YOLOX: Anchor Free YOLO
Most YOLO family, e.g. YOLOv3, YOLOv4, YOLOv5 and YOLOv7, are anchor based detectors. The performance are optimised for anchor based framework. However the predefined anchor size, as a strong prior, may limits the detection performance.
YOLOX is based on YOLOv3-SPP with DarkNet53 backbone and an SPP layer. Apart from make YOLO anchor free, it also applied decoupled head, mosaic/mix-up augmentation, multiple positive and simOTA to boost the performance.
Decoupled head is proposed to resolve the conflicts between regression and classification tasks. YOLOX proposed a lite decoupled head which increase the inference time for only 1ms on Tesla V100. Decoupled head helps model to coverage faster during training.
Strong augmentations: mosaic and mix-up are adopted. This strong augmentation eliminates the need of pre-trained weights with ImageNet. So all YOLOX models are trained from scratch.
Anchor free: Change the head to be anchor free make the model a lot simpler. The output for each pixel is four values, i.e. offset to top left corner and object width/height.
Multiple Positives: Use 3x3 area to allow multiple positive matches during…
