Datasetpublic

TIGER-Lab/VideoEval-Pro

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 paper can be found here. The evaluation code and scripts are available at: TIGER-AI-Lab/VideoEval-Pro Dataset Structure Each example in the… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/VideoEval-Pro.

sourceHugging Faceupdated 1y agoView on Hugging Face

4likes2kdownloads

Dataset Card

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 paper can be found here.

The evaluation code and scripts are available at: TIGER-AI-Lab/VideoEval-Pro

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'

1.[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.

1.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

1.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

1.Judge the results

bash

   cd VideoEval-Pro
   
   # Set PYTHONPATH
   export PYTHONPATH=.
   
   # Run judge script *gpt4o_judge.py* with the following parameters:
   # --input_path: Path to save output results
   # --output_path: Path to judged results
   # --model_name: Version of the judge model
   # --num_threads: Number of threads for parallel processing
   
   python tools/gpt4o_judge.py \
       --input_path <path_to_saved_results> \
       --output_path <path_to_judged_results> \
       --model_name <model_version> \
       --num_threads <num_threads>

   E.g.:
   python tools/gpt4o_judge.py \
       --input_path ./results/qwen_results.jsonl \
       --output_path ./results/qwen_results_judged.jsonl \
       --model_name gpt-4o-2024-08-06 \
       --num_threads 1

*Note: the released results are judged by gpt-4o-2024-08-06***