Prerequisites

Tracebloc is continually expanding supported data types and tasks to enable your use cases. In case your use case is not yet supported, please reach out to us at support@tracebloc.io.

Before you can create a use case on the tracebloc website, make sure the following requirements are met:

• You are registered as a user on the tracebloc platform
• You have a client deployed using kubernetes either locally or in the cloud
• Your dataset is cleaned and preprocessed
• You are familiar with the supported data types and tasks

Once these requirements are met, proceed with:

• Defining the use case
• Preparing and ingesting the dataset
• Setting evaluation metrics
• Evaluating models

Supported Data Types and Tasks

Image Data

Requirements for all image data tasks: Uniform image sizes and uniform file types. For example all images as 256x256 rgb .jpg files. Convert files if necessary and in case your images do not fit the supported size, crop or resize accordingly.

All images are validated before ingestion by the data ingestor. The ingestion process only starts when every file meets the requirements. Fix or remove any invalid images, then retry.

For object detection, images and annotations are automatically up- or downsized to 448x448 pixels.

Task	Input file type	Color mode	Supported image size	Label file type	Requirements	Links
Classification	PNG, JPG, JPEG	rgb (3 channels) or grayscale (1 channel), 8-bit per channel	height = width px	CSV	Uniform image size and file type per dataset	Detailed structure Example
Keypoint Detection	PNG, JPG, JPEG	rgb (3 channels) or grayscale (1 channel), 8-bit per channel	height = width px	CSV	Uniform image sizes Same number of keypoints per image and class	Detailed structure Example
Object Detection	PNG, JPG, JPEG	rgb (3 channels) or grayscale (1 channel), 8-bit per channel	height = width px. Images and annotations will be resized to 448x448 px automatically	Pascal VOC	Uniform image sizes, one xml per image	Detailed structure Example
Semantic Segmentation	PNG, JPG, JPEG	rgb (3 channels) or grayscale (1 channel), 8-bit per channel	height = width px	PNG, JPG, JPEG	Uniform image and mask sizes	Detailed structure Example

Image Classification

train/
    labels.csv
    images/
        cat01.jpg
        dog02.jpg
        ...
test/
    ...

filename,label
cat01.jpg,cat
dog02.jpg,dog
...

Image Keypoint Detection

The number of keypoints per class and per image must be fixed. For example, in a person/car keypoint detection project, both classes must define the same keypoints, and every image must contain the full set for its class. You cannot mix classes with different keypoint counts (e.g., 16 for person and 32 for car) or annotate some images with fewer keypoints for the same class.

train/
    annotations.csv
    images/
        image01.png
        image02.png
        ...
test/
    ...

filename,label,x,y,visibility
image01.jpg,person,100,150,2
image01.jpg,car,120,140,1
image02.jpg,person,95,155,0
image02.jpg,car,115,145,2

• X and Y determine the X-/Y-coordinates of a keypoint
• Visibility indicates whether a keypoint is visible in the image or not: 0 = not visible (point outside the image or point is in the image but occluded), 1 = visible

Image Object Detection

The filename like "street01.png" specifies the link between images and annotations. XML-file annotations are in Pascal VOC format. The labels.csv contains a global list of all images and objects. Currently, the images and annotations are resized to 448x448 pixels.

train/
    labels.csv
    images/
        street01.png
        street02.png
        ...
    annotations/
        street01.xml
        street02.xml
        ...
test/
    ...

<annotation>
    <filename>street01.jpg</filename>
    <object>
        <name>car</name>
        <bndbox>
            <xmin>100</xmin>
            <ymin>200</ymin>
            <xmax>300</xmax>
            <ymax>400</ymax>
        </bndbox>
    </object>
    <object>
        ...
    </object>
</annotation>

image filename,label
street01,car
street01,car
street01,person
street02,car
...

Image Semantic Segmentation

Each mask is an rgb image whose pixel values map to classes defined in labels.csv. The labels.csv contains a global list per image and class. All masks must exactly match their corresponding image sizes and file names. For binary segmentation (two classes), provide a single-channel grayscale mask where background pixels are black (0) and foreground pixels are white (255). For three or more classes, supply an RGB mask where each class is represented by a unique color (or pixel value).

train/
    labels.csv
    images/
        scene01.png
        scene02.png
        ...
    masks/
        scene01.png
        scene02.png
        ...
test/
    ...

image filename,mask filename,label,colour
image1.jpg,mask1.png,road,#FFFFFF
image1.jpg,mask1.png,background,#000000
image2.jpg,mask2.png,background,#000000
image2.jpg,mask2.png,road,#FFFFFF
...

Tabular Data

Requirements for all tabular data tasks: Each dataset must be provided as a single CSV file with a header row. Every column must contain uniform data types, for example numeric values for features and a categorical or alphanumeric column for labels. Use UTF-8 encoding with comma separators and validate that your schema matches the expected types. Invalid rows are skipped by the ingestor.

Task	Data file type	Requirements	Links
Classification	CSV (features and label in one single file)	Uniform data formats per column. Feature columns: Numeric Label columns: Alphanumeric	Detailed structure Example

Tabular Classification

Include a header row with clear column names, using a dedicated column for the labels. An id row is recommended but not required.

id,feature1,feature2,feature3,label
1,1.5,2.3,0.8,class_a
2,2.1,1.9,1.2,class_b
3,0.9,3.1,0.5,class_a
...

Text Data

Task	Input files	Label file type	Requirements	Links
Classification	TXT	CSV	Text file may not be empty	Detailed structure Example

Text Classification

train/
    labels.csv
    texts/
        review01.txt
        review02.txt
        ...
test/
    ...

text_id,label
review01.txt,positive
review02.txt,negative
...

# review01.txt
This product is amazing! I love it.