align-base

Model overview

The ALIGN model was proposed in "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision" by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig. ALIGN features a dual-encoder architecture with EfficientNet as its vision encoder and BERT as its text encoder, and learns to align visual and text representations with contrastive learning. Unlike previous work, ALIGN leverages a massive noisy dataset and shows that the scale of the corpus can be used to achieve SOTA representations with a simple recipe. The code for ALIGN was not publicly released, the base model is converted from the original implementation of the Kakao Brain team. This implementation follows the same architecture and hyperparameters as provided in the original Google model but is trained on the open source COYO dataset. Google’s ALIGN model, while trained on a huge dataset of 1.8 billion image-text pairs, cannot be replicated as the datasets is not public. Kakao Brain's ALIGN is on-par or outperforms Google ALIGN's reported metrics despite being trained on the much smaller, albeit carefully curated COYO-700M dataset. COYO is available on the hub as a dataset.

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

Sources & traceability

许可证：来源未披露，须进一步核验。模型介绍与第三方评测均以原来源的具体版本和方法为准。