Update README.md

README.md

@@ -1,3 +1,71 @@
 ---
 license: mit
+tags:
+- object-detection
+- object-tracking
+- video
+- video-object-segmentation
+inference: false
 ---
+
+# unicorn_track_large_mot_challenge_mask 
+
+## Table of Contents
+- [unicorn_track_large_mot_challenge_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)
+
+This model can simultaneously deal with SOT, MOT17, VOS, and MOTS Challenge
+
+<Eval_Results>
+
+## Evaluation Results
+
+MOT17 MOTA (%): 77.2
+MOTS sMOTSA (%): 65.3
+
+</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>