# Introduction to Built-in Algorithms

Based on the frequently-used AI engines in the industry, ModelArts provides built-in algorithms to meet a wide range of your requirements. You can directly select the algorithms for training jobs, without concerning model development.

Built-in algorithms of ModelArts adopt MXNet and TensorFlow engines and are mainly used for detection of object classes and locations, image classification, and semantic image segmentation.

#### Viewing Built-in Algorithms

In the left navigation pane of the ModelArts management console, choose Training Management > Training Jobs. On the displayed page, click Built-in Algorithms. In the built-in algorithm list, click next to an algorithm name to view details about the algorithm.

You can click Create Training Job in the Operation column for an algorithm to quickly create a training job, for which this algorithm serves as the Algorithm Source.

Before using a built-in algorithm to create a training job, prepare and upload training data to OBS. For details about the data storage path and data format requirements, see Requirements on Datasets.

For details about the built-in algorithms and their running parameters, see the following:

- yolo_v3
- retinanet_resnet_v1_50
- inception_v3
- darknet_53
- SegNet_VGG_BN_16
- ResNet_v2_50
- ResNet_v1_50
- Faster_RCNN_ResNet_v2_101
- Faster_RCNN_ResNet_v1_50

Parent topic: Built-in Algorithms