diff --git a/.gitattributes b/.gitattributes index 283fbfa5f5b59c93d9e4e77879c65a409bbe2afc..317ee21a9d4d4eb745891efdc9f7a956f274dcde 100644 --- a/.gitattributes +++ b/.gitattributes @@ -35,3 +35,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text *.mp4 filter=lfs diff=lfs merge=lfs -text *.png filter=lfs diff=lfs merge=lfs -text +VideoLLaMA2/assets/cat_and_chicken.mp4 filter=lfs diff=lfs merge=lfs -text +VideoLLaMA2/assets/logo.png filter=lfs diff=lfs merge=lfs -text +VideoLLaMA2/assets/pipeline.png filter=lfs diff=lfs merge=lfs -text +VideoLLaMA2/assets/sora.mp4 filter=lfs diff=lfs merge=lfs -text +VideoLLaMA2/assets/sora.png filter=lfs diff=lfs merge=lfs -text diff --git a/VideoLLaMA2/.gitignore b/VideoLLaMA2/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..5d2b4c1ab07337c2106b40a88610bf991e0707aa --- /dev/null +++ b/VideoLLaMA2/.gitignore @@ -0,0 +1,58 @@ +# Python +__pycache__ +*.pyc +*.egg-info +dist + +# Log +*.log +*.log.* +*.json +*.jsonl +log_dir*/ +temp*/ + +# Data +!**/alpaca-data-conversation.json + +# Editor +.idea +*.swp + +# Other +.DS_Store +3rd_parties + +# jupyter +.ipynb_checkpoints +*.ipynb + +# DevContainer +!.devcontainer/* + +# Demo +serve_images/ +temp/ + +# data folder +data/ +dataset/ +datasets/ + +# training folder +wandb +ckpts* +output +output/ +checkpoints +checkpoints/ +work_dirs*/ + +# evaluation folder +/eval +/eval* + +# pretrained weights +pretrained/ +publish_models/ +public_models/ \ No newline at end of file diff --git a/VideoLLaMA2/LICENSE b/VideoLLaMA2/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/VideoLLaMA2/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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+ +

+ +

+VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs

+
If our project helps you, please give us a star ⭐ on GitHub to support us. 🙏🙏
+ +
+ +[![hf_space](https://img.shields.io/badge/🤗-Demo-9C276A.svg)](https://huggingface.co/spaces/lixin4ever/VideoLLaMA2) +[![hf_checkpoint](https://img.shields.io/badge/🤗-Checkpoints-9C276A.svg)](https://huggingface.co/collections/DAMO-NLP-SG/videollama-2-6669b6b6f0493188305c87ed) +[![hf_data](https://img.shields.io/badge/🤗-MSVC-9C276A.svg)](https://huggingface.co/datasets/DAMO-NLP-SG/Multi-Source-Video-Captioning) +[![arXiv](https://img.shields.io/badge/Arxiv-2406.07476-AD1C18.svg?logo=arXiv)](https://arxiv.org/abs/2406.07476)
+[![License](https://img.shields.io/badge/License-Apache%202.0-yellow)](https://github.com/DAMO-NLP-SG/VideoLLaMA2/blob/main/LICENSE) +[![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FDAMO-NLP-SG%2FVideoLLaMA2&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=Visitor&edge_flat=false)](https://hits.seeyoufarm.com) +[![GitHub issues](https://img.shields.io/github/issues/DAMO-NLP-SG/VideoLLaMA2?color=critical&label=Issues)](https://github.com/DAMO-NLP-SG/VideoLLaMA2/issues?q=is%3Aopen+is%3Aissue) +[![GitHub closed issues](https://img.shields.io/github/issues-closed/DAMO-NLP-SG/VideoLLaMA2?color=success&label=Issues)](https://github.com/DAMO-NLP-SG/VideoLLaMA2/issues?q=is%3Aissue+is%3Aclosed)
+ +
+ +
💡 Some other multimodal-LLM projects from our team may interest you ✨.

+ + +> [**Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding**](https://github.com/DAMO-NLP-SG/Video-LLaMA)
+> Hang Zhang, Xin Li, Lidong Bing
+[![github](https://img.shields.io/badge/-Github-black?logo=github)](https://github.com/DAMO-NLP-SG/Video-LLaMA) [![github](https://img.shields.io/github/stars/DAMO-NLP-SG/Video-LLaMA.svg?style=social)](https://github.com/DAMO-NLP-SG/Video-LLaMA) [![arXiv](https://img.shields.io/badge/Arxiv-2306.02858-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2306.02858)
+ +> [**VCD: Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding**](https://arxiv.org/abs/2311.16922)
+> Sicong Leng, Hang Zhang, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, Lidong Bing
+[![github](https://img.shields.io/badge/-Github-black?logo=github)](https://github.com/DAMO-NLP-SG/VCD) [![github](https://img.shields.io/github/stars/DAMO-NLP-SG/VCD.svg?style=social)](https://github.com/DAMO-NLP-SG/VCD) [![arXiv](https://img.shields.io/badge/Arxiv-2311.16922-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2311.16922)
+ +

+ +
+ + +## 📰 News +* **[2024.07.30]** Release checkpoints of [VideoLLaMA2-8x7B-Base](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2-8x7B-Base) and [VideoLLaMA2-8x7B](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2-8x7B). +* **[2024.06.25]** 🔥🔥 As of Jun 25, our [VideoLLaMA2-7B-16F](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2-7B-16F) is the **Top-1** ~7B-sized VideoLLM on the [MLVU Leaderboard](https://github.com/JUNJIE99/MLVU?tab=readme-ov-file#trophy-mini-leaderboard). +* **[2024.06.18]** 🔥🔥 As of Jun 18, our [VideoLLaMA2-7B-16F](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2-7B-16F) is the **Top-1** ~7B-sized VideoLLM on the [VideoMME Leaderboard](https://video-mme.github.io/home_page.html#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](https://huggingface.co/spaces/lixin4ever/VideoLLaMA2) 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 >= 4.41.2 (for mistral tokenizer) +* tokenizers >= 0.19.1 (for mistral tokenizer) + +**[Online Mode]** Install required packages (better for development): +```bash +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): +```bash +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 +

+ +## :earth_americas: Model Zoo +| Model Name | Model Type | Visual Encoder | Language Decoder | # Training Frames | +|:----------------|:------------:|:----------------|:------------------|:----------------:| +| [VideoLLaMA2-7B-Base](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2-7B-Base) | Base | [clip-vit-large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) | [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | 8 | +| [VideoLLaMA2-7B](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2-7B) | Chat | [clip-vit-large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) | [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | 8 | +| [VideoLLaMA2-7B-16F-Base](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2-7B-16F-Base) | Base | [clip-vit-large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) | [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | 16 | +| [VideoLLaMA2-7B-16F](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2-7B-16F) | Chat | [clip-vit-large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) | [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | 16 | +| [VideoLLaMA2-8x7B-Base](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2-8x7B-Base) | Base | [clip-vit-large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) | [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) | 8 | +| [VideoLLaMA2-8x7B](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2-8x7B) | Chat | [clip-vit-large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) | [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) | 8 | + + +## [🤗 Demo](https://huggingface.co/spaces/lixin4ever/VideoLLaMA2) + +It is highly recommended to try our [online demo](https://huggingface.co/spaces/lixin4ever/VideoLLaMA2) 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](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2-7B) is adopted by default): +```bash +python videollama2/serve/gradio_web_server_adhoc.py +``` + +### Multi-model Version + +1. Launch a global controller +```bash +cd /path/to/VideoLLaMA2 +python -m videollama2.serve.controller --host 0.0.0.0 --port 10000 +``` + +2. Launch a gradio webserver +```bash +python -m videollama2.serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload +``` + +3. Launch one or multiple model workers +```bash +# 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](https://github.com/DAMO-NLP-SG/VideoLLaMA2) with [VideoLLaVA](https://github.com/PKU-YuanGroup/Video-LLaVA) dataset and evaluate the trained model on the mainstream video-llm benchmarks. + +1. Training Data Structure: +```bash +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: +```bash +# VideoLLaMA2-vllava pretraining +bash scripts/vllava/pretrain.sh +# VideoLLaMA2-vllava finetuning +bash scripts/vllava/finetune.sh +``` +3. Evaluation Data Structure: +```bash +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: +```bash +# 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: +```bash +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: +```json +[ + { + "id": 0, + "video": "images/xxx.jpg", + "conversations": [ + { + "from": "human", + "value": "\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": "