Dataset Types¶

note

Refer to ./examples/datasets/ for examples on pre-processing common dataset formats to conform to the SINGA-Auto’s own dataset formats.

CORPUS¶

The dataset file must be of the .zip archive format with a corpus.tsv at the root of the directory.

The corpus.tsv should be of a .TSV format with columns of token and N other variable column names (tag columns).

For each row,

token should be a string, a token (e.g. word) in the corpus. These tokens should appear in the order as it is in the text of the corpus. To delimit sentences, token can be take the value of \n.

The other N columns describe the corresponding token as part of the text of the corpus, depending on the task.

SEGMENTATION_IMAGES¶

Inside the uploaded .zip file, the training and validation sets should be wrapped separately, and be named strictly as train and val.
For train folder (the same for val folder), the images and annotated masks should also be wrapped separately, and be named strictly as image and mask.
mask folder should contain only .png files and file name should be the same as each mask’s corresponding image. (eg. for an image named 0001.jpg, its corresponding mask should be named as 0001.png)
An JSON file named params.json must also be included in the .zip file, in order to indicates the essential training parameters such as num_classes, for example:
```
{
    "num_classes": 21
}
```

An example of the upload .zip file structure:

+ dataset.zip
    + train
        + image
            + 0001.jpg
            + 0002.jpg
            + ...
        + mask
            + 0001.png
            + 0002.png
            + ..
    + val
        + image
            + 0003.jpg
            + ...
        + mask
            + 0003.png
            + ...
    + params.json

IMAGE_FILES¶

The dataset file must be of the .zip archive format with a images.csv at the root of the directory.

The images.csv should be of a .CSV format with columns of path and N other variable column names (tag columns).

For each row,

path should be a file path to a .png, .jpg or .jpeg image file within the archive, relative to the root of the directory.

The other N columns describe the corresponding image, depending on the task.

DETECTION_DATASET¶

It is recommended to follow the YOLO dataset format.

For folder hierarchy, two folders ‘images’ and ‘labels’ should be prepared. In ‘images’ folder, there are PIL loadable images, and the corresponding txt label files should be placed in ‘labels’ folder, with the same basename with the images.
The label file format is as follows, where object-id is the index of object, the following four numbers should be normalized to range between 0 and 1 by dividing by the width and height of the image. center_x center_y are the central coordinates of bounding box, and width heigh is the sides lengths of it. It is allowable to use empty label file (negative samples), which means there are no objects to detect in the image.

object-id center_x center_y width height
...

In addition, train.txt, valid.txt can be provided to note images used for training/validataion, only including the path of image files. A class.names contains the category names and thier line numbers are object-id.

GENERAL_FILES¶

For general task, as its name states, any domain’s task (or model) can be included within this category, such as image processing, nlp, speech, or video.
There is no requirements for the form of dataset, as long as it can be read into memory in the form of a file. However, the model developer has to know in advance how to handle the read-in file.

QUESTION_ANSWERING_COVID19¶

The dataset file must be of the .zip archive format, containing JSON files. JSON files under different levels of folders will be automaticly read all together.

Each JSON file is extracted from one paper. JSON structure contains field body_text, which is a list of {“text”: <str>} blocks. Each text block is namely each paragraph of corresponding paper.

Meanwhile, a metadata.csv file, at the root of the archive directory, is optional. It is to provide the model with publish_time column, each entry is in Date format, e.g. 2001-12-17. In this condition, each metadata entry is required to have sha value column in General format, and each JSON file required to have “sha”:<str> field, while both sha values linked. When neither metadata.csv or publish_time Date value is provided, the model would not check the timeliness of corresponding JSON body_text field.

QUESTION_ANSWERING_MEDQUAD¶

The dataset file must be of the .zip archive format, containing xml files. Xml files under different levels of folders will be automaticly read all together.

Model would only take <Document> <QAPairs> … </QAPairs> </Document>field, and this filed contains multiple <QAPair> … </QAPair>. Each QAPair has one <Question> … </Question> and its <Answer> … </Answer> combination.

TABULAR¶

The dataset file must be a tabular dataset of the .csv format with N columns.

AUDIO_FILES¶

The dataset file must be of the .zip archive format with a audios.csv at the root of the directory.

The audios.csv should be of a .CSV format with 3 columns of wav_filename, wav_filesize and transcript.

For each row,

wav_filename should be a file path to a .wav audio file within the archive, relative to the root of the directory. Each audio file’s sample rate must equal to 16kHz.

wav_filesize should be an integer representing the size of the .wav audio file, in number of bytes.

transcript should be a string of the true transcript for the audio file. Transcripts should only contain the following alphabets:
a
b
c
d
e
f
g
h
i
j
k
l
m
n
o
p
q
r
s
t
u
v
w
x
y
z


'
An example of audios.csv follows:

wav_filename,wav_filesize,transcript
6930-81414-0000.wav,412684,audio transcript one
6930-81414-0001.wav,559564,audio transcript two
...
672-122797-0005.wav,104364,audio transcript one thousand
...
1995-1837-0001.wav,279404,audio transcript three thousand

Query Format¶

A Base64-encoded string of the bytes of the audio as a 16kHz .wav file

Prediction Format¶

A string, representing the predicted transcript for the audio.