Update README

README.md

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-license: cc-by-nc-sa-4.0
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+---
+license: cc-by-nc-sa-4.0
+---
+
+# E.T. Bench
+
+<p align="center">
+  <img width="700" src="https://github.com/PolyU-ChenLab/ETBench/blob/main/.github/benchmark.jpg">
+</p>
+
+E.T. Bench is a large-scale and high-quality benchmark for open-ended event-level video understanding. Categorized within a 3-level task taxonomy, it encompasses 7.3K samples under 12 tasks with 7K videos (251.4h total length) under 8 domains, providing comprehensive evaluations on 4 essential capabilities for time-sensitive video understanding.
+
+## 📚 Data Preparation
+
+You may download the evaluation kit for E.T. Bench using the following command.
+
+```
+git lfs install
+git clone git@hf.co:datasets/PolyU-ChenLab/ETBench
+```
+
+Then, enter the directory and extract the files in the `videos` folder by running:
+
+```
+cd ETBench
+for path in videos/*.tar.gz; do tar -xvf $path -C videos; done
+```
+
+**[Optional]** You may also want to compress the videos (to lower FPS & resolution) for faster I/O.
+
+```
+python compress_videos.py --fps 3 --size 224
+```
+
+<details>
+<summary><i>Arguments of <code>compress_videos.py</code></i></summary>
+
+- `--src_dir` Path to the videos folder (Default: `videos`)
+- `--tgt_dir` Path to the output folder (Default: `videos_compressed`)
+- `--fps` The target FPS for output (Default: `3`)
+- `--size` The length of the shortest side of output frames (Default: `224`)
+- `--workers` Number of workers to use (Default: `None` same as the number of CPUs)
+
+</details>
+
+This will compress all the videos to `3 FPS` and `224 pixels shortest side`. The audio will be removed as well. The output videos will be saved in `videos_compressed` folder with the same structure as `videos`.
+
+## 🚀 Getting Started
+
+The folder for E.T. Bench is organized as follows.
+
+```
+ETBench
+├─ annotations
+│  ├─ txt (annotations for sub-tasks, with timestamps as text)
+│  ├─ vid (annotations for sub-tasks, with timestamps as <vid> tokens)
+│  ├─ etbench_txt_v1.0.json (merged annotations in `txt` folder)
+│  └─ etbench_vid_v1.0.json (merged annotations in `vid` folder)
+├─ evaluation
+│  ├─ compute_metrics.py (script for computing metrics)
+│  ├─ requirements.txt (requirements for the evaluation script)
+│  └─ subset.json (IDs of the subset for evaluating commercial models)
+├─ videos (raw video files)
+├─ videos_compressed (compressed video files)
+└─ compress_videos.py (script for compressing videos)
+```
+
+For full evaluation on 7,289 samples, you just need to use either of the following annotation file.
+
+- `etbench_txt_v1.0.json` - for models representing timestamps in pure text, e.g., '2.5 - 4.8 seconds'
+- `etbench_vid_v1.0.json` - for models using special tokens for timestamps, e.g., \<vid\> token in E.T. Chat
+
+Each JSON file contains a list of dicts with the following entries.
+
+```python
+{
+  "version": 1.0,                       # annotation version
+  "idx": 0,                             # sample index
+  "task": "tvg",                        # task
+  "source": "qvhighlights",             # source dataset
+  "video": "qvhighlights/example.mp4",  # path to video
+  "duration": 35.0,                     # video duration (seconds)
+  "src": [1.2, 15.0],                   # [optional] timestamps (seconds) in model inputs
+  "tgt": [[15.0, 31.0], [31.4, 34.9]],  # [optional] timestamps (seconds) in model outputs
+  "p": 0,                               # [optional] index of correct answer (for RAR, ECA, RVQ, GVQ)
+  "o": ["a", "b", "c", "d"],            # [optional] answer candidates (for RAR, ECA, RVQ, GVQ)
+  "g": ["a cat...", "it then..."],      # [optional] ground truth captions (for DVC, SLC)
+  "q": "...",                           # model input prompt
+  "a": "..."                            # [to be added by the user] model response
+}
+```
+
+For each sample, you can simply load the corresponding video and send it together with the prompt in `q` to the model. In `vid` style annotations, all the timestamps in `q` have been replaced with `<vid>` and their original values can be found in `src`.
+
+After obtaining model outputs, you need to place raw text responses into the `a` entries of each sample and dump the entire list to a new JSON file. ***Please make sure the dumped file has exactly the same structure as the annotation file, except that each sample has a new `a` entry storing model outputs.***
+
+Please refer to the [inference script](../etchat/eval/infer_etbench.py) of E.T. Chat as an example.
+
+## 🔮 Compute Metrics
+
+Run the following command to install the requirements for the evaluation script.
+
+```
+pip install -r evaluation/requirements.txt
+```
+
+After that, compute the metrics by running
+
+```
+python evaluation/compute_metrics.py <path-to-the-dumped-json>
+
+# In case you want to evaluate on the subset with 470 samples (same as the commercial models in Table 1 of the paper)
+# python evaluation/compute_metrics.py <path-to-the-dumped-json> --subset
+```
+
+The evaluation log and computed metrics will be saved in `metrics.log` and `metrics.json`, respectively.
+
+## 📖 Citation
+
+Please kindly cite our paper if you find this project helpful.
+
+```
+@article{liu2024etbench,
+  title={E.T. Bench: Towards Open-Ended Event-Level Video-Language Understanding},
+  author={Liu, Ye and Ma, Zongyang and Qi, Zhongang and Wu, Yang and Chen, Chang Wen and Shan, Ying},
+  journal={Tech Report},
+  year={2024}
+}
+```