---
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](https://huggingface.co/datasets/detection-datasets/coco) and [coco-2017](https://huggingface.co/datasets/rafaelpadilla/coco2017) 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](https://huggingface.co/blog/paligemma#Detection) 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.
```