---
dataset_info:
  features:
  - name: image_id
    dtype: int64
  - name: image
    dtype: image
  - name: width
    dtype: int64
  - name: height
    dtype: int64
  - name: objects
    sequence:
    - name: bbox_id
      dtype: int64
    - name: category
      dtype:
        class_label:
          names:
            '0': plane
            '1': ship
            '2': storage-tank
            '3': baseball-diamond
            '4': tennis-court
            '5': basketball-court
            '6': ground-track-field
            '7': harbor
            '8': bridge
            '9': small-vehicle
            '10': large-vehicle
            '11': roundabout
            '12': swimming-pool
            '13': helicopter
            '14': soccer-ball-field
            '15': container-crane
    - name: bbox
      sequence: int64
      length: 4
    - name: area
      dtype: int64
  splits:
  - name: train
    num_bytes: 5043231102.186
    num_examples: 54087
  - name: validation
    num_bytes: 184865300
    num_examples: 2000
  - name: test
    num_bytes: 628863995.564
    num_examples: 6854
  download_size: 5791640499
  dataset_size: 5856960397.75
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
license: mit
task_categories:
- object-detection
language:
- en
pretty_name: DOTA COCO format
---

# DOTA: Resized and Hugging Face-Ready Vision Dataset

This dataset is a restructured version of the DOTA (Dataset for Object Detection in Aerial Images), specifically designed to simplify object detection workflows. By resizing the original images and converting them to the COCO format, this project provides an easier way to use DOTA with popular computer vision frameworks. Additionally, the dataset is formatted for seamless integration with Hugging Face datasets, unlocking new possibilities for training and experimentation.

## 📂 Dataset Structure
### COCO Format
The dataset follows the COCO dataset structure, making it straightforward to work with:

```plaintext
dota/  
├── annotations/  
│   ├── instances_train.json  
│   ├── instances_val.json  
│   └── instances_test.json  
├── train/
├── val/
├── test/
```
### Hugging Face Format
The dataset is compatible with the datasets library. You can load it directly using:

```python
from datasets import load_dataset  

dataset = load_dataset("HichTala/dota")
```

## 🖼️ Sample Visualizations

Above: An example of resized images with bounding boxes in COCO format.

## 🚀 Getting Started
### Install Required Libraries

- Install datasets for Hugging Face compatibility:

```bash
pip install datasets  
```
- Use any object detection framework supporting COCO format for training.

### Load the Dataset
#### Hugging Face:

```python
from datasets import load_dataset  

dataset = load_dataset("HichTala/dota")  
train_data = dataset["train"]  
```

#### Custom Script for COCO-Compatible Frameworks:
```python
import json  
from pycocotools.coco import COCO

coco = COCO("annotations/train.json")
```

see demo notebook [here](https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoDemo.ipynb) for more details.

## ⚙️ Preprocessing Details
- Resizing: The original large images were split into smaller patches, each resized to *512x512 pixels*. 
- Annotations: Converted to COCO format for better compatibility and flexibility.

## 📝 How to Cite
If you use this dataset, please consider citing the original DOTA dataset:

```plaintext
Copy code
@inproceedings{Xia_2018_CVPR,  
    author = {Gui-Song Xia and Xiang Bai and Jieqing Zheng and others},  
    title = {DOTA: A Large-Scale Dataset for Object Detection in Aerial Images},  
    booktitle = {CVPR},  
    year = {2018}  
}  
```

Additionally, you can mention this repository for the resized COCO and Hugging Face formats.


Enjoy using DOTA in coco format for your object detection experiments! 🚀