The built-in algorithms provided by ModelArts can be used for image classification, object detection, and image semantic segmentation. The requirements for the datasets vary according to the built-in algorithms used for different purposes. Before using a built-in algorithm to create a training job, you are advised to prepare a dataset based on the requirements of the algorithm.

Image Classification

The training dataset must be stored in the OBS bucket. The following shows the OBS path structure of the dataset:

|-- data_url
      |--a.jpg
      |--a.txt
      |--b.jpg
      |--b.txt
      ...

data_url indicates the folder name. You can customize the folder name. Images and label files cannot be stored in the root directory of an OBS bucket.
Images and label files must have the same name. The .txt files are label files for image classification. The images can be in JPG, JPEG, PNG, or BMP format.
The first row of label files for image classification indicates the category name of images, which can be Chinese characters, English letters, or digits. The following provides an example of file content:
```
cat
```
In addition to the preceding files and folders, no other files or folders can exist in the data_url folder.
You can directly use an existing image classification dataset with published versions in Data Management of ModelArts.

You can also name sub-folders in the data_url directory by label, as shown in the following:

|-- data_url
    |--cat
      |--a.jpg
      |--a.txt
    |--dog
      |--b.jpg
      |--b.txt
      ...

Object Detection and Locating

The training dataset must be stored in the OBS bucket. The following shows the OBS path structure of the dataset:

|-- data_url
      |--a.jpg
      |--a.xml
      |--b.jpg
      |--b.xml
      ...

data_url indicates the folder name. You can customize the folder name. Images and label files cannot be stored in the root directory of an OBS bucket.
Images and label files must have the same name. The .xml files are label files for object detection. The images can be in JPG, JPEG, PNG, or BMP format.
In addition to the preceding files and folders, no other files or folders can exist in the data_url folder.
You can directly use an existing object detection dataset with published versions in Data Management of ModelArts.

The following provides a label file for object detection. The key parameters are size (image size), object (object information), and name (label name, which can be Chinese characters, English letters, or digits). Note that the values of xmin, ymin, xmax, and ymax in the bndbox field cannot exceed the value of size. That is, the value of min cannot be less than 0, and the value of max cannot be greater than the value of width or height.

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<annotation>
    <folder>Images</folder>
    <filename>IMG_20180919_120022.jpg</filename>
    <source>
        <database>Unknown</database>
    </source>
    <size>
        <width>800</width>
        <height>600</height>
        <depth>1</depth>
    </size>
    <segmented>0</segmented>
    <object>
        <name>yunbao</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>216.00</xmin>
            <ymin>108.00</ymin>
            <xmax>705.00</xmax>
            <ymax>488.00</ymax>
        </bndbox>
    </object>
</annotation>

Image Semantic Segmentation

The training dataset must be stored in the OBS bucket. The following shows the OBS path structure of the dataset:

|-- data_url
       |--Image
              |--a.jpg
              |--b.jpg
              ...
       |--Label
              |--a.jpg
              |--b.jpg
              ...
       |--train.txt
       |--val.txt

Description:

data_url, Image, and Label indicate the OBS folder names. The Image folder stores images for semantic segmentation, and the Label folder stores labeled images.
The name and format of the images for semantic segmentation must be the same as those of the corresponding labeled images. Images in JPG, JPEG, PNG, and BMP formats are supported.
In the preceding code snippet, train.txt and val.txt are two list files. train.txt is the list file of the training set, and val.txt is the list file of the validation set. It is recommended that the ratio of the training set to the validation set be 8:2.
In the list file, the relative paths of images and labels are separated by spaces. Different pieces of data are separated by newline characters. The following gives an example:
```
Image/a.jpg Label/a.jpg
Image/b.jpg Label/b.jpg
...
```

Requirements on Datasets

Image Classification

Object Detection and Locating

Image Semantic Segmentation