Viewer tab is used to view various datasets. It reads the images and the annotation files to label them with their respective class names and displays the same.

Currently, it supports various datasets, like COCO, Imagenet, Pascal VOC, Princeton, Spinello, etc. It also supports displaying and labelling depth images by converting them into a human readable depth map.

Below is an example of using Viewer to view COCO Dataset. To begin with, one would require COCO Dataset and the same can be downloaded from this link.

Download both the annotations and Train Val images, and put them in the same folder and then extract.

Command line use example

An example of config file would be:

    inputPath: /opt/datasets/weights/annotations/instances_val2017.json
    readerImplementation: COCO
    readerNames: /opt/datasets/names/coco.names

With the config file, change the directory to Tools/Evaluator inside build and run

    ./viewer -c appConfig.yml

This will generate a window with the a image a the detections. Pressing the space bar, the dataset is navigated.

GUI use video example

Change your appConfig.txt to include this folder’s path (containing both annotations and images) or it’s parent’s path in dataset path.

Now, you can run Detection Metrics, switch to viewer tab, select the annotation file for COCO, which will be instances_trainxxxx.json. Select reader Implementation as COCO and class name as coco.names (can be downloaded from here).

And click View!

Example video demonstrating it: