Awesome

<br> <br> <br>

<br> <br> <br> <br> <br>

Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding <br> Hang Zhang, Xin Li, Lidong Bing <br> <br>

VCD: Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding <br> Sicong Leng*, Hang Zhang*, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, Lidong Bing <br> <br>

The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audio <br> Sicong Leng, Yun Xing, Zesen Cheng, Yang Zhou, Hang Zhang, Xin Li, Deli Zhao, Shijian Lu, Chunyan Miao, Lidong Bing <br> <br>

Breaking the Memory Barrier: Near Infinite Batch Size Scaling for Contrastive Loss <br> Zesen Cheng*, Hang Zhang*, Kehan Li*, Sicong Leng, Zhiqiang Hu, Fei Wu, Deli Zhao, Xin Li, Lidong Bing <br> <br>

📰 News

• [2024.10.22] Release checkpoints of VideoLLaMA2.1-7B-AV. The audio_visual branch code can be seen here: https://github.com/DAMO-NLP-SG/VideoLLaMA2/tree/audio_visual.
• [2024.10.15] Release checkpoints of VideoLLaMA2.1-7B-16F-Base and VideoLLaMA2.1-7B-16F.
• [2024.08.14] Release checkpoints of VideoLLaMA2-72B-Base and VideoLLaMA2-72B.
• [2024.07.30] Release checkpoints of VideoLLaMA2-8x7B-Base and VideoLLaMA2-8x7B.
• [2024.06.25] 🔥🔥 As of Jun 25, our VideoLLaMA2-7B-16F is the Top-1 ~7B-sized VideoLLM on the MLVU Leaderboard.
• [2024.06.18] 🔥🔥 As of Jun 18, our VideoLLaMA2-7B-16F is the Top-1 ~7B-sized VideoLLM on the VideoMME Leaderboard.
• [2024.06.17] 👋👋 Update technical report with the latest results and the missing references. If you have works closely related to VideoLLaMA 2 but not mentioned in the paper, feel free to let us know.
• [2024.06.14] 🔥🔥 Online Demo is available.
• [2024.06.03] Release training, evaluation, and serving codes of VideoLLaMA 2.

🛠️ Requirements and Installation

Basic Dependencies:

• Python >= 3.8
• Pytorch >= 2.2.0
• CUDA Version >= 11.8
• transformers == 4.40.0 (for reproducing paper results)
• tokenizers == 0.19.1

[Online Mode] Install required packages (better for development):

git clone https://github.com/DAMO-NLP-SG/VideoLLaMA2
cd VideoLLaMA2
pip install -r requirements.txt
pip install flash-attn==2.5.8 --no-build-isolation

[Offline Mode] Install VideoLLaMA2 as a Python package (better for direct use):

git clone https://github.com/DAMO-NLP-SG/VideoLLaMA2
cd VideoLLaMA2
pip install --upgrade pip  # enable PEP 660 support
pip install -e .
pip install flash-attn==2.5.8 --no-build-isolation

🚀 Main Results

Multi-Choice Video QA & Video Captioning

Open-Ended Video QA

Audio QA

Audio-Visual QA

:earth_americas: Model Zoo

Vision-only Checkpoints

Audio-Visual Checkpoints

🤗 Demo

It is highly recommended to try our online demo first.

To run a video-based LLM (Large Language Model) web demonstration on your device, you will first need to ensure that you have the necessary model checkpoints prepared, followed by adhering to the steps outlined to successfully launch the demo.

Single-model Version

• Launch a gradio app directly (VideoLLaMA2-7B is adopted by default):

python videollama2/serve/gradio_web_server_adhoc.py

Multiple-model Version

1. Launch a global controller

cd /path/to/VideoLLaMA2
python -m videollama2.serve.controller --host 0.0.0.0 --port 10000

2. Launch a gradio webserver

python -m videollama2.serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload

3. Launch one or multiple model workers

#  export HF_ENDPOINT=https://hf-mirror.com  # If you are unable to access Hugging Face, try to uncomment this line.
python -m videollama2.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path /PATH/TO/MODEL1
python -m videollama2.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40001 --worker http://localhost:40001 --model-path /PATH/TO/MODEL2
python -m videollama2.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40002 --worker http://localhost:40002 --model-path /PATH/TO/MODEL3
...

🗝️ Training & Evaluation

Quick Start

To facilitate further development on top of our codebase, we provide a quick-start guide on how to train a customized VideoLLaMA2 with VideoLLaVA dataset and evaluate the trained model on the mainstream video-llm benchmarks.

1. Training Data Structure:

VideoLLaMA2
├── datasets
│   ├── videollava_pt
|   |   ├── llava_image/ # Available at: https://pan.baidu.com/s/17GYcE69FcJjjUM0e4Gad2w?pwd=9ga3 or https://drive.google.com/drive/folders/1QmFj2FcMAoWNCUyiUtdcW0-IOhLbOBcf?usp=drive_link
|   |   ├── valley/      # Available at: https://pan.baidu.com/s/1jluOimE7mmihEBfnpwwCew?pwd=jyjz or https://drive.google.com/drive/folders/1QmFj2FcMAoWNCUyiUtdcW0-IOhLbOBcf?usp=drive_link
|   |   └── valley_llavaimage.json # Available at: https://drive.google.com/file/d/1zGRyVSUMoczGq6cjQFmT0prH67bu2wXD/view, including 703K video-text and 558K image-text pairs
│   ├── videollava_sft
|   |   ├── llava_image_tune/  # Available at: https://pan.baidu.com/s/1l-jT6t_DlN5DTklwArsqGw?pwd=o6ko
|   |   ├── videochatgpt_tune/ # Available at: https://pan.baidu.com/s/10hJ_U7wVmYTUo75YHc_n8g?pwd=g1hf
|   |   └── videochatgpt_llavaimage_tune.json # Available at: https://drive.google.com/file/d/1zGRyVSUMoczGq6cjQFmT0prH67bu2wXD/view, including 100K video-centric, 625K image-centric and 40K text-only conversations

2. Command:

# VideoLLaMA2-vllava pretraining
bash scripts/vllava/pretrain.sh
# VideoLLaMA2-vllava finetuning
bash scripts/vllava/finetune.sh

3. Evaluation Data Structure:

VideoLLaMA2
├── eval
│   ├── egoschema # Official website: https://github.com/egoschema/EgoSchema
|   |   ├── good_clips_git/ # Available at: https://drive.google.com/drive/folders/1SS0VVz8rML1e5gWq7D7VtP1oxE2UtmhQ
|   |   └── questions.json  # Available at: https://github.com/egoschema/EgoSchema/blob/main/questions.json
│   ├── mvbench # Official website: https://huggingface.co/datasets/OpenGVLab/MVBench
|   |   ├── video/
|   |   |   ├── clever/
|   |   |   └── ...
|   |   └── json/
|   |   |   ├── action_antonym.json
|   |   |   └── ...
│   ├── perception_test_mcqa # Official website: https://huggingface.co/datasets/OpenGVLab/MVBench
|   |   ├── videos/ # Available at: https://storage.googleapis.com/dm-perception-test/zip_data/test_videos.zip
|   |   └── mc_question_test.json # Download from https://storage.googleapis.com/dm-perception-test/zip_data/mc_question_test_annotations.zip
│   ├── videomme # Official website: https://video-mme.github.io/home_page.html#leaderboard
|   |   ├── test-00000-of-00001.parquet
|   |   ├── videos/
|   |   └── subtitles/
│   ├── Activitynet_Zero_Shot_QA # Official website: https://github.com/MILVLG/activitynet-qa
|   |   ├── all_test/   # Available at: https://mbzuaiac-my.sharepoint.com/:u:/g/personal/hanoona_bangalath_mbzuai_ac_ae/EatOpE7j68tLm2XAd0u6b8ABGGdVAwLMN6rqlDGM_DwhVA?e=90WIuW
|   |   ├── test_q.json # Available at: https://github.com/MILVLG/activitynet-qa/tree/master/dataset
|   |   └── test_a.json # Available at: https://github.com/MILVLG/activitynet-qa/tree/master/dataset
│   ├── MSVD_Zero_Shot_QA # Official website: https://github.com/xudejing/video-question-answering
|   |   ├── videos/     
|   |   ├── test_q.json 
|   |   └── test_a.json
│   ├── videochatgpt_gen # Official website: https://github.com/mbzuai-oryx/Video-ChatGPT/tree/main/quantitative_evaluation
|   |   ├── Test_Videos/ # Available at: https://mbzuaiac-my.sharepoint.com/:u:/g/personal/hanoona_bangalath_mbzuai_ac_ae/EatOpE7j68tLm2XAd0u6b8ABGGdVAwLMN6rqlDGM_DwhVA?e=90WIuW
|   |   ├── Test_Human_Annotated_Captions/ # Available at: https://mbzuaiac-my.sharepoint.com/personal/hanoona_bangalath_mbzuai_ac_ae/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fhanoona%5Fbangalath%5Fmbzuai%5Fac%5Fae%2FDocuments%2FVideo%2DChatGPT%2FData%5FCode%5FModel%5FRelease%2FQuantitative%5FEvaluation%2Fbenchamarking%2FTest%5FHuman%5FAnnotated%5FCaptions%2Ezip&parent=%2Fpersonal%2Fhanoona%5Fbangalath%5Fmbzuai%5Fac%5Fae%2FDocuments%2FVideo%2DChatGPT%2FData%5FCode%5FModel%5FRelease%2FQuantitative%5FEvaluation%2Fbenchamarking&ga=1
|   |   ├── generic_qa.json     # These three json files available at: https://mbzuaiac-my.sharepoint.com/personal/hanoona_bangalath_mbzuai_ac_ae/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fhanoona%5Fbangalath%5Fmbzuai%5Fac%5Fae%2FDocuments%2FVideo%2DChatGPT%2FData%5FCode%5FModel%5FRelease%2FQuantitative%5FEvaluation%2Fbenchamarking%2FBenchmarking%5FQA&ga=1
|   |   ├── temporal_qa.json
|   |   └── consistency_qa.json

4. Command:

# mvbench evaluation
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/eval/eval_video_qa_mvbench.sh
# activitynet-qa evaluation (need to set azure openai key/endpoint/deployname)
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/eval/eval_video_qa_mvbench.sh

Data Format

If you want to train a video-llm on your data, you need to follow the procedures below to prepare the video/image sft data:

1. Suppose your data structure is like:

VideoLLaMA2
├── datasets
│   ├── custom_sft
│   |   ├── images
│   |   ├── videos
|   |   └── custom.json

2. Then you should re-organize the annotated video/image sft data according to the following format:

[
    {
        "id": 0,
        "video": "images/xxx.jpg",
        "conversations": [
            {
                "from": "human",
                "value": "<image>\nWhat are the colors of the bus in the image?"
            },
            {
                "from": "gpt",
                "value": "The bus in the image is white and red."
            },
            ...
        ],
    }
    {
        "id": 1,
        "video": "videos/xxx.mp4",
        "conversations": [
            {
                "from": "human",
                "value": "<video>\nWhat are the main activities that take place in the video?"
            },
            {
                "from": "gpt",
                "value": "The main activities that take place in the video are the preparation of camera equipment by a man, a group of men riding a helicopter, and a man sailing a boat through the water."
            },
            ...
        ],
    },
    ...
]

3. Modify the scripts/custom/finetune.sh:

...
--data_path datasets/custom_sft/custom.json
--data_folder datasets/custom_sft/
--pretrain_mm_mlp_adapter CONNECTOR_DOWNLOAD_PATH (e.g., DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base)
...

🤖 Inference

Video/Image Inference:

import sys
sys.path.append('./')
from videollama2 import model_init, mm_infer
from videollama2.utils import disable_torch_init


def inference():
    disable_torch_init()

    # Video Inference
    modal = 'video'
    modal_path = 'assets/cat_and_chicken.mp4' 
    instruct = 'What animals are in the video, what are they doing, and how does the video feel?'
    # Reply:
    # The video features a kitten and a baby chick playing together. The kitten is seen laying on the floor while the baby chick hops around. The two animals interact playfully with each other, and the video has a cute and heartwarming feel to it.

    # Image Inference
    modal = 'image'
    modal_path = 'assets/sora.png'
    instruct = 'What is the woman wearing, what is she doing, and how does the image feel?'
    # Reply:
    # The woman in the image is wearing a black coat and sunglasses, and she is walking down a rain-soaked city street. The image feels vibrant and lively, with the bright city lights reflecting off the wet pavement, creating a visually appealing atmosphere. The woman's presence adds a sense of style and confidence to the scene, as she navigates the bustling urban environment.

    model_path = 'DAMO-NLP-SG/VideoLLaMA2.1-7B-16F'
    # Base model inference (only need to replace model_path)
    # model_path = 'DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base'
    model, processor, tokenizer = model_init(model_path)
    output = mm_infer(processor[modal](modal_path), instruct, model=model, tokenizer=tokenizer, do_sample=False, modal=modal)

    print(output)

if __name__ == "__main__":
    inference()

📑 Citation

If you find VideoLLaMA useful for your research and applications, please cite using this BibTeX:

@article{damonlpsg2024videollama2,
  title={VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs},
  author={Cheng, Zesen and Leng, Sicong and Zhang, Hang and Xin, Yifei and Li, Xin and Chen, Guanzheng and Zhu, Yongxin and Zhang, Wenqi and Luo, Ziyang and Zhao, Deli and Bing, Lidong},
  journal={arXiv preprint arXiv:2406.07476},
  year={2024},
  url = {https://arxiv.org/abs/2406.07476}
}

@article{damonlpsg2023videollama,
  title = {Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding},
  author = {Zhang, Hang and Li, Xin and Bing, Lidong},
  journal = {arXiv preprint arXiv:2306.02858},
  year = {2023},
  url = {https://arxiv.org/abs/2306.02858}
}

👍 Acknowledgement

The codebase of VideoLLaMA 2 is adapted from LLaVA 1.5 and FastChat. We are also grateful for the following projects our VideoLLaMA 2 arise from:

• LLaMA 2, Mistral-7B, OpenAI CLIP, Qwen2, SigLIP, Honeybee.
• Video-ChatGPT, Video-LLaVA.
• WebVid, Panda-70M, LanguageBind, InternVid.
• VideoChat2, Valley, VTimeLLM, ShareGPT4V.
• Magpie, ALLaVA, AVInstruct.

🔒 License

This project is released under the Apache 2.0 license as found in the LICENSE file. The service is a research preview intended for non-commercial use ONLY, subject to the model Licenses of LLaMA and Mistral, Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT. Please get in touch with us if you find any potential violations.