Kakao 开源 Kanana-2-vision-encoder 视觉编码器，含 4.26 亿与 9800 万参数两版
Kakao Open-Sources AI Image Recognition Models
Kakao 将自研图像识别模型 Kanana-2-vision-encoder 的两个版本开源，中等版 4.26 亿参数、微型版 9800 万参数，均已上线 Hugging Face。Kakao 称两款模型在覆盖通用视觉问答、英韩 OCR、文档理解和目标定位等九大类共 42 项基准上，对比 Google SigLIP 2、Meta Perception Encoder、Apple AIMv2 和阿里 Qwen3.5-ViT 均取得最高性能，微型版在部分基准上超过参数量 4 亿以上的竞品，韩文识别、文档理解和目标定位差距尤为明显。训练中使用自研 Kanana-2 语言模型使训练效率提升约 4 倍，高分辨率图像处理架构改进使延迟降低 60% 以上；模型以 Kanana Open License 发布，允许研究、开发和商用。
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Kakao 开源两款视觉编码器，并给出与 SigLIP 2、Perception Encoder 等模型的 42 项基准对比，可据此判断小参数模型的竞争力。
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A performance comparison between Kakao's newly released AI models and overseas AI models. (Image: Kakao)
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Kakao has released two versions of its internally developed artificial intelligence image recognition model, Kanana-2-vision-encoder, as open source.
The models include a medium version with 426 million parameters and a tiny version with 98 million parameters. Both are available on Hugging Face, a global AI model-sharing platform.
Vision encoders analyze visual information in images, including objects, text, positions and shapes, and pass the information to language models.
The technology is used in multimodal AI applications that recognize document content and structure or answer user questions based on photographs.
Kakao evaluated the two models across 42 benchmarks in nine categories, including general visual question answering (VQA), English and Korean optical character recognition (OCR), document understanding and object localization within images.
According to Kakao, both models achieved the highest performance across the nine evaluation categories compared with major competing models, including Google's SigLIP 2, Meta's Perception Encoder, Apple's AIMv2 and Alibaba's Qwen3.5-ViT.
The tiny model, with fewer than 100 million parameters, outperformed some competing models with more than 400 million parameters on certain benchmarks.
Kakao said the performance gap was particularly significant in Korean text recognition, document understanding and object localization.
The company said it used its internally developed Kanana-2 language model during training to improve the vision encoders' training efficiency by approximately four times compared with the previous approach.
Kakao also said improvements to the high-resolution image processing architecture reduced processing latency by more than 60% compared with major competing models.
Both models are released under the Kanana Open License, which permits research, development and commercial use.
Kakao plans to support companies and researchers in South Korea and overseas in developing Korean-language document recognition and image-based AI services using the models.
The company also plans to gradually release the language, vision and audio processing components of its internally developed multimodal AI model, Kanana-o.