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Their repository is pretty neat! It includes three state-of-the-art architectures in object detection: Faster-RCNN, RFCN, and SSD. It is missing YOLO [1][2], th

Their repository is pretty neat! It includes three state-of-the-art architectures in object detection: Faster-RCNN, RFCN, and SSD. It is missing YOLO [1][2], though, which shares some similarities with SSD. Another detector is the recently released Mask-RCNN [3], which of course wouldn't be possible to be included in this publication as we can't travel through time yet. [1]: https://arxiv.org/abs/1506.02640 https://arxiv.org/abs/1506.02640 [2]: https://arxiv.org/abs/1612.08242 https://arxiv.org/abs/1612.08242 [3]: https://arxiv.org/abs/1703.06870 https://arxiv.org/abs/1703.06870

mrplank 9y ago
There are already newer versions, Yolov2 and DSSD. See http://github.com/sbrugman/deep-learning-papers http://github.com/sbrugman/deep-learning-papers In practice Faster R-CNN worked better for me than YOLOv2 as it, in contrast to what is reported in the paper, had a higher recall for the detect task I used it for.
- pveierland 9y ago
  "Speed/accuracy trade-offs for modern convolutional object detectors" seems to establish that Faster R-CNN beats R-FCN and SSD-type architectures in accuracy, however YOLOv2 can beat Faster R-CNN and R-FCN in speed, while retaining high accuracy.