YouTube video by Anadil Albalushi

YouTube video by Anadil Albalushi

YouTube video by Anadil Albalushi

YouTube video by Anadil Albalushi

As you can see Iam Back with Another Shenmue Playlist LETS GOOO !!!!!!

YouTube video by Royal Nightingale Music Prod.

El libro "Guerrilleras con Fidel", memorias de siete combatientes del pelotón femenino Mariana Grajales, fue presentado este viernes por sus autores, Yunet ...

(☡Sinopse com Spoiler) No Museu de História de Nova York, a tumba da rainha do Egito antigo Kamillah Yunet foi indetificada como falsa, junto ao seu sarcófago...

El pasado martes el Canal Caribe estrenó el documental “Fidel, mi hermano mayor”, producido por el Centro Fidel Castro Ruz, como parte de las celebraciones por el cumpleaños 98º del comandante de la Revolución cubana Guillermo García Frías; la obra constituye un emotivo testimonio narrado por el legendario guerrillero a los periodistas Yunet López Ricardo y Wilmer Rodríguez Fernández.

Thanks for the recommendation! I followed your suggestion and benchmarked YuNet against RetinaFace and MTCNN on my dataset. The results were quite interesting. YuNet performed the best overall for my use case. At a confidence threshold of 0.8, YuNet achieved 97.67% accuracy, 95.45% precision, 100% recall, and a 97.67% F1 score, with only 1 false positive out of 43 images. I also measured inference time on CPU. YuNet averaged around 11.6 ms per image, compared with about 175 ms for MTCNN and approximately 1051 ms for RetinaFace. So YuNet was roughly 15× faster than MTCNN and 90× faster than RetinaFace in my testing. I also tested different confidence thresholds. Interestingly, 0.8 gave the best balance for my current dataset, while 0.9 eliminated the false positive but started missing actual faces. This also reinforced your point that the confidence score shouldn’t automatically be interpreted as a calibrated probability. The dataset is still small (43 images), so I don’t consider these production-level accuracy numbers yet. My next step is to test YuNet on a much larger and more diverse dataset and determine the final threshold based on the acceptable false-positive/false-negative tradeoff. Thanks again — your suggestion to start with YuNet was very helpful!

For a simple “does this image contain a face?” check, I’d probably start with YuNet. It’s very lightweight, MIT-licensed, CPU-friendly, and OpenCV provides a direct CPU implementation. For your use case, I’d benchmark YuNet against RetinaFace on your own images. The confidence score can be used as a threshold, but I wouldn’t assume that a score like 0.9 means “90% probability” without calibration. If you mainly need a fast yes/no gate in a Django microservice, YuNet seems like a very good starting point. BlazeFace is another lightweight option, especially for camera/mobile scenarios.