--- 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 ---
[[🍎 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)]
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" } } } ```