TYOLO v3 was introduced in 2018 by Joseph Redmon and Ali Farhadi in the paper YOLOv3: An Incremental Improvement https://pjreddie.com/media/files/papers/YOLOv3.pdf. The YOLO v3 neural network architecture is shown in the following diagram. The network has 24 convolutional layers with 2 fully connected layers; it does not have any softmax layers.
The following diagram illustrates the YOLO v3 architecture graphically:
The most important feature of YOLO v3 is its detection mechanism and this is done at three different scales—at layers 82, 94, and 106:
- The network consists of 23 convolution and residual ...
