Update README.md

README.md

@@ -31,3 +31,118 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+# VideoEval-Pro
+
+VideoEval-Pro is a robust and realistic long video understanding benchmark containing open-ended, short-answer QA problems. The dataset is constructed by reformatting questions from four existing long video understanding MCQ benchmarks: Video-MME, MLVU, LVBench, and LongVideoBench into free-form questions.
+
+The evaluation code and scripts are available at: [TIGER-AI-Lab/VideoEval-Pro](https://github.com/TIGER-AI-Lab/VideoEval-Pro)
+
+## Task Types
+VideoEval-Pro contains various types of video understanding tasks. The distribution of task types is shown below:
+
+![Task Type Distribution](assets/task_types.png)
+
+
+## Dataset Structure
+Each example in the dataset contains:
+- `video`: Name (path) of the video file
+- `question`: The question about the video content
+- `options`: Original options from the source benchmark
+- `answer`: The correct MCQ answer
+- `answer_text`: The correct free-form answer
+- `meta`: Additional metadata from the source benchmark
+- `source`: Source benchmark
+- `qa_subtype`: Question task subtype
+- `qa_type`: Question task type
+
+## Evaluation Steps
+
+1. **Download and Prepare Videos**
+   ```bash
+   # Navigate to videos directory
+   cd videos
+   
+   # Merge all split tar.gz files into a single archive
+   cat videos_part_*.tar.gz > videos_merged.tar.gz
+   
+   # Extract the merged archive
+   tar -xzf videos_merged.tar.gz
+   
+   # [Optional] Clean up the split files and merged archive
+   rm videos_part_*.tar.gz videos_merged.tar.gz
+   
+   # After extraction, you will get a directory containing all videos
+   # The path to this directory will be used as --video_root in evaluation
+   # For example: 'VideoEval-Pro/videos'
+   ```
+
+2. **[Optional] Pre-extract Frames**
+   To improve efficiency, you can pre-extract frames from videos. The extracted frames should be organized as follows:
+   ```
+   frames_root/
+   ├── video_name_1/              # Directory name is thevideo name
+   │   ├── 000001.jpg             # Frame images
+   │   ├── 000002.jpg
+   │   └── ...
+   ├── video_name_2/
+   │   ├── 000001.jpg
+   │   ├── 000002.jpg
+   │   └── ...
+   └── ...
+   ```
+
+   After frame extraction, the path to the frames will be used as `--frames_root`. Set `--using_frames True` when running the evaluation script.
+
+3. **Setup Evaluation Environment**
+   ```bash
+   # Clone the repository from the GitHub repository
+   git clone https://github.com/TIGER-AI-Lab/VideoEval-Pro
+   cd VideoEval-Pro
+   
+   # Create conda environment from requirements.txt (there are different requirements files for different models)
+   conda create -n videoevalpro --file requirements.txt
+   conda activate videoevalpro
+   ```
+
+4. **Run Evaluation**
+   ```bash
+   cd VideoEval-Pro
+   
+   # Set PYTHONPATH
+   export PYTHONPATH=.
+   
+   # Run evaluation script with the following parameters:
+   # --video_root: Path to video files folder
+   # --frames_root: Path to video frames folder [For using_frames]
+   # --output_path: Path to save output results
+   # --using_frames: Whether to use pre-extracted frames
+   # --model_path: Path to model
+   # --device: Device to run inference on
+   # --num_frames: Number of frames to sample from video
+   # --max_retries: Maximum number of retries for failed inference
+   # --num_threads: Number of threads for parallel processing
+   
+   python tools/*_chat.py \
+       --video_root <path_to_videos> \
+       --frames_root <path_to_frames> \
+       --output_path <path_to_save_results> \
+       --using_frames <True/False> \
+       --model_path <model_name_or_path> \
+       --device <device> \
+       --num_frames <number_of_frames> \
+       --max_retries <max_retries> \
+       --num_threads <num_threads>
+
+   E.g.:
+   python tools/qwen_chat.py \
+       --video_root ./videos \
+       --frames_root ./frames \
+       --output_path ./results/qwen_results.jsonl \
+       --using_frames False \
+       --model_path Qwen/Qwen2-VL-7B-Instruct \
+       --device cuda \
+       --num_frames 32 \
+       --max_retries 10 \
+       --num_threads 1
+   ```