Datasets:
Tasks:
Object Detection
Modalities:
Image
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
File size: 4,385 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': Airplane
'1': Airport
'2': Baseball field
'3': Basketball court
'4': Bridge
'5': Chimney
'6': Dam
'7': Expressway service area
'8': Expressway toll station
'9': Golf course
'10': Ground track field
'11': Harbor
'12': Overpass
'13': Ship
'14': Stadium
'15': Storage tank
'16': Tennis court
'17': Train station
'18': Vehicle
'19': Wind mill
- name: bbox
sequence: int64
length: 4
- name: area
dtype: int64
splits:
- name: train
num_bytes: 5902685454
num_examples: 18000
- name: test
num_bytes: 1150035824
num_examples: 3463
- name: validation
num_bytes: 645393741
num_examples: 2000
download_size: 7626168863
dataset_size: 7698115019
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
task_categories:
- object-detection
language:
- en
pretty_name: DIOR
---
# DIOR Hugging Face-Ready Vision Dataset
This dataset is a restructured version of the DIOR (Object Detection in Optical Remote Sensing Images), specifically designed to simplify object detection workflows. By converting them to the COCO format, this project provides an easier way to use DIOR 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
dior/
├── 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/dior")
```
## 🖼️ 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/dior")
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.
## 📚 Used in Research
This processed version of DIOR has been used in the paper:\
📄 [LoRA for Cross-Domain Few-Shot Object Detection](https://huggingface.co/papers/2504.06330)\
The dataset served as a target domain for evaluating the generalization capabilities of diffusion-based object detectors in low-data regimes.
## 📝 How to Cite
If you use this dataset, please consider citing the original DIOR dataset:
```plaintext
@article{Li_2020,
title={Object detection in optical remote sensing images: A survey and a new benchmark},
volume={159},
ISSN={0924-2716},
url={http://dx.doi.org/10.1016/j.isprsjprs.2019.11.023},
DOI={10.1016/j.isprsjprs.2019.11.023},
journal={ISPRS Journal of Photogrammetry and Remote Sensing},
publisher={Elsevier BV},
author={Li, Ke and Wan, Gang and Cheng, Gong and Meng, Liqiu and Han, Junwei},
year={2020},
month=jan, pages={296–307}}
```
Additionally, you can mention this repository for the resized COCO and Hugging Face formats.
Enjoy using DIOR in coco format for your object detection experiments! 🚀 |