Datasets:
Tasks:
Object Detection
Modalities:
Image
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
Search is not available for this dataset
image_id
int64 2k
56.1k
| image
imagewidth (px) 512
512
| width
int64 512
512
| height
int64 512
512
| objects
sequence |
|---|---|---|---|---|
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End of preview. Expand
in Dataset Viewer.
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:
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:
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:
pip install datasets
- Use any object detection framework supporting COCO format for training.
Load the Dataset
Hugging Face:
from datasets import load_dataset
dataset = load_dataset("HichTala/dota")
train_data = dataset["train"]
Custom Script for COCO-Compatible Frameworks:
import json
from pycocotools.coco import COCO
coco = COCO("annotations/train.json")
see demo notebook here 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:
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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