**PP-YOLO** is an object detector based on [YOLOv3](https://paperswithcode.com/method/yolov3). It mainly tries to combine various existing tricks that almost not increase the number of model parameters and FLOPs, to achieve the goal of improving the accuracy of detector as much as possible while ensuring that the speed is almost unchanged. Some of these changes include:

- Changing the [DarkNet-53](https://paperswithcode.com/method/darknet-53) backbone with ResNet50-vd. Some of the convolutional layers in ResNet50-vd are also replaced with [deformable convolutional layers](https://paperswithcode.com/method/deformable-convolution).
- A larger batch size is used - changing from 64 to 192.
- An exponentially moving average is used for the parameters.
- [DropBlock](https://paperswithcode.com/method/dropblock) is applied to the [FPN](https://paperswithcode.com/method/fpn).
- An IoU loss is used.
- An IoU prediction branch is added to measure the accuracy of localization.
- [Grid Sensitive](https://paperswithcode.com/method/grid-sensitive) is used, similar to [YOLOv4](https://paperswithcode.com/method/yolov4).
- [Matrix NMS](https://paperswithcode.com/method/matrix-nms) is used.
- [CoordConv](https://paperswithcode.com/method/coordconv) is used for the [FPN](https://paperswithcode.com/method/fpn), replacing the 1x1 convolution layer, and also the first convolution layer in the detection head.
- [Spatial Pyramid Pooling](https://paperswithcode.com/method/spatial-pyramid-pooling) is used for the top feature map.

Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.

We compute the layer normalization statistics over all the hidden units in the same layer as follows:

$$ \mu^{l} = \frac{1}{H}\sum^{H}\_{i=1}a\_{i}^{l} $$

$$ \sigma^{l} = \sqrt{\frac{1}{H}\sum^{H}\_{i=1}\left(a\_{i}^{l}-\mu^{l}\right)^{2}}  $$

where $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\mu$ and $\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.

Layer Normalization

PP-YOLO

PP-YOLO: An Effective and Efficient Implementation of Object Detector

**DCN-V2** is an architecture for learning-to-rank that improves upon the original [DCN](http://paperswithcode.com/method/dcn) model. It first learns explicit feature interactions of the inputs (typically the embedding layer) through cross layers, and then combines with a deep network to learn complementary implicit interactions. The core of DCN-V2 is the cross layers, which inherit the simple structure of the cross network from DCN, however it is significantly more expressive at learning explicit and bounded-degree cross features.

Source	PP-YOLO: An Effective and Efficient Implementation of Object Detector
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com