Datasets:
Tasks:
Object Detection
Modalities:
Image
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
File size: 4,285 Bytes
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---
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: 184865300.0
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-*
---
# DOTA: Resized and Hugging Face-Ready Vision Dataset
DOTA 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.
## 🌟 Key Features
Resized Images: Reduced image dimensions for faster training and inference while maintaining key details.
COCO Format: Compatible with major deep learning libraries like PyTorch, TensorFlow, and MMDetection.
Hugging Face Integration: Ready-to-use with the Hugging Face datasets library for efficient data loading and preprocessing.
## 📂 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! 🚀
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