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-**Evaluating and Extending Unsupervised VideoSummarization Methods**
+## Evaluating and Extending Unsupervised VideoSummarization Methods
 
 1. Evaluation unsupervised methods with different metric under same configuration.
 2. Investigating effect of extracted features in unsupervised methods and extend it to perform better than baseline work.
@@ -23,3 +23,34 @@ Eventually, it aims to find an unsupervised video summarization method, in order
 * [Reproduce (Zhou et al. 2018)](https://gitlab.uni-hannover.de/hussainkanafani/unsupervised-video-summarization/-/wikis/Reproduce-(Zhou-et-al.-2018)%E2%80%8B)
 
 * [Conda cheat sheet](https://gitlab.uni-hannover.de/hussainkanafani/unsupervised-video-summarization/-/wikis/Conda-cheat-sheet)
+
+
+### SUM-Ind
+Make splits
+```bash
+python create_split.py -d datasets/eccv16_dataset_summe_google_pool5.h5 --save-dir datasets --save-name summe_splits  --num-splits 5
+```
+As a result, the dataset is randomly split for 5 times, which are saved as json file.
+
+Train and test codes are written in `main.py`. To see the detailed arguments, please do `python main.py -h`.
+
+#### How to train
+```bash
+python main.py -d datasets/eccv16_dataset_summe_google_pool5.h5 -s datasets/summe_splits.json -m summe --gpu 0 --save-dir log/summe-split0 --split-id 0 --verbose
+```
+
+#### How to test
+```bash
+python main.py -d datasets/eccv16_dataset_summe_google_pool5.h5 -s datasets/summe_splits.json -m summe --gpu 0 --save-dir log/summe-split0 --split-id 0 --evaluate --resume path_to_your_model.pth.tar --verbose --save-results
+```
+
+
+### Citation
+```
+@article{zhou2017reinforcevsumm, 
+   title={Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward},
+   author={Zhou, Kaiyang and Qiao, Yu and Xiang, Tao}, 
+   journal={arXiv:1801.00054}, 
+   year={2017} 
+}
+```
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