Tarsier
T
Tarsier
Overview :
Tarsier is a series of large-scale video language models developed by the ByteDance research team, designed to generate high-quality video descriptions and equipped with robust video comprehension capabilities. The model significantly enhances the accuracy and detail of video descriptions through a two-stage training strategy (multi-task pre-training and multi-granularity instruction fine-tuning). Its main advantages include high precision in video description, understanding of complex video content, and achieving state-of-the-art (SOTA) results in multiple video comprehension benchmark tests. The model's development addresses the shortcomings in detail and accuracy of existing video language models, achieving new heights in video description through extensive training on high-quality data and innovative training methods. Currently, the model is not explicitly priced and is mainly targeted at academic research and commercial applications, suitable for scenarios requiring high-quality understanding and generation of video content.
Target Users :
Tarsier is perfect for users requiring high-quality video content generation and comprehension, including video content creators, researchers, video platform developers, and commercial users needing automated video descriptions. It helps users quickly generate detailed video descriptions, enhancing content accessibility and user experience.
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Use Cases
Video content creators can use Tarsier to automatically generate detailed descriptions of their videos, saving time and effort.
Researchers can leverage Tarsier's model architecture and training methods for studies and improvements in video language models.
Video platforms can integrate Tarsier to provide automated video description features, enhancing user experience and content accessibility.
Features
Generates high-quality video descriptions that can detail events, actions, and scenes within videos.
Supports multi-task pre-training, covering various tasks such as video description and video question answering.
Employs multi-granularity instruction fine-tuning, enhancing the model's comprehension of videos with varying complexities.
Achieved SOTA results in multiple video comprehension benchmark tests, including MVBench and NeXT-QA.
Provides the DREAM-1K video description benchmark dataset for evaluating model performance.
Supports various input formats, including videos, images, and GIF files.
Offers online demos and open-source code for developers to facilitate research and deployment.
How to Use
1. Create a Python 3.9 virtual environment (if not already installed): `conda create -n tarsier python=3.9`
2. Clone the Tarsier code repository: `git clone https://github.com/bytedance/tarsier.git`
3. Navigate to the project directory and run the installation script: `cd tarsier && bash setup.sh`
4. Download the model weight files, available from Hugging Face: `Tarsier-7b` or `Tarsier-34b`
5. Prepare your input video file, e.g., `assets/videos/coffee.gif`
6. Run the quick start script to generate video descriptions: `python3 -m tasks.inference_quick_start --model_name_or_path <model_path> --instruction 'Describe the video in detail.' --input_path <video_path>`
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