人正面研究：洞察人性之光
- WebFace 42M, derived from WebFace 260M through a rigorous cleaning process, has demonstrated superior performance on the IJBC test set, reaching SOTA levels with a relative error rate reduction of 40%.
- On the renowned NIST-FRVT competition, WebFace 42M has consistently placed in the top three, showcasing its exceptional capabilities in a highly challenging and authoritative evaluation platform.
Moving Towards Real-World Applications
Revolutionizing Data Quality and Scale
- WebFace 260M boasts an unprecedented number of identities and images, setting new standards for dataset size and quality.
- The dataset has been meticulously cleaned, ensuring that the data is free from noise and more reliable for training.
Enhancing Performance with Embedded Features
| WebFace 260M | WebFace 42M |
|---|---|
| Largest dataset available | Largest clean dataset for training |
| 400M+ identities | 42M+ identities |
| 2.6B+ images | 27.4M+ images |
The Role of WebFace 42M in Achieving SOTA on IJBC and NIST-FRVT
- WebFace 42M, derived from WebFace 260M through a rigorous cleaning process, has demonstrated superior performance on the IJBC test set, reaching SOTA levels with a relative error rate reduction of 40%.
- On the renowned NIST-FRVT competition, WebFace 42M has consistently placed in the top three, showcasing its exceptional capabilities in a highly challenging and authoritative evaluation platform.
Moving Towards Real-World Applications
The introduction of FRUITS (Face Recognition Under
以WebFace42M為基礎，在“戴口罩人臉識別評測”中奪得冠軍，與第二名成績有顯著差距。
在人臉識別技術中，數據集對精度和速度有顯著影響。 為了滿足實際需求，科研人員和工業界需要更大規模的高質量數據集。
延伸閲讀…
人臉識別技術應用廣泛，從打卡、開門禁到解鎖手機，需求日益增長。
隨著深度學習的發展，軟件開發正從傳統模式轉向數據為核心的軟件2.0時代。
為了滿足實際需求，科研人員和工業界需要更大規模的高質量數據集。
延伸閲讀…
人臉識別技術的進步，對數據規模和質量的需求日益提升。
在1:1人臉識別評測中，基於WebFace42M的研究取得世界前三的成績。
數據集的不足，限制了科研和工業界在人臉識別技術上的進一步突破。
未來需要更多元化、更大規模的數據集，以推動人臉識別技術的不斷進步。
