# Yolo

## Description

The Yolo module is used to initiate a yolo  (You Only Look Once) object detection model.

![](https://1833277725-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-Lyc1OXsKqB2S62LxsOR%2F-M1ZCibIM9lHKCmsn0xG%2F-M1ZN1_LYap_xtk8vGt_%2FYOLO.png?alt=media\&token=db461dc7-246f-4e2a-8d4f-6f8b281351b8)

## Parameters

The Yolo module is a compact toolset for capturing  visual features.

Among others, we can :    

* select the optimizer : **adam,rmsprop, adagrad, adamax ,sgd**
* set : **beta, gradient, epsilon, learning rate, momentum, rho**

                                                   

#### Principal parameters

The Yolo module includes many functionalities we can select either by ticking the check boxes, by selecting them in dropdowns or by setting them manually.

We may for instance check the : **data augmentation , trainable skeleton, save checkpoint to local , early stopping**. All of them are boolean. 

Speaking about **anchor boxes**, default values are preset. However, in need, we might always add our own values to obtain proper results.  

![The Yolo module is a comprehensive feature capture toolset .](https://1833277725-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-Lyc1OXsKqB2S62LxsOR%2F-M1ZCibIM9lHKCmsn0xG%2F-M1ZT1YrOEDX-mnbjz9I%2Fimage.png?alt=media\&token=ae7a6021-d4f1-4e5f-884b-7df01b40bc41)

#### Other parameters

As a features extractor , we have the choice between: 

* **darknet,** 
* **mobilenet ,**
*  **resnet50/101/152\_v2,**
*  **nasnet,**
*  and **xception.**

![The features extractor weight  ](https://1833277725-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-Lyc1OXsKqB2S62LxsOR%2F-M1ZCibIM9lHKCmsn0xG%2F-M1ZTYZCHjQ-8OpSgqZS%2Fimage.png?alt=media\&token=1f37a337-353e-4a03-9c27-4449ca098910)

Finally, **labels,** **non max suppression threshold, number of classes, true boxes,object scale , object threshold** and **warm Up Batches** are all variable parameters to help us fine-tune the Yolo' s  accuracy**.**

{% hint style="info" %}
 To get a better insight on Yolo, check this [blog article](https://hackernoon.com/understanding-yolo-f5a74bbc7967) which explains it in a few lines.  
{% endhint %}