{"license": "mit", "tags": ["object-detection", "object-tracking", "video", "video-object-segmentation"], "inference": false}
object-detection object-tracking video video-object-segmentation arxiv:2111.12085 license:mit region:us
TAGS
#object-detection #object-tracking #video #video-object-segmentation #arxiv-2111.12085 #license-mit #region-us 


# unicorn_track_tiny_mask 

## Table of Contents
- unicorn_track_tiny_mask
  - Table of Contents
  - Model Details
  - Uses
      - Direct Use
  - 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
  - GitHub Repo

</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>




</Cite>
# unicorn_track_tiny_mask ## Table of Contents
- unicorn_track_tiny_mask
  - Table of Contents
  - Model Details
  - Uses
      - Direct Use
  - 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
  - GitHub Repo

</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>




</Cite>
TAGS
#object-detection #object-tracking #video #video-object-segmentation #arxiv-2111.12085 #license-mit #region-us 
 # unicorn_track_tiny_mask ## Table of Contents
- unicorn_track_tiny_mask
  - Table of Contents
  - Model Details
  - Uses
      - Direct Use
  - 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
  - GitHub Repo

</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>




</Cite>
passage: TAGS
#object-detection #object-tracking #video #video-object-segmentation #arxiv-2111.12085 #license-mit #region-us 
# unicorn_track_tiny_mask## Table of Contents
- unicorn_track_tiny_mask
  - Table of Contents
  - Model Details
  - Uses
      - Direct Use
  - 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
  - GitHub Repo

</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>




</Cite>