---
task_categories:
- video-text-to-text
language:
- en
tags:
- video-understanding
- streaming-video
- real-time
- long-video
- vision-language-model
---

# StreamingVLM Datasets

This repository contains datasets used in the paper [StreamingVLM: Real-Time Understanding for Infinite Video Streams](https://huggingface.co/papers/2510.09608).

StreamingVLM introduces a model designed for real-time, stable understanding of infinite visual input. It addresses challenges like escalating latency and memory usage in processing long video streams by maintaining a compact KV cache and aligning training with streaming inference. The project includes novel datasets for both training and evaluation, particularly `Inf-Streams-Eval`, a new benchmark with videos averaging over two hours that requires dense, per-second alignment between frames and text.

- **Paper**: [https://huggingface.co/papers/2510.09608](https://huggingface.co/papers/2510.09608)
- **Code**: [https://github.com/mit-han-lab/streaming-vlm](https://github.com/mit-han-lab/streaming-vlm)
- **Project/Demo Page**: [https://streamingvlm.hanlab.ai](https://streamingvlm.hanlab.ai)

## Included Datasets

The datasets associated with the StreamingVLM project include:

*   **Inf-Stream-Train**: This dataset is used for supervised fine-tuning (SFT) of the StreamingVLM model.
*   **Live-WhisperX-526K**: An additional dataset utilized during the SFT process, described as `Livecc_sft` in the project's setup.
*   **Inf-Stream-Eval**: A new benchmark dataset introduced for evaluating real-time video understanding, featuring long videos (averaging over two hours) and requiring dense, per-second alignment between frames and text.

## Sample Usage: Dataset Preparation for SFT

To prepare the necessary datasets for Supervised Fine-Tuning (SFT) as described in the [GitHub repository](https://github.com/mit-han-lab/streaming-vlm):

First, download `mit-han-lab/Inf-Stream-Train` to `/path/to/your/Inf-Stream-Train`.
Then, download `chenjoya/Live-WhisperX-526K` to `/path/to/your/Inf-Stream-Train/Livecc_sft`.
Preprocess the LiveCC dataset with the following command:

```bash
cd $DATASET_PATH/Livecc_sft
find . -type f -exec mv -t . {} +
```

Download `mit-han-lab/Inf-Stream-Eval` to `/path/to/your/Inf-Stream-Eval`.

Finally, set environment paths:

```bash
export DATASET_PATH=/path/to/your/Inf-Stream-Train
export EVAL_DATASET_PATH=/path/to/your/Inf-Stream-Eval
```

## Citation

If you find StreamingVLM useful or relevant to your project and research, please kindly cite our paper:

```bibtex
@misc{xu2025streamingvlmrealtimeunderstandinginfinite,
      title={StreamingVLM: Real-Time Understanding for Infinite Video Streams}, 
      author={Ruyi Xu and Guangxuan Xiao and Yukang Chen and Liuning He and Kelly Peng and Yao Lu and Song Han},
      year={2025},
      eprint={2510.09608},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2510.09608}, 
}
```