README.md

---
language:
- zh
- en
license: cc-by-sa-4.0
task_categories:
- video-text-to-text
tags:
- multimodal
- video-understanding
- short-video
- benchmark
- e-commerce
- vqa
library_name:
- transformers
---

<font size=3><div align='center' >  [[🍎 Home Page](https://kwai-keye.github.io/)] [[📖 Technical Report](https://huggingface.co/papers/2507.01949)] [[\ud83d\udcca Models](https://huggingface.co/Kwai-Keye)] [[\ud83d\ude80 Demo](https://huggingface.co/spaces/Kwai-Keye/Keye-VL-8B-Preview)] </div></font>

This repository contains **KC-MMBench**, a new benchmark dataset meticulously tailored for real-world short-video scenarios, as presented in the paper "[Kwai Keye-VL Technical Report](https://huggingface.co/papers/2507.01949)". Constructed from [Kuaishou](https://www.kuaishou.com/) short video data, KC-MMBench comprises 6 distinct datasets designed to evaluate the performance of Vision-Language Models (VLMs) like [**Kwai Keye-VL-8B**](https://huggingface.co/Kwai-Keye/Keye-VL-8B-Preview), Qwen2.5-VL, and InternVL in comprehending dynamic, information-dense short-form videos.

For the associated code, detailed documentation, and evaluation scripts, please refer to the official [Kwai Keye-VL GitHub repository](https://github.com/Kwai-Keye/Kwai-Keye-VL).

If you want to use KC-MMbench, please download with:
```bash
git clone https://huggingface.co/datasets/Kwai-Keye/KC-MMbench
```

## Tasks
| Task           | Description                                                                 |
| -------------- | --------------------------------------------------------------------------- |
| CPV            | The task of predicting product attributes in e-commerce.                    |
| Hot_Videos_Aggregation    | The task of determining whether multiple videos belong to the same topic.   |
| Collection_Order     | The task of determining the logical order between multiple videos with the same topic. |
| Pornographic_Comment    | The task of whether short video comments contain pornographic content.      |
| High_Like      | A binary classification task to determine the rate of likes of a short video. |
| SPU            | The task of determining whether two items are the same product in e-commerce. |

## Performance 
| Task           | Qwen2.5-VL-3B | Qwen2.5-VL-7B | InternVL-3-8B | MiMo-VL-7B | Kwai Keye-VL-8B |
| -------------- | ------------- | ------------- | ------------- | ------- | ---- |
| CPV            | 12.39         | 20.08         | 14.95         | 16.66   | 55.13 |
| Hot_Videos_Aggregation    | 42.38         | 46.35         | 52.31         | 49.00   | 54.30 |
| Collection_Order    | 36.88         | 59.83         | 64.75         | 78.68   | 84.43 |
| Pornographic_Comment    | 56.61         | 56.08         | 57.14         | 68.25   | 71.96 |
| High_Like      | 48.85         | 47.94         | 47.03         | 51.14   | 55.25 |
| SPU            | 74.09         | 81.34         | 75.64         | 81.86   | 87.05 |

## Usage

This section provides a quick guide on how to interact with models using the `keye-vl-utils` library, which is essential for processing and integrating visual language information with Keye Series Models like Kwai Keye-VL-8B.

### Install `keye-vl-utils`

First, install the necessary utility library:
```bash
pip install keye-vl-utils
```

### Keye-VL Inference Example

Here's an example of performing inference with a Kwai Keye-VL model, demonstrating how to prepare inputs for both image and video scenarios.

```python
from transformers import AutoModel, AutoProcessor
from keye_vl_utils import process_vision_info

# default: Load the model on the available device(s)
model_path = "Kwai-Keye/Keye-VL-8B-Preview"

model = AutoModel.from_pretrained(
    model_path, torch_dtype="auto", device_map="auto", attn_implementation="flash_attention_2", trust_remote_code=True,
).to('cuda')

# Example messages demonstrating various input types (image, video)
messages = [
    # Image Input Examples
    [{"role": "user", "content": [{"type": "image", "image": "file:///path/to/your/image.jpg"}, {"type": "text", "text": "Describe this image."}]}],
    [{"role": "user", "content": [{"type": "image", "image": "http://path/to/your/image.jpg"}, {"type": "text", "text": "Describe this image."}]}],
    [{"role": "user", "content": [{"type": "image", "image": "data:image;base64,/9j/..."}, {"type": "text", "text": "Describe this image."}]}],
    
    # Video Input Examples (most relevant for KC-MMBench)
    [{"role": "user", "content": [{"type": "video", "video": "file:///path/to/video1.mp4"}, {"type": "text", "text": "Describe this video."}]}],
    [{"role": "user", "content": [{"type": "video", "video": ["file:///path/to/extracted_frame1.jpg", "file:///path/to/extracted_frame2.jpg", "file:///path/to/extracted_frame3.jpg"],}, {"type": "text", "text": "Describe this video."},],}],
    [{"role": "user", "content": [{"type": "video", "video": "file:///path/to/video1.mp4", "fps": 2.0, "resized_height": 280, "resized_width": 280}, {"type": "text", "text": "Describe this video."}]}],
]

processor = AutoProcessor.from_pretrained(model_path)
# Note: model loaded above already
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
images, videos, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
inputs = processor(text=text, images=images, videos=videos, padding=True, return_tensors="pt", **video_kwargs).to("cuda")
generated_ids = model.generate(**inputs)
print(generated_ids)
```

### Evaluation

For detailed instructions on how to evaluate models using the KC-MMBench datasets, including setup and running evaluation scripts, please refer to the `evaluation/KC-MMBench/README.md` file in the official [Kwai Keye-VL GitHub repository](https://github.com/Kwai-Keye/Kwai-Keye-VL/tree/main/evaluation/KC-MMBench).

Below is the example configuration for evaluation using VLMs on our datasets:

```python
{
    "model": "...", # Specify your model
    "data": {
        "CPV": {
            "class": "KwaiVQADataset",
            "dataset": "CPV"
        },
        "Hot_Videos_Aggregation": {
            "class": "KwaiVQADataset",
            "dataset": "Hot_Videos_Aggregation"
        },
        "Collection_Order": {
            "class": "KwaiVQADataset",
            "dataset": "Collection_Order"
        },
        "Pornographic_Comment": {
            "class": "KwaiYORNDataset",
            "dataset": "Pornographic_Comment"
        },
        "High_like":{
            "class":"KwaiYORNDataset",
            "dataset":"High_like"
        },
        "SPU": {
            "class": "KwaiYORNDataset",
            "dataset": "SPU"
        }
    }
}
```