Update README.md

README.md

@@ -1,3 +1,46 @@
----
-license: mit
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+---
+license: mit
+---
+
+# TimeChat-7B-ActivityNet-VTune Model
+
+## Model details
+
+We trained [VideoLLaMA](https://arxiv.org/abs/2306.02858) using VTune, a developed instruction-tuning method specifically designed to account for consistency. 
+
+For the tuning, we utilized 10K training videos from ActivityNet-Captions with 205K automatically generated annotations.
+
+## Evaluation
+We evaluated the model on ActivtyNet-CON and ActivtyNet-Captions.
+
+- ActivityNet-CON
+| Metric          | Value       |
+|-----------------|-------------|
+| Ground          | 33.0        |
+| R-Ground        | 24.7 (74.8) |
+| S-Ground        | 10.0 (30.2) |
+| H-Verify        | 20.2 (61.1) |
+| C-Verify        | 17.7 (53.7) |
+
+- ActivityNet-Captions
+| Metric          | Value   |
+|-----------------|---------|
+| R@1 IoU=0.3     | 51.58   |
+| R@1 IoU=0.5     | 34.38   |
+| R@1 IoU=0.7     | 19.18  |
+| mIoU            | 36.16   |
+
+**Paper and Code for more information:**
+[Paper](https://arxiv.org/abs/2411.12951), [Code](https://github.com/minjoong507/consistency-of-video-llm)
+
+## Citation
+If you find our research and codes useful, please consider starring our repository and citing our paper:
+
+```
+@article{jung2024consistency,
+  title={On the Consistency of Video Large Language Models in Temporal Comprehension},
+  author={Jung, Minjoon and Xiao, Junbin and Zhang, Byoung-Tak and Yao, Angela},
+  journal={arXiv preprint arXiv:2411.12951},
+  year={2024}
+}
+```