“These results are testament to the caliber of PhDs we hire and our focus on real-time, real world uses of our research. “We have spent 3 years quietly working on the commercial viability of deep learning and computer vision,” said Stephen Neish, Sighthound’s CEO. The result is that Sighthound’s software is highly accurate, robust to a variety of real world use cases and runs in real time. Further, Sighthound’s algorithms are robust to both verification tasks such as LFW and identification tasks such as tests against the PubFig200 database. Some others on the LFW results list use up to 25 crops of the same image, each crop showing a small part of the face, which slows down or prohibits real world applications. Sighthound’s facial recognition system stands out by virtue of using less than 2% of the amount of training data used by Google, and by using only one crop per image. Sighthound Cloud also offers face detection, person detection, gender recognition and facial landmark identification.įounded in 2012, Sighthound develops innovative proprietary computer vision systems and is a leader in deep learning for real world implementations. Sighthound also announced that it is making the system available to developers for free on the Sighthound Cloud service. Previous highest accuracy scores include Google at 99.63% and Baidu at 99.77%. today announced that Sighthound’s new facial recognition system ranks first against the Labeled Faces in the Wild (LFW) benchmark database hosted by the University of Massachusetts, with an accuracy of 99.79%. Palo Alto, California – DecemSighthound, Inc. Sighthound achieves 99.79% accuracy against LFW benchmark, beating Google, Baidu and Facebook, makes system available for free to developers on the Sighthound Cloud service.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. Archives
November 2023
Categories |