**YOLOv4** is a one-stage object detection model that improves on [YOLOv3](https://paperswithcode.com/method/yolov3) with several bags of tricks and modules introduced in the literature. The components section below details the tricks and modules used.

**LightAutoML** is an AutoML solution targeted for financial services companies. A typical LightAutoML pipeline scheme is presented in the Figure, each pipeline containing:

- Reader: object that receives raw data and task as input, calculates some useful metadata, performs initial data cleaning and decides about data manipulations that should be done before fitting different model types.

- LightAutoML inner datasets that contains metadata and CV iterators that implements validation scheme for the datasets.

- Multiple ML Pipelines that are stacked and/or blended to get a single prediction.

An ML pipeline in LightAutoML is one or multiple ML models that share a single data preprocessing and validation scheme. The preprocessing step may have up to two feature selection steps, a feature engineering step or even just be empty if no preprocessing is needed. The ML pipelines can be computed independently on the same datasets and then blended together using averaging (or weighted averaging). Alternatively, a stacking ensemble scheme can be used to build multi level ensemble architectures.

LightAutoML

LightAutoML: AutoML Solution for a Large Financial Services Ecosystem

YOLOv4

YOLOv4: Optimal Speed and Accuracy of Object Detection

The **Adaptive Locally Connected Neuron (ALCN)** is a topology aware, and locally adaptive -focusing neuron:

$$a =  f\:\Bigg( \sum_{i=1}^{m} w_{i}\phi\left( \tau\left(i\right),\Theta\right) x_{i} + b \Bigg) %f\:\Bigg(\mathbf{X(W \circ \Phi) + b} \Bigg) $$

Source	YOLOv4: Optimal Speed and Accuracy of Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com