Datasets:
metadata
dataset_info:
features:
- name: image
dtype: image
- name: prompt
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 23257283614.36
num_examples: 153128
download_size: 23241036646
dataset_size: 23257283614.36
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- object-detection
language:
- tr
This dataset is combined and deduplicated version of coco-2014 and coco-2017 datasets for object detection. The labels are in Turkish and the dataset is in an instruction-tuning format with separate columns for prompts and completion labels.
For the bounding boxes, a similar annotation scheme to that of PaliGemma annotation is used. That is,
The bounding box coordinates are in the form of special <loc[value]> tokens, where value is a number that represents a normalized coordinate. Each detection is represented by four location coordinates in the order x_min(left), y_min(top), x_max(right), y_max(bottom), followed by the label that was detected in that box. To convert values to coordinates, you first need to divide the numbers by 1024, then multiply y by the image height and x by its width. This will give you the coordinates of the bounding boxes, relative to the original image size.